url
stringlengths 58
61
| repository_url
stringclasses 1
value | labels_url
stringlengths 72
75
| comments_url
stringlengths 67
70
| events_url
stringlengths 65
68
| html_url
stringlengths 48
51
| id
int64 600M
2.19B
| node_id
stringlengths 18
24
| number
int64 2
6.73k
| title
stringlengths 1
290
| user
dict | labels
listlengths 0
4
| state
stringclasses 2
values | locked
bool 1
class | assignee
dict | assignees
listlengths 0
4
| milestone
dict | comments
listlengths 0
30
| created_at
timestamp[s] | updated_at
timestamp[s] | closed_at
timestamp[s] | author_association
stringclasses 3
values | active_lock_reason
null | draft
null | pull_request
null | body
stringlengths 0
228k
⌀ | reactions
dict | timeline_url
stringlengths 67
70
| performed_via_github_app
null | state_reason
stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://api.github.com/repos/huggingface/datasets/issues/629
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/629/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/629/comments
|
https://api.github.com/repos/huggingface/datasets/issues/629/events
|
https://github.com/huggingface/datasets/issues/629
| 701,517,550 |
MDU6SXNzdWU3MDE1MTc1NTA=
| 629 |
straddling object straddles two block boundaries
|
{
"login": "bharaniabhishek123",
"id": 17970177,
"node_id": "MDQ6VXNlcjE3OTcwMTc3",
"avatar_url": "https://avatars.githubusercontent.com/u/17970177?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/bharaniabhishek123",
"html_url": "https://github.com/bharaniabhishek123",
"followers_url": "https://api.github.com/users/bharaniabhishek123/followers",
"following_url": "https://api.github.com/users/bharaniabhishek123/following{/other_user}",
"gists_url": "https://api.github.com/users/bharaniabhishek123/gists{/gist_id}",
"starred_url": "https://api.github.com/users/bharaniabhishek123/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/bharaniabhishek123/subscriptions",
"organizations_url": "https://api.github.com/users/bharaniabhishek123/orgs",
"repos_url": "https://api.github.com/users/bharaniabhishek123/repos",
"events_url": "https://api.github.com/users/bharaniabhishek123/events{/privacy}",
"received_events_url": "https://api.github.com/users/bharaniabhishek123/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"sorry it's an apache arrow issue."
] | 2020-09-15T00:30:46 | 2020-09-15T00:36:17 | 2020-09-15T00:32:17 |
NONE
| null | null | null |
I am trying to read json data (it's an array with lots of dictionaries) and getting block boundaries issue as below :
I tried calling read_json with readOptions but no luck .
```
table = json.read_json(fn)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pyarrow/_json.pyx", line 246, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/629/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/629/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/625
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/625/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/625/comments
|
https://api.github.com/repos/huggingface/datasets/issues/625/events
|
https://github.com/huggingface/datasets/issues/625
| 701,057,799 |
MDU6SXNzdWU3MDEwNTc3OTk=
| 625 |
dtype of tensors should be preserved
|
{
"login": "BramVanroy",
"id": 2779410,
"node_id": "MDQ6VXNlcjI3Nzk0MTA=",
"avatar_url": "https://avatars.githubusercontent.com/u/2779410?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/BramVanroy",
"html_url": "https://github.com/BramVanroy",
"followers_url": "https://api.github.com/users/BramVanroy/followers",
"following_url": "https://api.github.com/users/BramVanroy/following{/other_user}",
"gists_url": "https://api.github.com/users/BramVanroy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/BramVanroy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/BramVanroy/subscriptions",
"organizations_url": "https://api.github.com/users/BramVanroy/orgs",
"repos_url": "https://api.github.com/users/BramVanroy/repos",
"events_url": "https://api.github.com/users/BramVanroy/events{/privacy}",
"received_events_url": "https://api.github.com/users/BramVanroy/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..\r\n\r\nAnd then for your information, when reading from arrow format we have to cast from arrow to numpy (which is fast since pyarrow has a numpy integration), and then to torch.\r\n\r\nHowever there's one thing that can help you: we make sure that the dtypes correspond to what is defined in `features`.\r\nTherefore what you can do is provide `features` in `.map(preprocess, feature=...)` to specify the output types.\r\n\r\nFor example in your case:\r\n```python\r\nfrom datasets import Features, Value, Sequence\r\n\r\nfeatures = Features({\r\n \"input_ids\": Sequence(Value(\"int32\")),\r\n \"sembedding\": Sequence(Value(\"float32\"))\r\n})\r\npreprocessed_dataset = dataset.map(preprocess, features=features)\r\n\r\npreprocessed_dataset.set_format(\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\nprint(preprocessed_dataset[0][\"sembedding\"].dtype)\r\n# \"torch.float32\"\r\n```\r\n\r\nLet me know if it helps",
"If the arrow format is basically lists, why is the intermediate step to numpy necessary? I am a bit confused about that part.\r\n\r\nThanks for your suggestion. as I have currently implemented this, I cast to torch.Tensor in my collate_fn to save disk space (so I do not have to save padded tensors to max_len but can pad up to max batch len in collate_fn) at the cost of a bit slower processing. So for me this is not relevant anymore, but I am sure it is for others!",
"I'm glad you managed to figure something out :)\r\n\r\nCasting from arrow to numpy can be 100x faster than casting from arrow to list.\r\nThis is because arrow has an integration with numpy that allows it to instantiate numpy arrays with zero-copy from arrow.\r\nOn the other hand to create python lists it is slow since it has to recreate the list object by iterating through each element in python.",
"Ah that is interesting. I have no direct experience with arrow so I didn't know. ",
"I encountered a simliar issue: `datasets` converted my float numpy array to `torch.float64` tensors, while many pytorch operations require `torch.float32` inputs and it's very troublesome. \r\n\r\nI tried @lhoestq 's solution, but since it's mixed with the preprocess function, it's not very intuitive. \r\n\r\nI just want to share another possible simpler solution: directly cast the dtype of the processed dataset.\r\n\r\nNow I want to change the type of `labels` in `train_dataset` from float64 to float32, I can do this.\r\n\r\n```\r\nfrom datasets import Value, Sequence, Features\r\nfeats = train_dataset.features.copy()\r\nfeats['labels'].feature = Value(dtype='float32')\r\nfeats = Features(feats)\r\ntrain_dataset.cast_(feats)\r\n```\r\n",
"Reopening since @bhavitvyamalik started looking into it !\r\n\r\nAlso I'm posting here a function that could be helpful to support preserving the dtype of tensors.\r\n\r\nIt's used to build a pyarrow array out of a numpy array and:\r\n- it doesn't convert the numpy array to a python list\r\n- it keeps the precision of the numpy array for the pyarrow array\r\n- it works with multidimensional arrays (while `pa.array` can only take a 1D array as input)\r\n- it builds the pyarrow ListArray from offsets created on-the-fly and values that come from the flattened numpy array\r\n\r\n```python\r\nfrom functools import reduce\r\nfrom operator import mul\r\n\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\ndef pa_ndarray(a):\r\n \"\"\"Build a PyArrow ListArray from a multidimensional NumPy array\"\"\"\r\n values = pa.array(a.flatten()) \r\n for i in range(a.ndim - 1): \r\n n_offsets = reduce(mul, a.shape[:a.ndim - i - 1], 1) \r\n step_offsets = a.shape[a.ndim - i - 1] \r\n offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32()) \r\n values = pa.ListArray.from_arrays(offsets, values) \r\n return values \r\n\r\nnarr = np.arange(42).reshape(7, 2, 3).astype(np.uint8)\r\nparr = pa_ndarray(narr)\r\nassert isinstance(parr, pa.Array)\r\nassert parr.type == pa.list_(pa.list_(pa.uint8()))\r\nassert narr.tolist() == parr.to_pylist()\r\n```\r\n\r\nThe only costly operation is the offsets computations. Since it doesn't iterate on the numpy array values this function is pretty fast.",
"@lhoestq Have you thought about this further?\r\n\r\nWe have a use case where we're attempting to load data containing numpy arrays using the `datasets` library.\r\n\r\nWhen using one of the \"standard\" methods (`[Value(...)]` or `Sequence()`) we see ~200 samples processed per second during the call to `_prepare_split`. This slowdown is caused by the vast number of calls to `encode_nested_example` (each sequence is converted to a list, and each element in the sequence...). \r\n\r\nUsing the `Feature` `ArrayND` improves this somewhat to ~500/s as it now uses numpy's `tolist()` rather than iterating over each value in the array and converting them individually.\r\n\r\nHowever, it's still pretty slow and in theory it should be possible to avoid the `numpy -> python -> arrow` dance altogether. To demonstrate this, if you keep the `Feature` set to an `ArrayND` but instead return a `pa_ndarray(...)` in `_generate_examples` it skips the conversion (`return obj, False`) and hits ~11_000/s. Two orders of magnitude speed up! The problem is this then fails later on when the `ArrowWriter` tries to write the examples to disk :-( \r\n\r\nIt would be nice to have first-class support for user-defined PyArrow objects. Is this a possibility? We have _large_ datasets where even an order of magnitude difference is important so settling on the middle ~500/s is less than ideal! \r\n\r\nIs there a workaround for this or another method that should be used instead that gets near-to or equal performance to returning PyArrow arrays?",
"Note that manually generating the table using `pyarrow` achieves ~30_000/s",
"Hi !\r\n\r\nIt would be awesome to achieve this speed for numpy arrays !\r\nFor now we have to use `encode_nested_example` to convert numpy arrays to python lists since pyarrow doesn't support multidimensional numpy arrays (only 1D).\r\n\r\nMaybe let's start a new PR from your PR @bhavitvyamalik (idk why we didn't answer your PR at that time, sorry about that).\r\nBasically the idea is to allow `TypedSequence` to support numpy arrays as you did, and remove the numpy->python casting in `_cast_to_python_objects`.\r\n\r\nThis is really important since we are starting to have a focus on other modalities than text as well (audio, images).\r\n\r\nThough until then @samgd, there is another feature that may interest you and that may give you the speed you want:\r\n\r\nIn a dataset script you can subclass either a GeneratorBasedBuilder (with the `_generate_examples ` method) or an ArrowBasedBuilder if you want. the ArrowBasedBuilder allows to yield arrow data by implementing the `_generate_tables` method (it's the same as `_generate_examples` except you must yield arrow tables). Since the data are already in arrow format, it doesn't call `encode_nested_example`. Let me know if that helps."
] | 2020-09-14T12:38:05 | 2021-08-17T08:30:04 | 2021-08-17T08:30:04 |
CONTRIBUTOR
| null | null | null |
After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https://discuss.pytorch.org/t/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32/96221)).
As a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:
```python
def preprocess(sentences: List[str]):
token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]
sembeddings = stransformer.encode(sentences)
print(sembeddings.dtype)
return {"input_ids": token_ids, "sembedding": sembeddings}
```
Given a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column "sembedding" to a tensor that I as a user expect to be a float32.
It appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case.
My model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.
```python
dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
```
This happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation/deduction is different from torch dtypes** (see the snippet below). As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64.
```python
import torch
import numpy as np
l = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]
torch_tensor = torch.tensor(l)
np_array = np.array(l)
np_to_torch = torch.from_numpy(np_array)
print(torch_tensor.dtype)
# torch.float32
print(np_array.dtype)
# float64
print(np_to_torch.dtype)
# torch.float64
```
This might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.
The alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/625/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/625/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/624
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/624/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/624/comments
|
https://api.github.com/repos/huggingface/datasets/issues/624/events
|
https://github.com/huggingface/datasets/issues/624
| 700,541,628 |
MDU6SXNzdWU3MDA1NDE2Mjg=
| 624 |
Add learningq dataset
|
{
"login": "krrishdholakia",
"id": 17561003,
"node_id": "MDQ6VXNlcjE3NTYxMDAz",
"avatar_url": "https://avatars.githubusercontent.com/u/17561003?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/krrishdholakia",
"html_url": "https://github.com/krrishdholakia",
"followers_url": "https://api.github.com/users/krrishdholakia/followers",
"following_url": "https://api.github.com/users/krrishdholakia/following{/other_user}",
"gists_url": "https://api.github.com/users/krrishdholakia/gists{/gist_id}",
"starred_url": "https://api.github.com/users/krrishdholakia/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/krrishdholakia/subscriptions",
"organizations_url": "https://api.github.com/users/krrishdholakia/orgs",
"repos_url": "https://api.github.com/users/krrishdholakia/repos",
"events_url": "https://api.github.com/users/krrishdholakia/events{/privacy}",
"received_events_url": "https://api.github.com/users/krrishdholakia/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067376369,
"node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request",
"name": "dataset request",
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset"
}
] |
open
| false | null |
[] | null |
[] | 2020-09-13T10:20:27 | 2020-09-14T09:50:02 | null |
NONE
| null | null | null |
Hi,
Thank you again for this amazing repo.
Would it be possible for y'all to add the LearningQ dataset - https://github.com/AngusGLChen/LearningQ ?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/624/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/624/timeline
| null | null |
https://api.github.com/repos/huggingface/datasets/issues/623
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/623/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/623/comments
|
https://api.github.com/repos/huggingface/datasets/issues/623/events
|
https://github.com/huggingface/datasets/issues/623
| 700,235,308 |
MDU6SXNzdWU3MDAyMzUzMDg=
| 623 |
Custom feature types in `load_dataset` from CSV
|
{
"login": "lvwerra",
"id": 8264887,
"node_id": "MDQ6VXNlcjgyNjQ4ODc=",
"avatar_url": "https://avatars.githubusercontent.com/u/8264887?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lvwerra",
"html_url": "https://github.com/lvwerra",
"followers_url": "https://api.github.com/users/lvwerra/followers",
"following_url": "https://api.github.com/users/lvwerra/following{/other_user}",
"gists_url": "https://api.github.com/users/lvwerra/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lvwerra/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lvwerra/subscriptions",
"organizations_url": "https://api.github.com/users/lvwerra/orgs",
"repos_url": "https://api.github.com/users/lvwerra/repos",
"events_url": "https://api.github.com/users/lvwerra/events{/privacy}",
"received_events_url": "https://api.github.com/users/lvwerra/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] |
closed
| false | null |
[] | null |
[
"Currently `csv` doesn't support the `features` attribute (unlike `json`).\r\nWhat you can do for now is cast the features using the in-place transform `cast_`\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'])\r\ndataset.cast_(emotion_features)\r\n```\r\n",
"Thanks for the clarification!",
"Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<ipython-input-163-81ffd5ac18c9> in <module>\r\n----> 1 dataset.cast_(emotion_features)\r\n\r\n/usr/local/lib/python3.6/dist-packages/datasets/dataset_dict.py in cast_(self, features)\r\n 125 self._check_values_type()\r\n 126 for dataset in self.values():\r\n--> 127 dataset.cast_(features=features)\r\n 128 \r\n 129 def remove_columns_(self, column_names: Union[str, List[str]]):\r\n\r\n/usr/local/lib/python3.6/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)\r\n 161 # Call actual function\r\n 162 \r\n--> 163 out = func(self, *args, **kwargs)\r\n 164 \r\n 165 # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in cast_(self, features)\r\n 602 self._info.features = features\r\n 603 schema = pa.schema(features.type)\r\n--> 604 self._data = self._data.cast(schema)\r\n 605 \r\n 606 @fingerprint(inplace=True)\r\n\r\n/usr/local/lib/python3.6/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()\r\n\r\nValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n```\r\n\r\nLooking at the types in `emotion_features` we see that `label` and `text` appear to be swapped in the Arrow table:\r\n\r\n```\r\nemotion_features.type\r\nStructType(struct<label: int64, text: string>)\r\n```\r\n\r\nDid we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https://huggingface.co/docs/datasets/features.html?highlight=features#dataset-features), but perhaps we misunderstood something 😬 \r\n\r\n",
"In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n\r\nShould I add it?",
"> In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n> \r\n> Should I add it?\r\n\r\nSure let's add it. Setting the convert options should do the job\r\n\r\n> Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n> \r\n> ```\r\n> ---------------------------------------------------------------------------\r\n> ValueError Traceback (most recent call last)\r\n> <ipython-input-163-81ffd5ac18c9> in <module>\r\n> ----> 1 dataset.cast_(emotion_features)\r\n>\r\n> /usr/local/lib/python3.6/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()\r\n> \r\n> ValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n> ```\r\n>\r\n> Did we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https://huggingface.co/docs/datasets/features.html?highlight=features#dataset-features), but perhaps we misunderstood something 😬\r\n\r\nThanks for reporting, that's a bug :) I'm fixing it right now",
"PR is open for the `ValueError: Target schema's field names are not matching the table's field names` error.\r\n\r\nI'm adding the features parameter to csv",
"Thanks a lot for the PR and quick fix @lhoestq!"
] | 2020-09-12T13:21:34 | 2020-09-30T19:51:43 | 2020-09-30T08:39:54 |
MEMBER
| null | null | null |
I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`.
I am working with the local files from the emotion dataset. To get the data you can use the following code:
```Python
from pathlib import Path
import wget
EMOTION_PATH = Path("./data/emotion")
DOWNLOAD_URLS = [
"https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1",
"https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1",
"https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1",
]
if not Path.is_dir(EMOTION_PATH):
Path.mkdir(EMOTION_PATH)
for url in DOWNLOAD_URLS:
wget.download(url, str(EMOTION_PATH))
```
The first five lines of the train set are:
```
i didnt feel humiliated;sadness
i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness
im grabbing a minute to post i feel greedy wrong;anger
i am ever feeling nostalgic about the fireplace i will know that it is still on the property;love
i am feeling grouchy;anger
```
Here the code to reproduce the issue:
```Python
from datasets import Features, Value, ClassLabel, load_dataset
class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
emotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})
file_dict = {'train': EMOTION_PATH/'train.txt'}
dataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)
```
**Observed behaviour:**
```Python
dataset['train'].features
```
```Python
{'text': Value(dtype='string', id=None),
'label': Value(dtype='string', id=None)}
```
**Expected behaviour:**
```Python
dataset['train'].features
```
```Python
{'text': Value(dtype='string', id=None),
'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}
```
**Things I've tried:**
- deleting the cache
- trying other types such as `int64`
Am I missing anything? Thanks for any pointer in the right direction.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/623/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/623/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/622
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/622/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/622/comments
|
https://api.github.com/repos/huggingface/datasets/issues/622/events
|
https://github.com/huggingface/datasets/issues/622
| 700,225,826 |
MDU6SXNzdWU3MDAyMjU4MjY=
| 622 |
load_dataset for text files not working
|
{
"login": "BramVanroy",
"id": 2779410,
"node_id": "MDQ6VXNlcjI3Nzk0MTA=",
"avatar_url": "https://avatars.githubusercontent.com/u/2779410?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/BramVanroy",
"html_url": "https://github.com/BramVanroy",
"followers_url": "https://api.github.com/users/BramVanroy/followers",
"following_url": "https://api.github.com/users/BramVanroy/following{/other_user}",
"gists_url": "https://api.github.com/users/BramVanroy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/BramVanroy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/BramVanroy/subscriptions",
"organizations_url": "https://api.github.com/users/BramVanroy/orgs",
"repos_url": "https://api.github.com/users/BramVanroy/repos",
"events_url": "https://api.github.com/users/BramVanroy/events{/privacy}",
"received_events_url": "https://api.github.com/users/BramVanroy/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067388877,
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug",
"name": "dataset bug",
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library"
}
] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"Can you give us more information on your os and pip environments (pip list)?",
"@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage - Version\r\n---------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.2\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.6\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc2\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.1.0\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0\r\n\r\nWindows 10 - Python 3.8\r\n================\r\nPackage - Version\r\n----------------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnlp 0.4.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.1\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.5.1\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc1\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.0.2\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0",
"Downgrading to 3.7 does not help. Here is a dummy text file:\r\n\r\n```text\r\nVerzekering weigert vaker te betalen\r\nBedrijven van verzekeringen erkennen steeds minder arbeidsongevallen .\r\nIn 2012 weigerden de bedrijven te betalen voor 21.055 ongevallen op het werk .\r\nDat is 11,8 % van alle ongevallen op het werk .\r\nNog nooit weigerden verzekeraars zoveel zaken .\r\nIn 2012 hadden 135.118 mensen een ongeval op het werk .\r\nDat zijn elke werkdag 530 mensen .\r\nBij die ongevallen stierven 67 mensen .\r\nBijna 12.000 hebben een handicap na het ongeval .\r\nGeen echt arbeidsongeval Bedrijven moeten een verzekering hebben voor hun werknemers .\r\n```\r\n\r\nA temporary work around for the \"text\" type, is\r\n\r\n```python\r\ndataset = Dataset.from_dict({\"text\": Path(dataset_f).read_text().splitlines()})\r\n```",
"\r\n\r\neven i am facing the same issue.",
"@banunitte Please do not post screenshots in the future but copy-paste your code and the errors. That allows others to copy-and-paste your code and test it. You may also want to provide the Python version that you are using.",
"I have the exact same problem in Windows 10, Python 3.8.\r\n",
"I have the same problem on Linux of the script crashing with a CSV error. This may be caused by 'CRLF', when changed 'CRLF' to 'LF', the problem solved.",
"I pushed a fix for `pyarrow.lib.ArrowInvalid: CSV parse error`. Let me know if you still have this issue.\r\n\r\nNot sure about the windows one yet",
"To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n```python\r\ndataset = load_dataset('text', script_version='master', data_files=XXX)\r\n```\r\nWe do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).",
"\r\nwin10, py3.6\r\n\r\n\r\n```\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\n\r\nfeatures = Features({'text': Value('string'), 'ctext': Value('string')})\r\nfile_dict = {'train': PATH/'summary.csv'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n```",
"```python\r\nTraceback` (most recent call last):\r\n File \"main.py\", line 281, in <module>\r\n main()\r\n File \"main.py\", line 190, in main\r\n train_data, test_data = data_factory(\r\n File \"main.py\", line 129, in data_factory\r\n train_data = load_dataset('text', \r\n File \"/home/me/Downloads/datasets/src/datasets/load.py\", line 608, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 468, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 546, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 888, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/me/.local/lib/python3.8/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/me/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 103, in _generate_tables\r\n pa_table = pac.read_csv(\r\n File \"pyarrow/_csv.pyx\", line 617, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nUnfortunately i am still getting this issue on Linux. I installed datasets from source and specified script_version to master.\r\n\r\n",
"> \r\n> win10, py3.6\r\n> \r\n> ```\r\n> from datasets import Features, Value, ClassLabel, load_dataset\r\n> \r\n> \r\n> features = Features({'text': Value('string'), 'ctext': Value('string')})\r\n> file_dict = {'train': PATH/'summary.csv'}\r\n> \r\n> dataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n> ```\r\n\r\nSince #644 it should now work on windows @ScottishFold007 \r\n\r\n> Trying the following snippet, I get different problems on Linux and Windows.\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n> # or \r\n> dataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n> ```\r\n>\r\n> Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n> \r\n> ```\r\n> Checking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\n> Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\n> Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\n> Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\n> Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\\dataset_infos.json\r\n> Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\n> Using custom data configuration default\r\n> ```\r\n\r\nSame for you @BramVanroy .\r\n\r\nNot sure about the one on linux though",
"> To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n> \r\n> ```python\r\n> dataset = load_dataset('text', script_version='master', data_files=XXX)\r\n> ```\r\n> \r\n> We do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).\r\n\r\nLinux here:\r\n\r\nI was using the 0.4.0 nlp library load_dataset to load a text dataset of 9-10Gb without collapsing the RAM memory. However, today I got the csv error message mentioned in this issue. After installing the new (datasets) library from source and specifying the script_verson = 'master' I'm still having this same error message. Furthermore, I cannot use the dictionary \"trick\" to load the dataset since the system kills the process due to a RAM out of memory problem. Is there any other solution to this error? Thank you in advance. ",
"Hi @raruidol \r\nTo fix the RAM issue you'll need to shard your text files into smaller files (see https://github.com/huggingface/datasets/issues/610#issuecomment-691672919 for example)\r\n\r\nI'm not sure why you're having the csv error on linux.\r\nDo you think you could to to reproduce it on google colab for example ?\r\nOr send me a dummy .txt file that reproduces the issue ?",
"@lhoestq \r\n\r\nThe crash message shows up when loading the dataset:\r\n```\r\nprint('Loading corpus...') \r\nfiles = glob.glob('corpora/shards/*') \r\n-> dataset = load_dataset('text', script_version='master', data_files=files) \r\nprint('Corpus loaded.')\r\n```\r\nAnd this is the exact message:\r\n```\r\nTraceback (most recent call last):\r\n File \"run_language_modeling.py\", line 27, in <module>\r\n dataset = load_dataset('text', script_version='master', data_files=files)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 471, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 548, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 892, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/jupyter-raruidol/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 107, in _generate_tables\r\n convert_options=self.config.convert_options,\r\n File \"pyarrow/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nAnd these are the pip packages I have atm and their versions:\r\n\r\n```\r\nPackage Version Location \r\n--------------- --------- -------------------------------------------------------------\r\ncertifi 2020.6.20 \r\nchardet 3.0.4 \r\nclick 7.1.2 \r\ndatasets 1.0.2 \r\ndill 0.3.2 \r\nfilelock 3.0.12 \r\nfuture 0.18.2 \r\nidna 2.10 \r\njoblib 0.16.0 \r\nnumpy 1.19.1 \r\npackaging 20.4 \r\npandas 1.1.1 \r\npip 19.0.3 \r\npyarrow 1.0.1 \r\npyparsing 2.4.7 \r\npython-dateutil 2.8.1 \r\npytz 2020.1 \r\nregex 2020.7.14 \r\nrequests 2.24.0 \r\nsacremoses 0.0.43 \r\nsentencepiece 0.1.91 \r\nsetuptools 40.8.0 \r\nsix 1.15.0 \r\ntokenizers 0.8.1rc2 \r\ntorch 1.6.0 \r\ntqdm 4.48.2 \r\ntransformers 3.0.2 /home/jupyter-raruidol/DebatAnalyser/env/src/transformers/src\r\n```\r\n\r\n\r\n",
"I tested on google colab which is also linux using this code:\r\n\r\n- first download an arbitrary text file\r\n```bash\r\nwget https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt\r\n```\r\n- then run\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"text\", data_files=\"all_train.txt\", script_version='master')\r\n```\r\nAnd I don't get this issue.\r\n\r\n\\> Could you test on your side if these lines work @raruidol ?\r\n\r\nalso cc @Skyy93 as it seems you have the same issue\r\n\r\nIf it works:\r\nIt could mean that the issue could come from unexpected patterns in the files you want to use.\r\nIn that case we should find a way to handle them.\r\n\r\nAnd if it doesn't work:\r\nIt could mean that it comes from the way pyarrow reads text files on linux.\r\nIn that case we should report it to pyarrow and find a workaround in the meantime\r\n\r\nEither way it should help to find where this bug comes from and fix it :)\r\n\r\nThank you in advance !",
"Update: also tested the above code in a docker container from [jupyter/minimal-notebook](https://hub.docker.com/r/jupyter/minimal-notebook/) (based on ubuntu) and still not able to reproduce",
"It looks like with your text input file works without any problem. I have been doing some experiments this morning with my input files and I'm almost certain that the crash is caused by some unexpected pattern in the files. However, I've not been able to spot the main cause of it. What I find strange is that this same corpus was being loaded by the nlp 0.4.0 library without any problem... Where can I find the code where you structure the input text data in order to use it with pyarrow?",
"Under the hood it does\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\n# Use csv reader from Pyarrow with one column for text files\r\n\r\n# To force the one-column setting, we set an arbitrary character\r\n# that is not in text files as delimiter, such as \\b or \\v.\r\n# The bell character, \\b, was used to make beeps back in the days\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"all_train.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\nNote that we changed the parse options with datasets 1.0\r\nIn particular the delimiter used to be `\\r` but this delimiter doesn't work on windows.",
"Could you try with `\\a` instead of `\\b` ? It looks like the bell character is \\a in python and not \\b",
"I was just exploring if the crash was happening in every shard or not, and which shards were generating the error message. With \\b I got the following list of shards crashing:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0032', 'corpora/shards/shard_0088', 'corpora/shards/shard_0018', 'corpora/shards/shard_0073', 'corpora/shards/shard_0079', 'corpora/shards/shard_0038', 'corpora/shards/shard_0041', 'corpora/shards/shard_0007', 'corpora/shards/shard_0004', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0030', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0052', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0064', 'corpora/shards/shard_0044', 'corpora/shards/shard_0013', 'corpora/shards/shard_0062', 'corpora/shards/shard_0057', 'corpora/shards/shard_0097', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0075', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0021', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0023', 'corpora/shards/shard_0095', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0086', 'corpora/shards/shard_0047', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0101', 'corpora/shards/shard_0093', 'corpora/shards/shard_0082', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0020', 'corpora/shards/shard_0070', 'corpora/shards/shard_0008', 'corpora/shards/shard_0058', 'corpora/shards/shard_0060', 'corpora/shards/shard_0022', 'corpora/shards/shard_0059', 'corpora/shards/shard_0100', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0066', 'corpora/shards/shard_0042', 'corpora/shards/shard_0053']\r\n```\r\n\r\nI also tried with \\a and the list decreased but there were still several crashes:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0055', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0073', 'corpora/shards/shard_0025', 'corpora/shards/shard_0068', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0044', 'corpora/shards/shard_0087', 'corpora/shards/shard_0092', 'corpora/shards/shard_0074', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0103', 'corpora/shards/shard_0047', 'corpora/shards/shard_0033', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0082', 'corpora/shards/shard_0071', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0070', 'corpora/shards/shard_0058', 'corpora/shards/shard_0081', 'corpora/shards/shard_0060', 'corpora/shards/shard_0002', 'corpora/shards/shard_0059', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0045', 'corpora/shards/shard_0036', 'corpora/shards/shard_0066', 'corpora/shards/shard_0053']\r\n```\r\n\r\nWhich means that it is quite possible that the assumption of that some unexpected pattern in the files is causing the crashes is true. If I am able to reach any conclusion I will post It here asap.",
"Hmmm I was expecting it to work with \\a, not sure why they appear in your text files though",
"Hi @lhoestq, is there any input length restriction which was not before the update of the nlp library?",
"No we never set any input length restriction on our side (maybe arrow but I don't think so)",
"@lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong? \r\n\r\nIf I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?",
"Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones:\r\n\r\n\r\n_4. DE L’ORGANITZACIÓ ESTAMENTAL A L’ORGANITZACIÓ EN CLASSES A mesura que es desenvolupava un sistema econòmic capitalista i naixia una classe burgesa cada vegada més preparada per a substituir els dirigents de les velles monarquies absolutistes, es qüestionava l’abundància de béns amortitzats, que com s’ha dit estaven fora del mercat i no pagaven tributs, pels perjudicis que ocasionaven a les finances públiques i a l’economia en general. Aquest estat d’opinió revolucionari va desembocar en un conjunt de mesures pràctiques de caràcter liberal. D’una banda, les que intentaven desposseir les mans mortes del domini de béns acumulats, procés que acostumem a denominar desamortització, i que no és més que la nacionalització i venda d’aquests béns eclesiàstics o civils en subhasta pública al millor postor. D’altra banda, les que redimien o reduïen els censos i delmes o aixecaven les prohibicions de venda, és a dir, les vinculacions. La desamortització, que va afectar béns dels ordes religiosos, dels pobles i d’algunes corporacions civils, no va ser un camí fàcil, perquè costava i costa trobar algú que sigui indiferent a la pèrdua de béns, drets i privilegis. I té una gran transcendència, va privar els antics estaments de les Espanyes, clero i pobles —la noblesa en queda al marge—, de la força econòmica que els donaven bona part de les seves terres i, en última instància, va preparar el terreny per a la substitució de la vella societat estamental per la nova societat classista. En aquesta societat, en teoria, les agrupacions socials són obertes, no tenen cap estatut jurídic privilegiat i estan definides per la possessió o no d’uns béns econòmics que són lliurement alienables. A les Espanyes la transformació va afectar poc l’aristocràcia latifundista, allà on n’hi havia. Aquesta situació va afavorir, en part, la persistència de la vella cultura de la societat estamental en determinats ambients, i això ha influït decisivament en la manca de democràcia que caracteritza la majoria de règims polítics que s’han anat succeint. Una manera de pensar que sempre sura en un moment o altre, i que de fet no acaba de desaparèixer del tot. 5. INICI DE LA DESAMORTITZACIÓ A LES ESPANYES Durant el segle xviii, dins d’aquesta visió lliberal, va agafar força en alguns cercles de les Espanyes el corrent d’opinió contrari a les mans mortes. Durant el regnat de Carles III, s’arbitraren les primeres mesures desamortitzadores proposades per alguns ministres il·lustrats. Aquestes disposicions foren modestes i poc eficaces, no van aturar l’acumulació de terres per part dels estaments que constituïen les mans mortes i varen afectar principalment béns dels pobles. L’Església no va ser tocada, excepte en el cas de 110_\r\n\r\n_la revolució liberal, perquè, encara que havia perdut els seus drets jurisdiccionals, havia conservat la majoria de terres i fins i tot les havia incrementat amb d’altres que procedien de la desamortització. En la nova situació, les mans mortes del bosc públic eren l’Estat, que no cerca mai l’autofinançament de les despeses de gestió; els diners que manquin ja els posarà l’Estat. 9. DEFENSA I INTENTS DE RECUPERACIÓ DELS BÉNS COMUNALS DESAMORTITZATS El procés de centralització no era senzill, perquè, d’una banda, la nova organització apartava de la gestió moltes corporacions locals i molts veïns que l’havien portada des de l’edat mitjana, i, de l’altra, era difícil de coordinar la nova silvicultura amb moltes pràctiques forestals i drets tradicionals, com la pastura, fer llenya o tallar un arbre aquí i un altre allà quan tenia el gruix suficient, les pràctiques que s’havien fet sempre. Les primeres passes de la nova organització centralitzada varen tenir moltes dificultats en aquells indrets en què els terrenys municipals i comunals tenien un paper important en l’economia local. La desobediència a determinades normes imposades varen prendre formes diferents. Algunes institucions, com, per exemple, la Diputació de Lleida, varen retardar la tramitació d’alguns expedients i varen evitar la venda de béns municipals. Molts pobles permeteren deixar que els veïns continuessin amb les seves pràctiques tradicionals, d’altres varen boicotejar les subhastes d’aprofitaments. L’Estat va reaccionar encomanant a la Guàrdia Civil el compliment de les noves directrius. Imposar el nou règim va costar a l’Administració un grapat d’anys, però de mica en mica, amb molta, molta guarderia i gens de negociació, ho va aconseguir. La nova gestió estatal dels béns municipals va deixar, com hem comentat, molta gent sense uns recursos necessaris per a la supervivència, sobre tot en àrees on predominaven les grans propietats, i on els pagesos sense terra treballaven de jornalers temporers. Això va afavorir que, a bona part de les Espanyes, les primeres lluites camperoles de la segona meitat del segle xix defensessin la recuperació dels comunals desamortitzats; per a molts aquella expropiació i venda dirigida pels governs monàrquics era la causa de molta misèria. D’altres, més radicalitzats, varen entendre que l’eliminació de la propietat col·lectiva i la gestió estatal dels boscos no desamortitzats suposava una usurpació pura i dura. En les zones més afectades per la desamortització això va donar lloc a un imaginari centrat en la defensa del comunal. La Segona República va arribar en una conjuntura econòmica de crisi, generada pel crac del 1929. Al camp, aquesta situació va produir una forta caiguda dels preus dels productes agraris i un increment important de l’atur. QUADERNS AGRARIS 42 (juny 2017), p. 105-126_\r\n\r\nI think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.",
"So we're using the csv reader to read text files because arrow doesn't have a text reader.\r\nTo workaround the fact that text files are just csv with one column, we want to set a delimiter that doesn't appear in text files.\r\nUntil now I thought that it would do the job but unfortunately it looks like even characters like \\a appear in text files.\r\n\r\nSo we have to option:\r\n- find another delimiter that does the job (maybe `\\x1b` esc or `\\x18` cancel)\r\n- don't use the csv reader from arrow but the text reader from pandas instead (or any other reader). The only important thing is that it must be fast (arrow's reader has a nice and fast multithreaded for csv that we're using now but hopefully we can find an alternative)\r\n\r\n\r\n\r\n> @lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong?\r\n\r\nAs long as the text file follows some encoding it wouldn't make sense to have characters such as the bell character. However I agree it can happen.\r\n\r\n> If I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?\r\n\r\nExactly. Arrow doesn't allow the newline character unfortunately.",
"> Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones\r\n\r\nThanks for digging into it !\r\n\r\nCharacters like \\a or \\b are not shown when printing the text, so as it is I can't tell if it contains unexpected characters.\r\nMaybe could could open the file in python and check if `\"\\b\" in open(\"path/to/file\", \"r\").read()` ?\r\n\r\n> I think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.\r\n\r\nTo check that you could try to run \r\n\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\nopen(\"dummy.txt\", \"w\").write(((\"a\" * 10_000) + \"\\n\") * 4) # 4 lines of 10 000 'a'\r\n\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"dummy.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\non my side it runs without error though",
"That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?",
"> That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?\r\n\r\nI don't think it would work since we only want one column, and \"\\b\" is set to be the delimiter between two columns, so it will raise the same issue again. Pyarrow would think that there is more than one column if the delimiter is found somewhere.\r\n\r\nAnyway, I I'll work on a new text reader if we don't find the right workaround about this delimiter issue."
] | 2020-09-12T12:49:28 | 2020-10-28T11:07:31 | 2020-10-28T11:07:30 |
CONTRIBUTOR
| null | null | null |
Trying the following snippet, I get different problems on Linux and Windows.
```python
dataset = load_dataset("text", data_files="data.txt")
# or
dataset = load_dataset("text", data_files=["data.txt"])
```
(ps [This example](https://huggingface.co/docs/datasets/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)
The problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.
Linux stack trace:
```
PyTorch version 1.6.0+cu101 available.
Checking /home/bram/.cache/huggingface/datasets/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.json
Using custom data configuration default
Generating dataset text (/home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)
Downloading and preparing dataset text/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...
Dataset not on Hf google storage. Downloading and preparing it from source
Downloading took 0.0 min
Checksum Computation took 0.0 min
Unable to verify checksums.
Generating split train
Traceback (most recent call last):
File "/home/bram/Python/projects/dutch-simplification/utils.py", line 45, in prepare_data
dataset = load_dataset("text", data_files=dataset_f)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/load.py", line 608, in load_dataset
builder_instance.download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 468, in download_and_prepare
self._download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 546, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 888, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/tqdm/std.py", line 1130, in __iter__
for obj in iterable:
File "/home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 100, in _generate_tables
pa_table = pac.read_csv(
File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2
```
Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:
```
Checking C:\Users\bramv\.cache\huggingface\datasets\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.json
Using custom data configuration default
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/622/reactions",
"total_count": 3,
"+1": 3,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/622/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/620
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/620/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/620/comments
|
https://api.github.com/repos/huggingface/datasets/issues/620/events
|
https://github.com/huggingface/datasets/issues/620
| 699,815,135 |
MDU6SXNzdWU2OTk4MTUxMzU=
| 620 |
map/filter multiprocessing raises errors and corrupts datasets
|
{
"login": "timothyjlaurent",
"id": 2000204,
"node_id": "MDQ6VXNlcjIwMDAyMDQ=",
"avatar_url": "https://avatars.githubusercontent.com/u/2000204?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/timothyjlaurent",
"html_url": "https://github.com/timothyjlaurent",
"followers_url": "https://api.github.com/users/timothyjlaurent/followers",
"following_url": "https://api.github.com/users/timothyjlaurent/following{/other_user}",
"gists_url": "https://api.github.com/users/timothyjlaurent/gists{/gist_id}",
"starred_url": "https://api.github.com/users/timothyjlaurent/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/timothyjlaurent/subscriptions",
"organizations_url": "https://api.github.com/users/timothyjlaurent/orgs",
"repos_url": "https://api.github.com/users/timothyjlaurent/repos",
"events_url": "https://api.github.com/users/timothyjlaurent/events{/privacy}",
"received_events_url": "https://api.github.com/users/timothyjlaurent/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 1935892857,
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/bug",
"name": "bug",
"color": "d73a4a",
"default": true,
"description": "Something isn't working"
}
] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"It seems that I ran into the same problem\r\n```\r\ndef tokenize(cols, example):\r\n for in_col, out_col in cols.items():\r\n example[out_col] = hf_tokenizer.convert_tokens_to_ids(hf_tokenizer.tokenize(example[in_col]))\r\n return example\r\ncola = datasets.load_dataset('glue', 'cola')\r\ntokenized_cola = cola.map(partial(tokenize, {'sentence': 'text_idxs'}),\r\n num_proc=2,)\r\n```\r\nand it outpus (exceprts)\r\n```\r\nConcatenating 2 shards from multiprocessing\r\nSet __getitem__(key) output type to python objects for ['idx', 'label', 'sentence', 'text_idxs'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nDone writing 532 indices in 4256 bytes .\r\nDone writing 531 indices in 4248 bytes .\r\nProcess #0 will write at /home/yisiang/.cache/huggingface/datasets/glue/cola/1.0.0/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542/tokenized_test_00000_of_00002.arrow\r\nProcess #1 will write at /home/yisiang/.cache/huggingface/datasets/glue/cola/1.0.0/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542/tokenized_test_00001_of_00002.arrow\r\nSpawning 2 processes\r\n```\r\nand then the program never stop.",
"same problem.\r\n`encoded_dataset = core_data.map(lambda examples: tokenizer(examples[\"query\"], examples[\"document\"], padding=True, truncation='longest_first', return_tensors=\"pt\", max_length=384), num_proc=16, keep_in_memory=True)`\r\nit outputs:\r\n```\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787499 indices in 25568385696 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nSpawning 16 processes\r\n```",
"Thanks for reporting.\r\n\r\n\r\nWhich tokenizers are you using ? What platform are you on ? Can you tell me which version of datasets and pyarrow you're using ? @timothyjlaurent @richarddwang @HuangLianzhe \r\n\r\nAlso if you're able to reproduce the issue on google colab that would be very helpful.\r\n\r\nI tried to run your code @richarddwang with the bert tokenizer and I wasn't able to reproduce",
"Hi, Sorry that I forgot to see what my version was.\r\nBut after updating datasets to master (editable install), and latest pyarrow. \r\nIt works now ~",
"Sorry, I just noticed this.\r\nI'm running this on MACOS the version of datasets I'm was 1.0.0 but I've also tried it on 1.0.2. `pyarrow==1.0.1`, Python 3.6\r\n\r\nConsider this code:\r\n```python\r\n\r\n loader_path = str(Path(__file__).parent / \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path, name=\"prodigy-ds\", data_files=list(file_paths), cache_dir=cache_dir\r\n )[\"train\"]\r\n valid_relations = set(vocabulary.relation_types.keys())\r\n\r\n ds = ds.filter(filter_good_rows, fn_kwargs=dict(valid_rel_labels=valid_relations))\r\n\r\n ds = ds.map(map_bpe_encodings, batched=True, fn_kwargs=dict(tokenizer=vocabulary.tokenizer), num_proc=10)\r\n\r\n # add all feature data\r\n ner_ds: Dataset = ds.map(\r\n add_bio_tags,\r\n fn_kwargs=dict(ner_label_map=vocabulary.ner_labels, tokenizer=vocabulary.tokenizer),\r\n )\r\n rel_ds: Dataset = ner_ds.map(\r\n relation_ds_factory,\r\n batched=True,\r\n writer_batch_size=100,\r\n fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n )\r\n```\r\nThe loader is essentially a jsonloader with some extra error handling. The data is a jsonlines format with text field and a list of span objects and relation objects. \r\n\r\nIn the `ner_ds` a field, `ner_labels` is added, this is used in the downstream `relation_ds_factory`. It all runs fine in a single process but I get a KeyError error if run with num_proc set\r\n:\r\n\r\n```\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 348, in relation_ds_factory\r\n ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n``` \r\n\r\nThis is just one example of what goes wrong. I've started just saving the dataset as arrow at the end because it takes a long time to map/filter/shuffle and the caching isn't working (tracked it down to byte differences in the pickled functions). \r\n\r\n^^ Interestingly if I heed the warning from Tokenizers and set the environment variable, `TOKENIZERS_PARALLELISM=true` the map just hangs:\r\n\r\n```\r\n[I 200921 21:43:18 filelock:318] Lock 5694118768 released on /Users/timothy.laurent/.cache/huggingface/datasets/_Users_timothy.laurent_.cache_huggingface_datasets_prodigy_dataset_builder_prodigy-ds-5f34378723c4e83f_0.0.0_e67d9b43d5cd82c50b1eae8f2097daf95b601a04dc03ddd504f2b234a5fa247a.lock\r\n100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.34ba/s]\r\n#0: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#2: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#3: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#4: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#5: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#6: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#7: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#8: 0%| | 0/1 [00:00<?, ?ba/s]\r\n```",
"Thank you, I was able to reproduce :)\r\nI'm on it",
"#659 should fix the `KeyError` issue. It was due to the formatting not getting updated the right way",
"Also maybe @n1t0 knows why setting `TOKENIZERS_PARALLELISM=true` creates deadlock issues when calling `map` with multiprocessing ?",
"@lhoestq \r\n\r\nThanks for taking a look. I pulled the master but I still see the key error.\r\n\r\n```\r\nTo disable this warning, you can either:\r\n - Avoid using `tokenizers` before the fork if possible\r\n - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 21.56ba/s]\r\n#1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 17.71ba/s]\r\n#2: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 20.45ba/s]\r\n#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 26.05ba/s]\r\n#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 26.83ba/s]\r\n#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 27.00ba/s]\r\n#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 27.40ba/s]\r\n#7: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 25.91ba/s]\r\n#8: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22.46ba/s]\r\n#9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 20.15ba/s]\r\n#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 26.81ba/s]\r\n#11: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 27.45ba/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 322/322 [00:00<00:00, 1462.85ex/s]\r\nTraceback (most recent call last): | 0/1 [00:00<?, ?ba/s]\r\n File \"text2struct/run_model.py\", line 372, in <module>\r\n main()\r\n File \"text2struct/run_model.py\", line 368, in main | 0/1 [00:00<?, ?ba/s]\r\n run_model(auto_envvar_prefix=\"GFB_CIES\") # pragma: no cover\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 829, in __call__\r\n return self.main(*args, **kwargs) | 0/1 [00:00<?, ?ba/s]\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 782, in main\r\n rv = self.invoke(ctx)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 1236, in invoke\r\n return Command.invoke(self, ctx)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 1066, in invoke\r\n return ctx.invoke(self.callback, **ctx.params)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 610, in invoke\r\n return callback(*args, **kwargs)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/decorators.py\", line 21, in new_func\r\n return f(get_current_context(), *args, **kwargs)\r\n File \"text2struct/run_model.py\", line 136, in run_model\r\n ctx.invoke(ctx.command.commands[config_dict[\"mode\"]])\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 610, in invoke\r\n return callback(*args, **kwargs)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/decorators.py\", line 21, in new_func\r\n return f(get_current_context(), *args, **kwargs)\r\n File \"text2struct/run_model.py\", line 187, in train\r\n run_train_model(_parse_subcommand(ctx))\r\n File \"text2struct/run_model.py\", line 241, in run_train_model\r\n checkpoint_steps=config.train.checkpoint_steps,\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/train.py\", line 153, in alternate_training\r\n max_len=config.model.dim.max_len,\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 466, in load_prodigy_tf_datasets\r\n folder, file_patterns, vocabulary, cache_dir=cache_dir, test_pct=test_pct\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 447, in load_prodigy_arrow_datasets\r\n fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1224, in map\r\n update_data = does_function_return_dict(test_inputs, test_indices)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 348, in relation_ds_factory\r\n ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n\r\n```",
"The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf https://github.com/huggingface/tokenizers/issues/187).\r\nSo if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.",
"> Thanks for taking a look. I pulled the master but I still see the key error.\r\n\r\nI am no longer able to get the error since #659 was merged. Not sure why you still have it @timothyjlaurent \r\nMaybe it is a cache issue ? Could you try to use `load_from_cache_file=False` in your `.map()` calls ?",
"> The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf [huggingface/tokenizers#187](https://github.com/huggingface/tokenizers/issues/187)).\r\n> So if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.\r\n\r\nOk thanks :)\r\n\r\nIs there something we should do on the `datasets` side to avoid that that the program hangs ?\r\n\r\nAlso when doing `.map` with a tokenizer, the tokenizer is called once on the first examples of the dataset to check the function output before spawning the processes. Is that compatible with how tokenizers are supposed to be used with multiprocessing ?",
"#659 fixes the empty dict issue\r\n#688 fixes the hang issue",
"Hmmm I pulled the latest commit, `b93c5517f70a480533a44e0c42638392fd53d90`, and I'm still seeing both the hanging and the key error. ",
"Hi @timothyjlaurent \r\n\r\nThe hanging fix just got merged, that why you still had it.\r\n\r\nFor the key error it's possible that the code you ran reused cached datasets from where the KeyError bug was still there.\r\nCould you try to clear your cache or make sure that it doesn't reuse cached data with `.map(..., load_from_cache=False)` ?\r\nLet me know if it it helps",
"Hi @lhoestq , \r\n\r\nThanks for letting me know about the update.\r\n\r\nSo I don't think it's the caching - because hashing mechanism isn't stable for me -- but that's a different issue. In any case I `rm -rf ~/.cache/huggingface` to make a clean slate.\r\n\r\nI synced with master and I see the key error has gone away, I tried with and without the `TOKENIZERS_PARALLELISM` variable set and see the log line for setting the value false before the map.\r\n\r\nNow I'm seeing an issue with `.train_test_split()` on datasets that are the product of a multiprocess map.\r\n\r\nHere is the stack trace\r\n\r\n```\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 451, in load_prodigy_arrow_datasets\r\n ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/src/datasets/src/datasets/arrow_dataset.py\", line 168, in wrapper\r\n dataset.set_format(**new_format)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/src/datasets/src/datasets/fingerprint.py\", line 163, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/src/datasets/src/datasets/arrow_dataset.py\", line 794, in set_format\r\n list(filter(lambda col: col not in self._data.column_names, columns)), self._data.column_names\r\nValueError: Columns ['train', 'test'] not in the dataset. Current columns in the dataset: ['_input_hash', '_task_hash', '_view_id', 'answer', 'encoding__ids', 'encoding__offsets', 'encoding__overflowing', 'encoding__tokens', 'encoding__words', 'ner_ids', 'ner_labels', 'relations', 'spans', 'text', 'tokens']\r\n```\r\n\r\n\r\n",
"Thanks for reporting.\r\nI'm going to fix that and add a test case so that it doesn't happen again :) \r\nI'll let you know when it's done\r\n\r\nIn the meantime if you could make a google colab that reproduces the issue it would be helpful ! @timothyjlaurent ",
"Sure thing, @lhoestq.\r\n\r\nhttps://colab.research.google.com/drive/1lg4fbyrUO6m8ssQ2dNdVFaUqMUfA2zZ3?usp=sharing",
"Thanks @timothyjlaurent ! I just merged a fix on master. I also checked your notebook and it looks like it's working now.\r\nI added some tests to make sure it works as expected now :)",
"Great, @lhoestq . I'm trying to verify in the colab:\r\nchanged\r\n```\r\n!pip install datasets\r\n```\r\nto \r\n\r\n```\r\n!pip install git+https://github.com/huggingface/datasets@master\r\n```\r\n\r\nBut I'm still seeing the error - I wonder why?",
"It works on my side @timothyjlaurent on google colab.\r\nDid you try to uninstall datasets first, before updating it to master's version ?",
"I didn't -- it was a new sessions --- buuut - look like it's working today -- woot! I'll close this issue. Thanks @lhoestq "
] | 2020-09-11T22:30:06 | 2020-10-08T16:31:47 | 2020-10-08T16:31:46 |
NONE
| null | null | null |
After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.
```python
...
ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)
ner_ds_dict["validation"] = ner_ds_dict["test"]
rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)
rel_ds_dict["validation"] = rel_ds_dict["test"]
return ner_ds_dict, rel_ds_dict
```
The first train_test_split, `ner_ds`/`ner_ds_dict`, returns a `train` and `test` split that are iterable.
The second, `rel_ds`/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.
Ok I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.
I also see errors with other map and filter functions when `num_proc` is set.
```
Done writing 67 indices in 536 bytes .
Done writing 67 indices in 536 bytes .
Fatal Python error: PyCOND_WAIT(gil_cond) failed
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/620/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/620/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/619
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/619/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/619/comments
|
https://api.github.com/repos/huggingface/datasets/issues/619/events
|
https://github.com/huggingface/datasets/issues/619
| 699,733,612 |
MDU6SXNzdWU2OTk3MzM2MTI=
| 619 |
Mistakes in MLQA features names
|
{
"login": "M-Salti",
"id": 9285264,
"node_id": "MDQ6VXNlcjkyODUyNjQ=",
"avatar_url": "https://avatars.githubusercontent.com/u/9285264?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/M-Salti",
"html_url": "https://github.com/M-Salti",
"followers_url": "https://api.github.com/users/M-Salti/followers",
"following_url": "https://api.github.com/users/M-Salti/following{/other_user}",
"gists_url": "https://api.github.com/users/M-Salti/gists{/gist_id}",
"starred_url": "https://api.github.com/users/M-Salti/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/M-Salti/subscriptions",
"organizations_url": "https://api.github.com/users/M-Salti/orgs",
"repos_url": "https://api.github.com/users/M-Salti/repos",
"events_url": "https://api.github.com/users/M-Salti/events{/privacy}",
"received_events_url": "https://api.github.com/users/M-Salti/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Indeed you're right ! Thanks for reporting that\r\n\r\nCould you open a PR to fix the features names ?"
] | 2020-09-11T20:46:23 | 2020-09-16T06:59:19 | 2020-09-16T06:59:19 |
CONTRIBUTOR
| null | null | null |
I think the following features in MLQA shouldn't be named the way they are:
1. `questions` (should be `question`)
2. `ids` (should be `id`)
3. `start` (should be `answer_start`)
The reasons I'm suggesting these features be renamed are:
* To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA etc. and hence make it easier to concatenate multiple QA datasets.
* The features names are not the same as the ones provided in the original MLQA datasets (it uses the names I suggested).
I know these columns can be renamed using using `Dataset.rename_column_`, `questions` and `ids` can be easily renamed but `start` on the other hand is annoying to rename since it's nested inside the feature `answers`.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/619/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/619/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/617
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/617/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/617/comments
|
https://api.github.com/repos/huggingface/datasets/issues/617/events
|
https://github.com/huggingface/datasets/issues/617
| 699,472,596 |
MDU6SXNzdWU2OTk0NzI1OTY=
| 617 |
Compare different Rouge implementations
|
{
"login": "ibeltagy",
"id": 2287797,
"node_id": "MDQ6VXNlcjIyODc3OTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/2287797?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ibeltagy",
"html_url": "https://github.com/ibeltagy",
"followers_url": "https://api.github.com/users/ibeltagy/followers",
"following_url": "https://api.github.com/users/ibeltagy/following{/other_user}",
"gists_url": "https://api.github.com/users/ibeltagy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ibeltagy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ibeltagy/subscriptions",
"organizations_url": "https://api.github.com/users/ibeltagy/orgs",
"repos_url": "https://api.github.com/users/ibeltagy/repos",
"events_url": "https://api.github.com/users/ibeltagy/events{/privacy}",
"received_events_url": "https://api.github.com/users/ibeltagy/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Updates - the differences between the following three\r\n(1) https://github.com/bheinzerling/pyrouge (previously popular. The one I trust the most)\r\n(2) https://github.com/google-research/google-research/tree/master/rouge\r\n(3) https://github.com/pltrdy/files2rouge (used in fairseq)\r\ncan be explained by two things, stemming and handling multiple sentences.\r\n\r\nStemming: \r\n(1), (2): default is no stemming. (3): default is with stemming ==> No stemming is the correct default as you did [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py#L84)\r\n\r\nMultiple sentences:\r\n(1) `rougeL` splits text using `\\n`\r\n(2) `rougeL` ignores `\\n`. \r\n(2) `rougeLsum` splits text using `\\n`\r\n(3) `rougeL` splits text using `.`\r\n\r\nFor (2), `rougeL` and `rougeLsum` are identical if the sequence doesn't contain `\\n`. With `\\n`, it is `rougeLsum` that matches (1) not `rougeL`. \r\n\r\nOverall, and as far as I understand, for your implementation here https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py#L65 to match the default, you only need to change `rougeL` [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py#L86) to `rougeLsum` to correctly compute metrics for text with newlines.\r\n\r\nTagging @sshleifer who might be interested.",
"Thanks for the clarification !\r\nWe're adding Rouge Lsum in #701 ",
"This is a real issue, sorry for missing the mention @ibeltagy\r\n\r\nWe implemented a more involved [solution](https://github.com/huggingface/transformers/blob/99cb924bfb6c4092bed9232bea3c242e27c6911f/examples/seq2seq/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines. \r\n\r\nUnfortunately, the best/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\n#### Sidebar: Wouldn't Deterministic Be Better?\r\n\r\n`rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n\r\nI have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\n",
"> This is a real issue, sorry for missing the mention @ibeltagy\r\n> \r\n> We implemented a more involved [solution](https://github.com/huggingface/transformers/blob/99cb924bfb6c4092bed9232bea3c242e27c6911f/examples/seq2seq/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines.\r\n> \r\n> Unfortunately, the best/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\nThanks for the details, I didn't know about that. Maybe we should consider adding this processing step or at least mention it somewhere in the library or the documentation\r\n\r\n> #### Sidebar: Wouldn't Deterministic Be Better?\r\n> `rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n> \r\n> I have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\nI think the default `n_samples` of the aggregator is 1000. We could increase it or at least allow users to change it if they want more precise results.",
"Hi, thanks for the solution. \r\n\r\nI am not sure if this is a bug, but on line [510](https://github.com/huggingface/transformers/blob/99cb924bfb6c4092bed9232bea3c242e27c6911f/examples/seq2seq/utils.py#L510), are pred, tgt supposed to be swapped?",
"This looks like a bug in an old version of the examples in `transformers`",
"Hi, so I took this example from the HF implementation. What I can see is that the precision of `Hello there` being summarized to `general kenobi` is 1. I don't understand how this calculation is correct.\r\nIs the comparison just counting the words?\r\nand if Yes, then how does this translates to summarization evaluation?\r\n```\r\n >>> rouge = datasets.load_metric('rouge')\r\n >>> predictions = [\"hello there\", \"general kenobi\"]\r\n >>> references = [\"hello there\", \"general kenobi\"]\r\n >>> results = rouge.compute(predictions=predictions, references=references)\r\n >>> print(list(results.keys()))\r\n ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\r\n >>> print(results[\"rouge1\"])\r\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\r\n >>> print(results[\"rouge1\"].mid.fmeasure)\r\n 1.0\r\n\"\"\", stored examples: 0)\r\n```\r\n\r\n\r\n"
] | 2020-09-11T15:49:32 | 2023-03-22T12:08:44 | 2020-10-02T09:52:18 |
NONE
| null | null | null |
I used RougeL implementation provided in `datasets` [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https://arxiv.org/pdf/1909.03186.pdf) for example.
Can you make sure the google-research implementation you are using matches the official perl implementation?
There are a couple of python wrappers around the perl implementation, [this](https://pypi.org/project/pyrouge/) has been commonly used, and [this](https://github.com/pltrdy/files2rouge) is used in fairseq).
There's also a python reimplementation [here](https://github.com/pltrdy/rouge) but its RougeL numbers are way off.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/617/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/617/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/616
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/616/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/616/comments
|
https://api.github.com/repos/huggingface/datasets/issues/616/events
|
https://github.com/huggingface/datasets/issues/616
| 699,462,293 |
MDU6SXNzdWU2OTk0NjIyOTM=
| 616 |
UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors
|
{
"login": "BramVanroy",
"id": 2779410,
"node_id": "MDQ6VXNlcjI3Nzk0MTA=",
"avatar_url": "https://avatars.githubusercontent.com/u/2779410?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/BramVanroy",
"html_url": "https://github.com/BramVanroy",
"followers_url": "https://api.github.com/users/BramVanroy/followers",
"following_url": "https://api.github.com/users/BramVanroy/following{/other_user}",
"gists_url": "https://api.github.com/users/BramVanroy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/BramVanroy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/BramVanroy/subscriptions",
"organizations_url": "https://api.github.com/users/BramVanroy/orgs",
"repos_url": "https://api.github.com/users/BramVanroy/repos",
"events_url": "https://api.github.com/users/BramVanroy/events{/privacy}",
"received_events_url": "https://api.github.com/users/BramVanroy/received_events",
"type": "User",
"site_admin": false
}
|
[] |
open
| false | null |
[] | null |
[
"I have the same issue",
"Same issue here when Trying to load a dataset from disk.",
"I am also experiencing this issue, and don't know if it's affecting my training.",
"Same here. I hope the dataset is not being modified in-place.",
"I think the only way to avoid this warning would be to do a copy of the numpy array before providing it.\r\n\r\nThis would slow down a bit the iteration over the dataset but maybe it would be safer. We could disable the copy with a flag on the `set_format` command.\r\n\r\nIn most typical cases of training a NLP model, PyTorch shouldn't modify the input so it's ok to have a non-writable array but I can understand the warning is a bit scary so maybe we could choose the side of non-warning/slower by default and have an option to speedup.\r\n\r\nWhat do you think @lhoestq ? ",
"@thomwolf Would it be possible to have the array look writeable, but raise an error if it is actually written to?\r\n\r\nI would like to keep my code free of warning, but I also wouldn't like to slow down the program because of unnecessary copy operations. ",
"@AndreasMadsen probably not I would guess (no free lunch hahah)",
"@thomwolf Why not? Writable is checked with `arr.flags.writeable`, and writing is done via magic methods.",
"Well because I don't know the internal of numpy as well as you I guess hahahah, do you want to try to open a PR proposing a solution?",
"@thomwolf @AndreasMadsen I think this is a terrible idea, n/o, and I am very much against it. Modifying internals of an array in such a hacky way is bound to run into other (user) issues down the line. To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing. \r\n\r\nIf your only goal is to get rid of warnings in your code, then you can just use a [simplefilter](https://docs.python.org/3.8/library/warnings.html#temporarily-suppressing-warnings) for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo. \r\n\r\n(Feel free to disagree, of course.)\r\n\r\nIMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.",
"> To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing.\r\n\r\nConfusion can be resolved with a helpful error message. In this case, that error message can be controlled by huggingface/datasets. The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\n> If your only goal is to get rid of warnings in your code, then you can just use a simplefilter for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo.\r\n\r\nI don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\n> IMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.\r\n\r\nPlain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\n---\r\n\r\nThe best solution would be to allow non-writable arrays in `DataLoader`, but that is a PyTorch issue.",
"> The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\nThat's exactly the argument in my first sentence. Too often someone \"cannot think of a use-case\", but you can not foresee the use-cases of a whole research community.\r\n \r\n> I don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\nThat's fair.\r\n\r\n> Plain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\nBut this is not a plain use-case (because Pytorch does not support these read-only tensors). Manually setting the flag to writable will solve the issue on the surface but is basically just a hack to compensate for something that is not allowed in another library. \r\n\r\nWhat about an \"ignore_warnings\" flag in `set_format` that when True wraps the offending code in a block to ignore userwarnings at that specific step in [_convert_outputs](https://github.com/huggingface/datasets/blob/880c2c76a8223a00c303eab2909371e857113063/src/datasets/arrow_dataset.py#L821)? Something like:\r\n\r\n```python\r\ndef _convert_outputs(..., ignore_warnings=True):\r\n ...\r\n with warnings.catch_warnings():\r\n if ignore_warnings:\r\n warnings.simplefilter(\"ignore\", UserWarning)\r\n return torch.tensor(...)\r\n# continues without warning filter after context manager...\r\n```",
"> But this is not a plain use-case (because Pytorch does not support these read-only tensors).\r\n\r\nBy \"plain\", I mean the recommended way to use `datasets` with PyTorch according to the `datasets` documentation.",
"This error is what I see when I run the first lines of the Pytorch Quickstart. It should also say that it should be ignored and/or how to fix it. BTW, this is a Pytorch error message -- not a Huggingface error message. My code runs anyway."
] | 2020-09-11T15:39:16 | 2021-07-22T21:12:21 | null |
CONTRIBUTOR
| null | null | null |
I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:
> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns (when key is int or slice) and don't output other (un-formatted) columns.
> C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\datasets\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:141.)
> return torch.tensor(x, **format_kwargs)
The first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.
Snippet:
```
dataset = Dataset.from_dict(torch.load("data/dummy.pt.pt"))
print(dataset)
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
keys_to_retain = {"input_ids", "sembedding"}
dataset = dataset.map(lambda example: tokenizer(example["text"], padding='max_length'), batched=True)
dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)
dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)
print(next(iter(dataloader)))
```
PS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/616/reactions",
"total_count": 4,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 4
}
|
https://api.github.com/repos/huggingface/datasets/issues/616/timeline
| null | null |
https://api.github.com/repos/huggingface/datasets/issues/615
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/615/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/615/comments
|
https://api.github.com/repos/huggingface/datasets/issues/615/events
|
https://github.com/huggingface/datasets/issues/615
| 699,410,773 |
MDU6SXNzdWU2OTk0MTA3NzM=
| 615 |
Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0
|
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Related: https://issues.apache.org/jira/browse/ARROW-9773\r\n\r\nIt's definitely a size thing. I took a smaller dataset with 87000 rows and did:\r\n```\r\nfor i in range(10,1000,20):\r\n table = pa.concat_tables([dset._data]*i)\r\n table.take([0])\r\n```\r\nand it broke at around i=300.\r\n\r\nAlso when `_indices` is not None, this breaks indexing by slice. E.g. `dset.shuffle()[:1]` breaks.\r\n\r\nLuckily so far I haven't seen `_indices.column(0).take` break, which means it doesn't break `select` or anything like that which is where the speed really matters, it's just `_getitem`. So I'm currently working around it by just doing the arrow v0 method in `_getitem`:\r\n```\r\n#if PYARROW_V0:\r\ndata_subset = pa.concat_tables(\r\n self._data.slice(indices_array[i].as_py(), 1) for i in range(len(indices_array))\r\n)\r\n#else:\r\n #data_subset = self._data.take(indices_array)\r\n```",
"Let me know if you meet other offset overflow issues @joeddav ",
"Will this problem be solved in newer version?",
"This specific issue has been fixed in https://github.com/huggingface/datasets/pull/645\r\n\r\nIf you still have this error, could you open a new issue and explain how to reproduce the error ?",
"same error here in version 2.1.0",
"Facing the same issue. \r\nSteps to reproduce: (dataset is a few GB big so try in colab maybe)\r\nDatasets version - 2.11.0\r\n```\r\nimport datasets\r\nimport re\r\n\r\nds = datasets.load_dataset('nishanthc/dnd_map_dataset_v0.1', split = 'train')\r\n\r\ndef get_text_caption(example):\r\n regex_pattern = r'\\s\\d+x\\d+|,\\sLQ|,\\sgrid|\\.\\w+$'\r\n example['text_caption'] = re.sub(regex_pattern, '', example['picture_text'])\r\n return example\r\n\r\nds = ds.map(get_text_caption)\r\n```\r\n\r\nI am trying to apply a regex to remove certain patterns from a text column. Not sure why this error is showing up.",
"Got this error on a very large data set (900m rows, 35 cols) performing a similar batch map operation.",
"There is a solution that has been proposed here: https://github.com/huggingface/datasets/issues/5783",
"@lhoestq I ran into this problem with load_dataset. What should I do\r\n",
"What version of `datasets` are you using ? Feel free to open a new issue with some details (e.g. what dataset you loaded, what code you ran etc)",
"@lhoestq It's been solved,thanks"
] | 2020-09-11T14:50:38 | 2023-09-21T07:59:23 | 2020-09-19T16:46:31 |
MEMBER
| null | null | null |
How to reproduce:
```python
from datasets import load_dataset
wiki = load_dataset("wikipedia", "20200501.en", split="train")
wiki[[0]]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-381aedc9811b> in <module>
----> 1 wikipedia[[0]]
~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in __getitem__(self, key)
1069 format_columns=self._format_columns,
1070 output_all_columns=self._output_all_columns,
-> 1071 format_kwargs=self._format_kwargs,
1072 )
1073
~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)
1037 )
1038 else:
-> 1039 data_subset = self._data.take(indices_array)
1040
1041 if format_type is not None:
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.take()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/compute.py in take(data, indices, boundscheck)
266 """
267 options = TakeOptions(boundscheck)
--> 268 return call_function('take', [data, indices], options)
269
270
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: offset overflow while concatenating arrays
```
It seems to work fine with small datasets or with pyarrow 0.17.1
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/615/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/615/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/611
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/611/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/611/comments
|
https://api.github.com/repos/huggingface/datasets/issues/611/events
|
https://github.com/huggingface/datasets/issues/611
| 698,863,988 |
MDU6SXNzdWU2OTg4NjM5ODg=
| 611 |
ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648
|
{
"login": "sangyx",
"id": 32364921,
"node_id": "MDQ6VXNlcjMyMzY0OTIx",
"avatar_url": "https://avatars.githubusercontent.com/u/32364921?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/sangyx",
"html_url": "https://github.com/sangyx",
"followers_url": "https://api.github.com/users/sangyx/followers",
"following_url": "https://api.github.com/users/sangyx/following{/other_user}",
"gists_url": "https://api.github.com/users/sangyx/gists{/gist_id}",
"starred_url": "https://api.github.com/users/sangyx/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/sangyx/subscriptions",
"organizations_url": "https://api.github.com/users/sangyx/orgs",
"repos_url": "https://api.github.com/users/sangyx/repos",
"events_url": "https://api.github.com/users/sangyx/events{/privacy}",
"received_events_url": "https://api.github.com/users/sangyx/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Can you give us stats/information on your pandas DataFrame?",
"```\r\n<class 'pandas.core.frame.DataFrame'>\r\nInt64Index: 17136104 entries, 0 to 17136103\r\nData columns (total 6 columns):\r\n # Column Dtype \r\n--- ------ ----- \r\n 0 item_id int64 \r\n 1 item_titl object \r\n 2 start_price float64\r\n 3 shipping_fee float64\r\n 4 picture_url object \r\n 5 embeddings object \r\ndtypes: float64(2), int64(1), object(3)\r\nmemory usage: 915.2+ MB\r\n```",
"Thanks and some more on the `embeddings` and `picture_url` would be nice as well (type and max lengths of the elements)",
"`embedding` is `np.array` of shape `(128,)`. `picture_url` is url, such as 'https://i.ebayimg.com/00/s/MTE5OVgxNjAw/z/ZOsAAOSwAG9fHQq5/$_12.JPG?set_id=880000500F;https://i.ebayimg.com/00/s/MTE5OVgxNjAw/z/OSgAAOSwokBfHQq8/$_12.JPG?set_id=880000500F'",
"It looks like a Pyarrow limitation.\r\nI was able to reproduce the error with \r\n\r\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\n n = 1713614\r\ndf = pd.DataFrame.from_dict({\"a\": list(np.zeros((n, 128))), \"b\": range(n)})\r\npa.Table.from_pandas(df)\r\n```\r\n\r\nI also tried with 50% of the dataframe and it actually works.\r\nI created an issue on Apache Arrow's JIRA [here](https://issues.apache.org/jira/browse/ARROW-9976)\r\n\r\nOne way to fix that would be to chunk the dataframe and concatenate arrow tables.",
"It looks like it's going to be fixed in pyarrow 2.0.0 :)\r\n\r\nIn the meantime I suggest to chunk big dataframes to create several small datasets, and then concatenate them using [concatenate_datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets)"
] | 2020-09-11T05:29:12 | 2022-06-01T15:11:43 | 2022-06-01T15:11:43 |
NONE
| null | null | null |
Hi, I'm trying to load a dataset from Dataframe, but I get the error:
```bash
---------------------------------------------------------------------------
ArrowCapacityError Traceback (most recent call last)
<ipython-input-7-146b6b495963> in <module>
----> 1 dataset = Dataset.from_pandas(emb)
~/miniconda3/envs/dev/lib/python3.7/site-packages/nlp/arrow_dataset.py in from_pandas(cls, df, features, info, split)
223 info.features = features
224 pa_table: pa.Table = pa.Table.from_pandas(
--> 225 df=df, schema=pa.schema(features.type) if features is not None else None
226 )
227 return cls(pa_table, info=info, split=split)
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pandas()
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)
591 for i, maybe_fut in enumerate(arrays):
592 if isinstance(maybe_fut, futures.Future):
--> 593 arrays[i] = maybe_fut.result()
594
595 types = [x.type for x in arrays]
~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/pandas_compat.py in convert_column(col, field)
557
558 try:
--> 559 result = pa.array(col, type=type_, from_pandas=True, safe=safe)
560 except (pa.ArrowInvalid,
561 pa.ArrowNotImplementedError,
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._ndarray_to_array()
~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648
```
My code is :
```python
from nlp import Dataset
dataset = Dataset.from_pandas(emb)
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/611/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/611/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/610
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/610/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/610/comments
|
https://api.github.com/repos/huggingface/datasets/issues/610/events
|
https://github.com/huggingface/datasets/issues/610
| 698,349,388 |
MDU6SXNzdWU2OTgzNDkzODg=
| 610 |
Load text file for RoBERTa pre-training.
|
{
"login": "chiyuzhang94",
"id": 33407613,
"node_id": "MDQ6VXNlcjMzNDA3NjEz",
"avatar_url": "https://avatars.githubusercontent.com/u/33407613?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/chiyuzhang94",
"html_url": "https://github.com/chiyuzhang94",
"followers_url": "https://api.github.com/users/chiyuzhang94/followers",
"following_url": "https://api.github.com/users/chiyuzhang94/following{/other_user}",
"gists_url": "https://api.github.com/users/chiyuzhang94/gists{/gist_id}",
"starred_url": "https://api.github.com/users/chiyuzhang94/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/chiyuzhang94/subscriptions",
"organizations_url": "https://api.github.com/users/chiyuzhang94/orgs",
"repos_url": "https://api.github.com/users/chiyuzhang94/repos",
"events_url": "https://api.github.com/users/chiyuzhang94/events{/privacy}",
"received_events_url": "https://api.github.com/users/chiyuzhang94/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Could you try\r\n```python\r\nload_dataset('text', data_files='test.txt',cache_dir=\"./\", split=\"train\")\r\n```\r\n?\r\n\r\n`load_dataset` returns a dictionary by default, like {\"train\": your_dataset}",
"Hi @lhoestq\r\nThanks for your suggestion.\r\n\r\nI tried \r\n```\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\"./\", split=\"train\")\r\nprint(dataset)\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut it still doesn't work and got error:\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-7-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)\r\n 361 \r\n 362 def __next__(self):\r\n--> 363 data = self._next_data()\r\n 364 self._num_yielded += 1\r\n 365 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)\r\n 401 def _next_data(self):\r\n 402 index = self._next_index() # may raise StopIteration\r\n--> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 404 if self._pin_memory:\r\n 405 data = _utils.pin_memory.pin_memory(data)\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)\r\n 42 def fetch(self, possibly_batched_index):\r\n 43 if self.auto_collation:\r\n---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]\r\n 45 else:\r\n 46 data = self.dataset[possibly_batched_index]\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)\r\n 42 def fetch(self, possibly_batched_index):\r\n 43 if self.auto_collation:\r\n---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]\r\n 45 else:\r\n 46 data = self.dataset[possibly_batched_index]\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in __getitem__(self, key)\r\n 1069 format_columns=self._format_columns,\r\n 1070 output_all_columns=self._output_all_columns,\r\n-> 1071 format_kwargs=self._format_kwargs,\r\n 1072 )\r\n 1073 \r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)\r\n 1056 format_columns=format_columns,\r\n 1057 output_all_columns=output_all_columns,\r\n-> 1058 format_kwargs=format_kwargs,\r\n 1059 )\r\n 1060 return outputs\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in _convert_outputs(self, outputs, format_type, format_columns, output_all_columns, format_kwargs)\r\n 872 continue\r\n 873 if format_columns is None or k in format_columns:\r\n--> 874 v = map_nested(command, v, **map_nested_kwargs)\r\n 875 output_dict[k] = v\r\n 876 return output_dict\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n 214 # Singleton\r\n 215 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 216 return function(data_struct)\r\n 217 \r\n 218 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in command(x)\r\n 833 if x.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects\r\n 834 return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n--> 835 return torch.tensor(x, **format_kwargs)\r\n 836 \r\n 837 elif format_type == \"tensorflow\":\r\n\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nI found type can be ['numpy', 'torch', 'tensorflow', 'pandas'] only, how can I deal with the string type?",
"You need to tokenize the string inputs to convert them in integers before you can feed them to a pytorch dataloader.\r\n\r\nYou can read the quicktour of the datasets or the transformers libraries to know more about that:\r\n- transformers: https://huggingface.co/transformers/quicktour.html\r\n- dataset: https://huggingface.co/docs/datasets/quicktour.html",
"Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\nBut finally got it working. This is what I did after looking into the documentation.\r\n\r\n1. split the whole dataset file into smaller files\r\n```bash\r\nmkdir ./shards\r\nsplit -a 4 -l 256000 -d full_raw_corpus.txt ./shards/shard_\r\n````\r\n2. Pass paths of small data files to `load_dataset`\r\n```python\r\nfiles = glob.glob('shards/*')\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files=files, split='train')\r\n```\r\n(On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n\r\n3. Tokenization\r\n```python\r\ndef encode(examples):\r\n return tokenizer(examples['text'], truncation=True, padding='max_length')\r\ndataset = dataset.map(encode, batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n```python\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\nnext(iter(dataloader))\r\n```\r\nHope this helps\r\n",
"Thanks, @thomwolf and @sipah00 ,\r\n\r\nI tried to implement your suggestions in my scripts. \r\nNow, I am facing some connection time-out error. I am using my local file, I have no idea why the module request s3 database.\r\n\r\nThe log is:\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/.local/lib/python3.6/site-packages/requests/adapters.py\", line 449, in send\r\n raise err\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/util/connection.py\", line 74, in create_connection\r\n timeout=timeout\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 720, in urlopen\r\n sock.connect(sa)\r\nTimeoutError: [Errno 110] Connection timed out\r\n\r\nTraceback (most recent call last):\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 672, in urlopen\r\n method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/util/retry.py\", line 436, in increment\r\n chunked=chunked,\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 376, in _make_request\r\n raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/text/text.py (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection obj\r\nect at 0x7fff401e0e48>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\nTraceback (most recent call last):\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 1019, in <module>\r\n main()\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 962, in main\r\n train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 177, in load_and_cache_examples\r\n return HG_Datasets(tokenizer, file_path, args)\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 117, in HG_Datasets\r\n dataset = load_dataset('text', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n File \"/arc/project/evn_py36/datasets/datasets/src/datasets/load.py\", line 590, in load_dataset\r\n self._validate_conn(conn)\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 994, in _validate_conn\r\n conn.connect()\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connection.py\", line 300, in connect\r\n conn = self._new_conn()\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connection.py\", line 169, in _new_conn\r\n self, \"Failed to establish a new connection: %s\" % e\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.VerifiedHTTPSConnection object at 0x7fff401e0da0>: Failed to establish a new connection: [Errno 110] Connection timed out\r\n\r\n``` \r\n\r\nDo you have any experience on this issue?",
"No, I didn't encounter this problem, it seems to me a network problem",
"I noticed this is because I use a cloud server where does not provide for connections from our standard compute nodes to outside resources. \r\n\r\nFor the `datasets` package, it seems that if the loading script is not already cached in the library it will attempt to connect to an AWS resource to download the dataset loading script. \r\n\r\nI am wondering why the package works in this way. Do you have any suggestions to solve this issue? ",
"I solved the above issue by downloading text.py manually and passing the path to the `load_dataset` function. \r\n\r\nNow, I have a new issue with the Read-only file system.\r\n\r\nThe error is: \r\n```\r\nI0916 22:14:38.453380 140737353971520 filelock.py:274] Lock 140734268996072 acquired on /scratch/chiyuzh/roberta/text.py.lock\r\nFound main folder for dataset /scratch/chiyuzh/roberta/text.py at /home/chiyuzh/.cache/huggingface/modules/datasets_modules/datasets/text\r\nCreating specific version folder for dataset /scratch/chiyuzh/roberta/text.py at /home/chiyuzh/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\r\nI0916 22:14:38.530371 140737353971520 filelock.py:318] Lock 140734268996072 released on /scratch/chiyuzh/roberta/text.py.lock\r\nTraceback (most recent call last):\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 1019, in <module>\r\n main()\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 962, in main\r\n train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 177, in load_and_cache_examples\r\n return HG_Datasets(tokenizer, file_path, args)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 117, in HG_Datasets\r\n dataset = load_dataset('/scratch/chiyuzh/roberta/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n File \"/arc/project/chiyuzh/evn_py36/datasets/src/datasets/load.py\", line 590, in load_dataset\r\n path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n File \"/arc/project/chiyuzh/evn_py36/datasets/src/datasets/load.py\", line 385, in prepare_module\r\n os.makedirs(hash_folder_path)\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/os.py\", line 220, in makedirs\r\n mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '/home/chiyuzh/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014'\r\n\r\n```\r\n\r\nI installed datasets at /project/chiyuzh/evn_py36/datasets/src where is a writable directory.\r\nI also tried change the environment variables to the writable directory:\r\n`export HF_MODULES_PATH=/project/chiyuzh/evn_py36/datasets/cache_dir/`\r\n`export HF_DATASETS_CACHE=/project/chiyuzh/evn_py36/datasets/cache_dir/`\r\n \r\nIn my scripts, I also changed to:\r\n`dataset = load_dataset('/scratch/chiyuzh/roberta/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")`\r\n`data_cache_dir = $TMPDIR/data/` that also a writable directory.\r\n \r\nBut it still try to make directory at /home/chiyuzh/.cache/huggingface/modules/.\r\nDo you have any idea about this issue? @thomwolf \r\n",
"> Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> But finally got it working. This is what I did after looking into the documentation.\r\n> \r\n> 1. split the whole dataset file into smaller files\r\n> \r\n> ```shell\r\n> mkdir ./shards\r\n> split -a 4 -l 256000 -d full_raw_corpus.txt ./shards/shard_\r\n> ```\r\n> \r\n> 1. Pass paths of small data files to `load_dataset`\r\n> \r\n> ```python\r\n> files = glob.glob('shards/*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files, split='train')\r\n> ```\r\n> \r\n> (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> \r\n> 1. Tokenization\r\n> \r\n> ```python\r\n> def encode(examples):\r\n> return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> dataset = dataset.map(encode, batched=True)\r\n> dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> ```\r\n> \r\n> Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> \r\n> ```python\r\n> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> next(iter(dataloader))\r\n> ```\r\n> \r\n> Hope this helps\r\n\r\nWhen I run 'dataset = dataset.map(encode, batched=True)',\r\nI encountered a problem like this:\r\n\r\n> Testing the mapped function outputs\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in map\r\n for k, dataset in self.items()\r\n File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in <dictcomp>\r\n for k, dataset in self.items()\r\n File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1224, in map\r\n update_data = does_function_return_dict(test_inputs, test_indices)\r\n File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n File \"<stdin>\", line 3, in encode\r\nTypeError: __init__() takes 1 positional argument but 2 were given",
"> > Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> > But finally got it working. This is what I did after looking into the documentation.\r\n> > \r\n> > 1. split the whole dataset file into smaller files\r\n> > \r\n> > ```shell\r\n> > mkdir ./shards\r\n> > split -a 4 -l 256000 -d full_raw_corpus.txt ./shards/shard_\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > 1. Pass paths of small data files to `load_dataset`\r\n> > \r\n> > ```python\r\n> > files = glob.glob('shards/*')\r\n> > from datasets import load_dataset\r\n> > dataset = load_dataset('text', data_files=files, split='train')\r\n> > ```\r\n> > \r\n> > \r\n> > (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> > \r\n> > 1. Tokenization\r\n> > \r\n> > ```python\r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > dataset = dataset.map(encode, batched=True)\r\n> > dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> > ```\r\n> > \r\n> > \r\n> > Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> > ```python\r\n> > dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> > next(iter(dataloader))\r\n> > ```\r\n> > \r\n> > \r\n> > Hope this helps\r\n> \r\n> When I run 'dataset = dataset.map(encode, batched=True)',\r\n> I encountered a problem like this:\r\n> \r\n> > Testing the mapped function outputs\r\n> > Traceback (most recent call last):\r\n> > File \"\", line 1, in \r\n> > File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in map\r\n> > for k, dataset in self.items()\r\n> > File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in \r\n> > for k, dataset in self.items()\r\n> > File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1224, in map\r\n> > update_data = does_function_return_dict(test_inputs, test_indices)\r\n> > File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n> > function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n> > File \"\", line 3, in encode\r\n> > TypeError: **init**() takes 1 positional argument but 2 were given\r\n\r\nWhat is your encoder function?",
"> ```python\r\n> def encode(examples):\r\n> return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> ```\r\n\r\nIt is the same as suggested:\r\n\r\n> def encode(examples):\r\n return tokenizer(examples['text'], truncation=True, padding='max_length')",
"> > ```python\r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > ```\r\n> \r\n> It is the same as suggested:\r\n> \r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\nDo you use this function in a `class` object? \r\n\r\ninit() takes 1 positional argument but 2 were given. I guess the additional argument is self?",
"> > > ```python\r\n> > > def encode(examples):\r\n> > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > ```\r\n> > \r\n> > \r\n> > It is the same as suggested:\r\n> > > def encode(examples):\r\n> > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> Do you use this function in a `class` object?\r\n> \r\n> init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n\r\nThanks for your reply.\r\nCould you provide some simple example here?\r\nCurrently, I do not use this function in a class object. \r\nI think you are right and I was wondering how to construct this class.\r\nI try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n\r\n> class LineByLineTextDataset(Dataset):\r\n \"\"\"\r\n This will be superseded by a framework-agnostic approach\r\n soon.\r\n \"\"\"\r\n\r\n def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n # Here, we do not cache the features, operating under the assumption\r\n # that we will soon use fast multithreaded tokenizers from the\r\n # `tokenizers` repo everywhere =)\r\n #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n #with open(file_path, encoding=\"utf-8\") as f:\r\n # lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n\r\n\timport glob\r\n\tfiles = glob.glob('/home/mtzhang111/fairseq/cs_doc/shards/shard_003*')\r\n\tfrom datasets import load_dataset\r\n\tdataset = load_dataset('text', data_files=files)\r\n batch_encoding= dataset.map(encode, batched=True)\r\n self.examples = batch_encoding[\"input_ids\"]\r\n\t\r\n\r\n def encode(examples):\r\n return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\n def __len__(self):\r\n return len(self.examples)\r\n\r\n def __getitem__(self, i) -> torch.Tensor:\r\n return torch.tensor(self.examples[i], dtype=torch.long)\r\n",
"> > > > ```python\r\n> > > > def encode(examples):\r\n> > > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > > ```\r\n> > > \r\n> > > \r\n> > > It is the same as suggested:\r\n> > > > def encode(examples):\r\n> > > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > \r\n> > \r\n> > Do you use this function in a `class` object?\r\n> > init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n> \r\n> Thanks for your reply.\r\n> Could you provide some simple example here?\r\n> Currently, I do not use this function in a class object.\r\n> I think you are right and I was wondering how to construct this class.\r\n> I try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n> \r\n> > class LineByLineTextDataset(Dataset):\r\n> > \"\"\"\r\n> > This will be superseded by a framework-agnostic approach\r\n> > soon.\r\n> > \"\"\"\r\n> \r\n> ```\r\n> def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n> assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n> # Here, we do not cache the features, operating under the assumption\r\n> # that we will soon use fast multithreaded tokenizers from the\r\n> # `tokenizers` repo everywhere =)\r\n> #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n> #with open(file_path, encoding=\"utf-8\") as f:\r\n> # lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n> #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n> \r\n> import glob\r\n> files = glob.glob('/home/mtzhang111/fairseq/cs_doc/shards/shard_003*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files)\r\n> batch_encoding= dataset.map(encode, batched=True)\r\n> self.examples = batch_encoding[\"input_ids\"]\r\n> \r\n> \r\n> def encode(examples):\r\n> return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> def __len__(self):\r\n> return len(self.examples)\r\n> \r\n> def __getitem__(self, i) -> torch.Tensor:\r\n> return torch.tensor(self.examples[i], dtype=torch.long)\r\n> ```\r\n\r\nI am also struggling with this adaptation. \r\nI am not sure whether I am right.\r\n\r\nI think you don't need to construct `class LazyLineByLineTextDataset(Dataset)` at all. \r\ntorch.utils.data.Dataset is a generator. \r\n\r\nNow, we use `dataset = dataset.map(encode, batched=True)` as a generator. So we just pass dataset to torch.utils.data.DataLoader. ",
"@chiyuzhang94 Thanks for your reply. After some changes, currently, I managed to make the data loading process running.\r\nI published it in case you might want to take a look. Thanks for your help!\r\nhttps://github.com/shizhediao/Transformers_TPU",
"Hi @shizhediao ,\r\n\r\nThanks! It looks great!\r\n\r\nBut my problem still is the cache directory is a read-only file system. \r\n[As I mentioned](https://github.com/huggingface/datasets/issues/610#issuecomment-693912285), I tried to change the cache directory but it didn't work. \r\n\r\nDo you have any suggestions?\r\n\r\n",
"> I installed datasets at /project/chiyuzh/evn_py36/datasets/src where is a writable directory.\r\n> I also tried change the environment variables to the writable directory:\r\n> `export HF_MODULES_PATH=/project/chiyuzh/evn_py36/datasets/cache_dir/`\r\n\r\nI think it is `HF_MODULES_CACHE` and not `HF_MODULES_PATH` @chiyuzhang94 .\r\nCould you try again and let me know if it fixes your issue ?\r\n",
"We should probably add a section in the doc on the caching system with the env variables in particular.",
"Hi @thomwolf , @lhoestq ,\r\n\r\nThanks for your suggestions. With the latest version of this package, I can load text data without Internet. \r\n\r\nBut I found the speed of dataset loading is very slow. \r\n\r\nMy scrips like this: \r\n```\r\n def token_encode(examples):\r\n tokenizer_out = tokenizer(examples['text'], truncation=True, padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n return tokenizer_out\r\n \r\n path = Path(file_path)\r\n files = sorted(path.glob('*'))\r\n dataset = load_dataset('./text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n dataset = dataset.map(token_encode, batched=True)\r\n\r\n dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n\r\nI have 1,123,870,657 lines in my input directory. \r\nI can find the processing speed as following. It is very slow. \r\n```\r\n| 13/1123871 [00:02<62:37:39, 4.98ba/s]^M 0%| \r\n| 14/1123871 [00:03<61:27:31, 5.08ba/s]^M 0%| \r\n| 15/1123871 [00:03<66:34:19, 4.69ba/s]^M 0%| \r\n| 16/1123871 [00:03<68:25:01, 4.56ba/s]^M 0%| \r\n| 17/1123871 [00:03<72:00:03, 4.34ba/s]^M 0%| \r\n```\r\nDo you have any suggestions to accelerate this loading process?",
"You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n\r\nAlso it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\nAm I right ?",
"> You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n> \r\n> Also it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\n> Am I right ?\r\n\r\nHi @lhoestq ,\r\n\r\nThanks. I will try it.\r\n\r\nYou are right. I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nI have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work? \r\n",
"Hi @lhoestq ,\r\n\r\nI tried to use multi-processor, but I got errors as follow: \r\nBecause I am using python distributed training, it seems some conflicts with the distributed job. \r\n\r\nDo you have any suggestions?\r\n```\r\nI0925 10:19:35.603023 140737353971520 filelock.py:318] Lock 140737229443368 released on /tmp/pbs.1120510.pbsha.ib.sockeye/cache/_tmp_pbs.1120510.pbsha.ib.sockeye_cache_text_default-7fb934ed6fac5d01_0.0.0_512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7\r\nfcc649178b014.lock\r\nTraceback (most recent call last):\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 1024, in <module>\r\n main()\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 967, in main\r\n train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 180, in load_and_cache_examples\r\n return HG_Datasets(tokenizer, file_path, args)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 119, in HG_Datasets\r\n dataset = dataset.map(token_encode, batched=True, batch_size = 10000, num_proc = 16)\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1287, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1287, in <listcomp>\r\n transformed_shards = [r.get() for r in results]\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/pool.py\", line 644, in get\r\n raise self._value\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/pool.py\", line 424, in _handle_tasks\r\n put(task)\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/connection.py\", line 206, in send\r\n self._send_bytes(_ForkingPickler.dumps(obj))\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/reduction.py\", line 51, in dumps\r\n cls(buf, protocol).dump(obj)\r\nAttributeError: Can't pickle local object 'HG_Datasets.<locals>.token_encode'\r\n```",
"For multiprocessing, the function given to `map` must be picklable.\r\nMaybe you could try to define `token_encode` outside `HG_Datasets` ?\r\n\r\nAlso maybe #656 could make functions defined locally picklable for multiprocessing, once it's merged.",
"> I have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work?\r\n\r\nFeel free to save your processed dataset using `dataset.save_to_disk(\"path/to/save/directory\")`.\r\n\r\nThen you'll be able to reload it again using\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset = load_from_disk(\"path/to/save/directory\")\r\n```",
"Hi @lhoestq ,\r\n\r\nThanks for your suggestion. \r\nI tried to process the dataset and save it to disk. \r\nI have 1.12B samples in the raw dataset. I used 16 processors.\r\nI run this process job for 7 days. But it didn't finish. I don't why the processing is such slow. \r\n\r\nThe log shows that some processors (\\#12, \\#14, \\#15) are very slow. The different processor has a different speed. These slow processors look like a bottleneck. \r\n\r\nCould you please give me any suggestion to improve the processing speed? \r\n\r\nThanks. \r\nChiyu\r\n\r\nHere is my code:\r\n```\r\ndef token_encode(examples):\r\n tokenizer_out = tokenizer(examples['text'], truncation=True, padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n return tokenizer_out\r\n\r\npath = Path(file_path)\r\nfiles = sorted(path.glob('*'))\r\ndataset = load_dataset('./text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\ndataset = dataset.map(token_encode, batched=True, batch_size = 16384, num_proc = 16)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\ndataset.save_to_disk(output_dir)\r\n```\r\nHere is the log. \r\n```\r\n^M#6: 1%|▏ | 59/4288 [55:10<66:11:58, 56.35s/ba]\r\n^M#1: 8%|▊ | 356/4288 [55:39<10:40:02, 9.77s/ba]\r\n^M#2: 5%|▍ | 210/4288 [55:33<17:47:19, 15.70s/ba]\r\n^M#0: 19%|█▉ | 836/4288 [55:53<4:08:56, 4.33s/ba]\r\n^M#0: 20%|█▉ | 837/4288 [55:57<4:01:52, 4.21s/ba]\r\n^M#1: 8%|▊ | 357/4288 [55:48<10:38:09, 9.74s/ba]\r\n^M#0: 20%|█▉ | 838/4288 [56:01<4:02:56, 4.23s/ba]\r\n^M#3: 4%|▎ | 155/4288 [55:43<24:41:20, 21.51s/ba]\r\n^M#0: 20%|█▉ | 839/4288 [56:05<4:04:48, 4.26s/ba]\r\n^M#12: 1%| | 29/4288 [54:50<133:20:53, 112.72s/ba]\r\n^M#2: 5%|▍ | 211/4288 [55:48<17:40:33, 15.61s/ba]\r\n^M#14: 0%| | 2/4288 [04:24<157:17:50, 132.12s/ba]\r\n^M#15: 0%| | 1/4288 [02:24<172:11:37, 144.60s/ba]\r\n```",
"Hi !\r\n\r\nAs far as I can tell, there could be several reasons for your processes to have different speeds:\r\n- some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n- OR there are other processes running that prevent some of them to run at full speed\r\n- OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n\r\nSo I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\nWhen doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n\r\n```python\r\nmy_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n```\r\n\r\nHope this helps, let me know if you find what is causing this slow down.",
"Do you use a fast or a slow tokenizer from the `transformers` library @chiyuzhang94?",
"> Do you use a fast or a slow tokenizer from the `transformers` library @chiyuzhang94?\r\n\r\nHi @thomwolf ,\r\n I use this: \r\n```\r\nfrom transformers import\r\nAutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n```\r\n\r\nI guess this is a slow one, let me explore the fast tokenizer. ",
"> Hi !\r\n> \r\n> As far as I can tell, there could be several reasons for your processes to have different speeds:\r\n> \r\n> * some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n> * OR there are other processes running that prevent some of them to run at full speed\r\n> * OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n> \r\n> So I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\n> When doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n> \r\n> ```python\r\n> my_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n> ```\r\n> \r\n> Hope this helps, let me know if you find what is causing this slow down.\r\n\r\nHi @lhoestq ,\r\n\r\nThanks for your suggestions. \r\nI don't think my problem is due to any one of these seasons. \r\n\r\n1. I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines. The last file is smaller a little bit. But they are similar. I randomly shuffled all the 1,123,870,657 lines. Hence, the sequences should also be similar across all the files. \r\n\r\n2. I run this script on the entire node. I requested all the resources on the nodes (40 CPUs, 384GB memory). Hence, these were not any other processes. \r\n\r\n 3. As I say, the node has 40 CPUs, but I set num_proc = 16. This should not be a problem.",
"Hi @thomwolf \r\nI am using `RobertaTokenizerFast` now. \r\n\r\nBut the speed is still imbalanced, some processors are still slow. \r\nHere is the part of the log. #0 is always much fast than lower rank processors. \r\n\r\n```\r\n#15: 3%|▎ | 115/3513 [3:18:36<98:01:33, 103.85s/ba]\r\n#2: 24%|██▍ | 847/3513 [3:20:43<11:06:49, 15.01s/ba]\r\n#1: 37%|███▋ | 1287/3513 [3:20:52<6:19:02, 10.22s/ba]\r\n#0: 72%|███████▏ | 2546/3513 [3:20:52<1:51:03, 6.89s/ba]\r\n#3: 18%|█▊ | 617/3513 [3:20:36<15:50:30, 19.69s/ba]\r\n#0: 73%|███████▎ | 2547/3513 [3:20:59<1:50:25, 6.86s/ba]\r\n#1: 37%|███▋ | 1288/3513 [3:21:02<6:21:13, 10.28s/ba]\r\n#7: 7%|▋ | 252/3513 [3:20:09<44:09:03, 48.74s/ba]\r\n#12: 4%|▍ | 144/3513 [3:19:19<78:00:54, 83.36s/ba]\r\n#4: 14%|█▍ | 494/3513 [3:20:37<20:46:06, 24.77s/ba]\r\n#0: 73%|███████▎ | 2548/3513 [3:21:06<1:49:26, 6.80s/ba]\r\n#2: 24%|██▍ | 848/3513 [3:20:58<11:06:17, 15.00s/ba]\r\n```\r\nHere is my script related to the datasets processing, \r\n\r\n```\r\ntokenizer = RobertaTokenizerFast.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n\r\ndef token_encode(examples):\r\n tokenizer_out = tokenizer(examples['text'], truncation=True, padding=\"max_length\", add_special_tokens=True, max_length=128)\r\n return tokenizer_out\r\n\r\ndef HG_Datasets(tokenizer, file_path, args):\r\n \r\n path = Path(file_path)\r\n files = sorted(path.glob('*'))\r\n dataset = load_dataset('./text.py', data_files=files, cache_dir = \"\"./, split=\"train\")\r\n dataset = dataset.map(token_encode, batched=True, batch_size = 20000, num_proc = 16)\r\n\r\n dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n return dataset\r\n\r\n```\r\nI have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nCould you please give any suggestion? Thanks very much!!"
] | 2020-09-10T18:41:38 | 2022-11-22T13:51:24 | 2022-11-22T13:51:23 |
NONE
| null | null | null |
I migrate my question from https://github.com/huggingface/transformers/pull/4009#issuecomment-690039444
I tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file.
According to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.
```
from datasets import load_dataset
dataset = load_dataset('text', data_files='test.txt',cache_dir="./")
dataset.set_format(type='torch',columns=["text"])
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
next(iter(dataloader))
```
But dataload cannot yield sample and error is:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-12-388aca337e2f> in <module>
----> 1 next(iter(dataloader))
/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
361
362 def __next__(self):
--> 363 data = self._next_data()
364 self._num_yielded += 1
365 if self._dataset_kind == _DatasetKind.Iterable and \
/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
401 def _next_data(self):
402 index = self._next_index() # may raise StopIteration
--> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
404 if self._pin_memory:
405 data = _utils.pin_memory.pin_memory(data)
/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
KeyError: 0
```
`dataset.set_format(type='torch',columns=["text"])` returns a log says:
```
Set __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.
```
I noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.
Each sample can be accessed by `dataset["train"]["text"]` instead of `dataset["text"]`.
Could you please give me any suggestions on how to modify this code to load the text file?
Versions:
Python version 3.7.3
PyTorch version 1.6.0
TensorFlow version 2.3.0
datasets version: 1.0.1
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/610/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/610/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/608
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/608/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/608/comments
|
https://api.github.com/repos/huggingface/datasets/issues/608/events
|
https://github.com/huggingface/datasets/issues/608
| 698,291,156 |
MDU6SXNzdWU2OTgyOTExNTY=
| 608 |
Don't use the old NYU GLUE dataset URLs
|
{
"login": "jeswan",
"id": 57466294,
"node_id": "MDQ6VXNlcjU3NDY2Mjk0",
"avatar_url": "https://avatars.githubusercontent.com/u/57466294?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jeswan",
"html_url": "https://github.com/jeswan",
"followers_url": "https://api.github.com/users/jeswan/followers",
"following_url": "https://api.github.com/users/jeswan/following{/other_user}",
"gists_url": "https://api.github.com/users/jeswan/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jeswan/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jeswan/subscriptions",
"organizations_url": "https://api.github.com/users/jeswan/orgs",
"repos_url": "https://api.github.com/users/jeswan/repos",
"events_url": "https://api.github.com/users/jeswan/events{/privacy}",
"received_events_url": "https://api.github.com/users/jeswan/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Feel free to open the PR ;)\r\nThanks for updating the dataset_info.json file !"
] | 2020-09-10T17:47:02 | 2020-09-16T06:53:18 | 2020-09-16T06:53:18 |
CONTRIBUTOR
| null | null | null |
NYU is switching dataset hosting from Google to FB. Initial changes to `datasets` are in https://github.com/jeswan/nlp/commit/b7d4a071d432592ded971e30ef73330529de25ce. What tests do you suggest I run before opening a PR?
See: https://github.com/jiant-dev/jiant/issues/161 and https://github.com/nyu-mll/jiant/pull/1112
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/608/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/608/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/600
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/600/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/600/comments
|
https://api.github.com/repos/huggingface/datasets/issues/600/events
|
https://github.com/huggingface/datasets/issues/600
| 697,496,913 |
MDU6SXNzdWU2OTc0OTY5MTM=
| 600 |
Pickling error when loading dataset
|
{
"login": "kandorm",
"id": 17310286,
"node_id": "MDQ6VXNlcjE3MzEwMjg2",
"avatar_url": "https://avatars.githubusercontent.com/u/17310286?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/kandorm",
"html_url": "https://github.com/kandorm",
"followers_url": "https://api.github.com/users/kandorm/followers",
"following_url": "https://api.github.com/users/kandorm/following{/other_user}",
"gists_url": "https://api.github.com/users/kandorm/gists{/gist_id}",
"starred_url": "https://api.github.com/users/kandorm/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/kandorm/subscriptions",
"organizations_url": "https://api.github.com/users/kandorm/orgs",
"repos_url": "https://api.github.com/users/kandorm/repos",
"events_url": "https://api.github.com/users/kandorm/events{/privacy}",
"received_events_url": "https://api.github.com/users/kandorm/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"When I change from python3.6 to python3.8, it works! ",
"Does it work when you install `nlp` from source on python 3.6?",
"No, still the pickling error.",
"I wasn't able to reproduce on google colab (python 3.6.9 as well) with \r\n\r\npickle==4.0\r\ndill=0.3.2\r\ntransformers==3.1.0\r\ndatasets=1.0.1 (also tried nlp 0.4.0)\r\n\r\nIf I try\r\n\r\n```python\r\nfrom datasets import load_dataset # or from nlp\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=512), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nIt runs without error",
"Closing since it looks like it's working on >= 3.6.9\r\nFeel free to re-open if you have other questions :)"
] | 2020-09-10T06:28:08 | 2020-09-25T14:31:54 | 2020-09-25T14:31:54 |
NONE
| null | null | null |
Hi,
I modified line 136 in the original [run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) as:
```
# line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
dataset = load_dataset("text", data_files=file_path, split="train")
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
truncation=True, max_length=args.block_size), batched=True)
dataset.set_format(type='torch', columns=['input_ids'])
return dataset
```
When I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error:
```
Traceback (most recent call last):
File "src/run_language_modeling.py", line 319, in <module>
main()
File "src/run_language_modeling.py", line 248, in main
get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
File "src/run_language_modeling.py", line 139, in get_dataset
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True)
File "/data/nlp/src/nlp/arrow_dataset.py", line 1136, in map
new_fingerprint=new_fingerprint,
File "/data/nlp/src/nlp/fingerprint.py", line 158, in wrapper
self._fingerprint, transform, kwargs_for_fingerprint
File "/data/nlp/src/nlp/fingerprint.py", line 105, in update_fingerprint
hasher.update(transform_args[key])
File "/data/nlp/src/nlp/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/data/nlp/src/nlp/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/data/nlp/src/nlp/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/data/nlp/src/nlp/utils/py_utils.py", line 362, in dumps
dump(obj, file)
File "/data/nlp/src/nlp/utils/py_utils.py", line 339, in dump
Pickler(file, recurse=True).dump(obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump
StockPickler.dump(self, obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 409, in dump
self.save(obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function
obj.__dict__, fkwdefaults), obj=obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 605, in save_reduce
save(cls)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type
obj.__bases__, _dict), obj=obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce
save(args)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple
save(element)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems
save(v)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 507, in save
self.save_global(obj, rv)
File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 927, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/600/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/600/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/598
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/598/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/598/comments
|
https://api.github.com/repos/huggingface/datasets/issues/598/events
|
https://github.com/huggingface/datasets/issues/598
| 697,156,501 |
MDU6SXNzdWU2OTcxNTY1MDE=
| 598 |
The current version of the package on github has an error when loading dataset
|
{
"login": "zeyuyun1",
"id": 43428393,
"node_id": "MDQ6VXNlcjQzNDI4Mzkz",
"avatar_url": "https://avatars.githubusercontent.com/u/43428393?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/zeyuyun1",
"html_url": "https://github.com/zeyuyun1",
"followers_url": "https://api.github.com/users/zeyuyun1/followers",
"following_url": "https://api.github.com/users/zeyuyun1/following{/other_user}",
"gists_url": "https://api.github.com/users/zeyuyun1/gists{/gist_id}",
"starred_url": "https://api.github.com/users/zeyuyun1/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/zeyuyun1/subscriptions",
"organizations_url": "https://api.github.com/users/zeyuyun1/orgs",
"repos_url": "https://api.github.com/users/zeyuyun1/repos",
"events_url": "https://api.github.com/users/zeyuyun1/events{/privacy}",
"received_events_url": "https://api.github.com/users/zeyuyun1/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase class",
"I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time. Didn't realize loading the data part requires using tokenizer.\r\n",
"Yes it shouldn’t fail with older version of transformers since this is only a special feature to make caching more efficient when using transformers for tokenization.\r\nWe’ll update this."
] | 2020-09-09T21:03:23 | 2020-09-10T06:25:21 | 2020-09-09T22:57:28 |
NONE
| null | null | null |
Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):
To recreate the error:
First, installing nlp directly from source:
```
git clone https://github.com/huggingface/nlp.git
cd nlp
pip install -e .
```
Then run:
```
from nlp import load_dataset
dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')
```
will give error:
```
>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')
Checking /home/zeyuy/.cache/huggingface/datasets/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Found script file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.py
Found dataset infos file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/dataset_infos.json to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.json
Loading Dataset Infos from /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Overwrite dataset info from restored data version.
Loading Dataset info from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Reusing dataset wikitext (/home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)
Constructing Dataset for split train, from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/load.py", line 600, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 611, in as_dataset
datasets = utils.map_nested(
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 216, in map_nested
return function(data_struct)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 631, in _build_single_dataset
ds = self._as_dataset(
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 704, in _as_dataset
return Dataset(**dataset_kwargs)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/arrow_dataset.py", line 188, in __init__
self._fingerprint = generate_fingerprint(self)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 91, in generate_fingerprint
hasher.update(key)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 361, in dumps
with _no_cache_fields(obj):
File "/home/zeyuy/miniconda3/lib/python3.8/contextlib.py", line 113, in __enter__
return next(self.gen)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 348, in _no_cache_fields
if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, "cache") and isinstance(obj.cache, dict):
AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/598/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/598/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/597
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/597/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/597/comments
|
https://api.github.com/repos/huggingface/datasets/issues/597/events
|
https://github.com/huggingface/datasets/issues/597
| 697,112,029 |
MDU6SXNzdWU2OTcxMTIwMjk=
| 597 |
Indices incorrect with multiprocessing
|
{
"login": "joeddav",
"id": 9353833,
"node_id": "MDQ6VXNlcjkzNTM4MzM=",
"avatar_url": "https://avatars.githubusercontent.com/u/9353833?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/joeddav",
"html_url": "https://github.com/joeddav",
"followers_url": "https://api.github.com/users/joeddav/followers",
"following_url": "https://api.github.com/users/joeddav/following{/other_user}",
"gists_url": "https://api.github.com/users/joeddav/gists{/gist_id}",
"starred_url": "https://api.github.com/users/joeddav/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/joeddav/subscriptions",
"organizations_url": "https://api.github.com/users/joeddav/orgs",
"repos_url": "https://api.github.com/users/joeddav/repos",
"events_url": "https://api.github.com/users/joeddav/events{/privacy}",
"received_events_url": "https://api.github.com/users/joeddav/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?",
"Still the case on master.\r\nI guess we should have an offset in the multi-procs indeed (hopefully it's enough).\r\n\r\nAlso, side note is that we should add some logging before the \"test\" to say we are testing the function otherwise its confusing for the user to see two outputs I think. Proposal (see the \"Testing the mapped function outputs:\" lines):\r\n```\r\n>>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\nDone writing 10 indices in 80 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nSpawning 2 processes\r\nTesting the mapped function outputs:\r\ninds: [0, 1]\r\ninds: [0, 1]\r\nTesting finished, running the mapped function on the dataset:\r\n#0: 0%| | 0/1 [00:00<?, ?ba/s]\r\ninds: [0, 1, 2, 3, 4] inds: [0, 1, 2, 3, 4] | 0/1 [00:00<?, ?ba/s]\r\n#0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1321.04ba/s]\r\n#1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1841.22ba/s]\r\nConcatenating 2 shards from multiprocessing\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None)}, num_rows: 10)\r\n```"
] | 2020-09-09T19:50:56 | 2020-09-10T11:03:37 | 2020-09-10T11:03:37 |
CONTRIBUTOR
| null | null | null |
When `num_proc` > 1, the indices argument passed to the map function is incorrect:
```python
d = load_dataset('imdb', split='test[:1%]')
def fn(x, inds):
print(inds)
return x
d.select(range(10)).map(fn, with_indices=True, batched=True)
# [0, 1]
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)
# [0, 1]
# [0, 1]
# [0, 1, 2, 3, 4]
# [0, 1, 2, 3, 4]
```
As you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/597/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/597/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/595
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/595/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/595/comments
|
https://api.github.com/repos/huggingface/datasets/issues/595/events
|
https://github.com/huggingface/datasets/issues/595
| 696,892,304 |
MDU6SXNzdWU2OTY4OTIzMDQ=
| 595 |
`Dataset`/`DatasetDict` has no attribute 'save_to_disk'
|
{
"login": "sudarshan85",
"id": 488428,
"node_id": "MDQ6VXNlcjQ4ODQyOA==",
"avatar_url": "https://avatars.githubusercontent.com/u/488428?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/sudarshan85",
"html_url": "https://github.com/sudarshan85",
"followers_url": "https://api.github.com/users/sudarshan85/followers",
"following_url": "https://api.github.com/users/sudarshan85/following{/other_user}",
"gists_url": "https://api.github.com/users/sudarshan85/gists{/gist_id}",
"starred_url": "https://api.github.com/users/sudarshan85/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/sudarshan85/subscriptions",
"organizations_url": "https://api.github.com/users/sudarshan85/orgs",
"repos_url": "https://api.github.com/users/sudarshan85/repos",
"events_url": "https://api.github.com/users/sudarshan85/events{/privacy}",
"received_events_url": "https://api.github.com/users/sudarshan85/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"`pip install git+https://github.com/huggingface/nlp.git` should have done the job.\r\n\r\nDid you uninstall `nlp` before installing from github ?",
"> Did you uninstall `nlp` before installing from github ?\r\n\r\nI did not. I created a new environment and installed `nlp` directly from `github` and it worked!\r\n\r\nThanks.\r\n"
] | 2020-09-09T15:01:52 | 2020-09-09T16:20:19 | 2020-09-09T16:20:18 |
NONE
| null | null | null |
Hi,
As the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method. While the file [`arrow_dataset.py`](https://github.com/huggingface/nlp/blob/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1/src/nlp/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https://github.com/huggingface/nlp.git ` and still no luck. Do I need to install the library in another way?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/595/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/595/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/590
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/590/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/590/comments
|
https://api.github.com/repos/huggingface/datasets/issues/590/events
|
https://github.com/huggingface/datasets/issues/590
| 696,501,827 |
MDU6SXNzdWU2OTY1MDE4Mjc=
| 590 |
The process cannot access the file because it is being used by another process (windows)
|
{
"login": "saareliad",
"id": 22762845,
"node_id": "MDQ6VXNlcjIyNzYyODQ1",
"avatar_url": "https://avatars.githubusercontent.com/u/22762845?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/saareliad",
"html_url": "https://github.com/saareliad",
"followers_url": "https://api.github.com/users/saareliad/followers",
"following_url": "https://api.github.com/users/saareliad/following{/other_user}",
"gists_url": "https://api.github.com/users/saareliad/gists{/gist_id}",
"starred_url": "https://api.github.com/users/saareliad/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/saareliad/subscriptions",
"organizations_url": "https://api.github.com/users/saareliad/orgs",
"repos_url": "https://api.github.com/users/saareliad/repos",
"events_url": "https://api.github.com/users/saareliad/events{/privacy}",
"received_events_url": "https://api.github.com/users/saareliad/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi, which version of `nlp` are you using?\r\n\r\nBy the way we'll be releasing today a significant update fixing many issues (but also comprising a few breaking changes).\r\nYou can see more informations here #545 and try it by installing from source from the master branch.",
"I'm using version 0.4.0.\r\n\r\n",
"Ok, it's probably fixed on master. Otherwise if you can give me a fully self-contained exemple to reproduce the error, I can try to investigate.",
"I get the same behavior, on Windows, when `map`ping a function to a loaded dataset. \r\nThe error doesn't occur if I re-run the cell a second time though! \r\nI'm on version 1.0.1.",
"This is going to be fixed by #644 ",
"@saareliad I got the same issue that troubled me quite a while. Unfortunately, there are no good answers to this issue online, I tried it on Linux and that's absolutely fine. After hacking the source code, I solved this problem as follows.\r\n\r\nIn the source code file: arrow_dataset.py -> _map_single(...)\r\n\r\nchange\r\n```python\r\nif update_data and tmp_file is not None:\r\n shutil.move(tmp_file.name, cache_file_name)\r\n```\r\nto\r\n```python\r\ntmp_file.close()\r\nif update_data and tmp_file is not None:\r\n shutil.move(tmp_file.name, cache_file_name)\r\n```\r\n\r\nThen it works without needing multiple times runs to avoid the permission error.\r\nI know this solution is unusual since it changes the source code. Hopefully, the lib's contributors can have better solutions in the future.\r\n",
"@wangcongcong123 thanks for sharing.\n(BTW I also solved it locally on windows by putting the problematic line under try except and not using cache... On windows I just needed 1% of the dataset anyway)"
] | 2020-09-09T07:01:36 | 2020-09-25T14:02:28 | 2020-09-25T14:02:28 |
NONE
| null | null | null |
Hi, I consistently get the following error when developing in my PC (windows 10):
```
train_dataset = train_dataset.map(convert_to_features, batched=True)
File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\site-packages\nlp\arrow_dataset.py", line 970, in map
shutil.move(tmp_file.name, cache_file_name)
File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\shutil.py", line 803, in move
os.unlink(src)
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\saareliad\\.cache\\huggingface\\datasets\\squad\\plain_text\\1.0.0\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\tmpsau1bep1'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/590/reactions",
"total_count": 1,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 1
}
|
https://api.github.com/repos/huggingface/datasets/issues/590/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/589
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/589/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/589/comments
|
https://api.github.com/repos/huggingface/datasets/issues/589/events
|
https://github.com/huggingface/datasets/issues/589
| 696,488,447 |
MDU6SXNzdWU2OTY0ODg0NDc=
| 589 |
Cannot use nlp.load_dataset text, AttributeError: module 'nlp.utils' has no attribute 'logging'
|
{
"login": "ksjae",
"id": 17930170,
"node_id": "MDQ6VXNlcjE3OTMwMTcw",
"avatar_url": "https://avatars.githubusercontent.com/u/17930170?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ksjae",
"html_url": "https://github.com/ksjae",
"followers_url": "https://api.github.com/users/ksjae/followers",
"following_url": "https://api.github.com/users/ksjae/following{/other_user}",
"gists_url": "https://api.github.com/users/ksjae/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ksjae/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ksjae/subscriptions",
"organizations_url": "https://api.github.com/users/ksjae/orgs",
"repos_url": "https://api.github.com/users/ksjae/repos",
"events_url": "https://api.github.com/users/ksjae/events{/privacy}",
"received_events_url": "https://api.github.com/users/ksjae/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-09-09T06:46:53 | 2020-09-09T08:57:54 | 2020-09-09T08:57:54 |
NONE
| null | null | null |
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 533, in load_dataset
builder_cls = import_main_class(module_path, dataset=True)
File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 61, in import_main_class
module = importlib.import_module(module_path)
File "/root/anaconda3/envs/pytorch/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/datasets/text/5dc629379536c4037d9c2063e1caa829a1676cf795f8e030cd90a537eba20c08/text.py", line 9, in <module>
logger = nlp.utils.logging.get_logger(__name__)
AttributeError: module 'nlp.utils' has no attribute 'logging'
```
Occurs on the following code, or any code including the load_dataset('text'):
```
dataset = load_dataset("text", data_files=file_path, split="train")
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
truncation=True, max_length=args.block_size), batched=True)
dataset.set_format(type='torch', columns=['input_ids'])
return dataset
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/589/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/589/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/583
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/583/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/583/comments
|
https://api.github.com/repos/huggingface/datasets/issues/583/events
|
https://github.com/huggingface/datasets/issues/583
| 695,166,265 |
MDU6SXNzdWU2OTUxNjYyNjU=
| 583 |
ArrowIndexError on Dataset.select
|
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-09-07T14:36:29 | 2020-09-08T07:43:15 | 2020-09-08T07:43:15 |
MEMBER
| null | null | null |
If the indices table consists in several chunks, then `dataset.select` results in an `ArrowIndexError` error for pyarrow < 1.0.0
Example:
```python
from nlp import load_dataset
mnli = load_dataset("glue", "mnli", split="train")
shuffled = mnli.shuffle(seed=42)
mnli.select(list(range(len(mnli))))
```
raises:
```python
---------------------------------------------------------------------------
ArrowIndexError Traceback (most recent call last)
<ipython-input-64-006a5d38d418> in <module>
----> 1 mnli.shuffle(seed=42).select(list(range(len(mnli))))
~/Desktop/hf/nlp/src/nlp/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/Desktop/hf/nlp/src/nlp/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
1653 if self._indices is not None:
1654 if PYARROW_V0:
-> 1655 indices_array = self._indices.column(0).chunk(0).take(indices_array)
1656 else:
1657 indices_array = self._indices.column(0).take(indices_array)
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.Array.take()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIndexError: take index out of bounds
```
This is because the `take` method is only done on the first chunk which only contains 1000 elements by default (mnli has ~400 000 elements).
Shall we change that to use
```python
pa.concat_tables(self._indices._indices.slice(i, 1) for i in indices_array)
```
instead of `take` ? @thomwolf
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/583/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/583/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/582
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/582/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/582/comments
|
https://api.github.com/repos/huggingface/datasets/issues/582/events
|
https://github.com/huggingface/datasets/issues/582
| 695,126,456 |
MDU6SXNzdWU2OTUxMjY0NTY=
| 582 |
Allow for PathLike objects
|
{
"login": "BramVanroy",
"id": 2779410,
"node_id": "MDQ6VXNlcjI3Nzk0MTA=",
"avatar_url": "https://avatars.githubusercontent.com/u/2779410?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/BramVanroy",
"html_url": "https://github.com/BramVanroy",
"followers_url": "https://api.github.com/users/BramVanroy/followers",
"following_url": "https://api.github.com/users/BramVanroy/following{/other_user}",
"gists_url": "https://api.github.com/users/BramVanroy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/BramVanroy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/BramVanroy/subscriptions",
"organizations_url": "https://api.github.com/users/BramVanroy/orgs",
"repos_url": "https://api.github.com/users/BramVanroy/repos",
"events_url": "https://api.github.com/users/BramVanroy/events{/privacy}",
"received_events_url": "https://api.github.com/users/BramVanroy/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-09-07T13:54:51 | 2020-09-08T07:45:17 | 2020-09-08T07:45:17 |
CONTRIBUTOR
| null | null | null |
Using PathLike objects as input for `load_dataset` does not seem to work. The following will throw an error.
```python
files = list(Path(r"D:\corpora\yourcorpus").glob("*.txt"))
dataset = load_dataset("text", data_files=files)
```
Traceback:
```
Traceback (most recent call last):
File "C:/dev/python/dutch-simplification/main.py", line 7, in <module>
dataset = load_dataset("text", data_files=files)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 470, in download_and_prepare
self._save_info()
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 564, in _save_info
self.info.write_to_directory(self._cache_dir)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 149, in write_to_directory
self._dump_info(f)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 156, in _dump_info
file.write(json.dumps(asdict(self)).encode("utf-8"))
File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 257, in iterencode
return _iterencode(o, 0)
TypeError: keys must be str, int, float, bool or None, not WindowsPath
```
We have to cast to a string explicitly to make this work. It would be nicer if we could actually use PathLike objects.
```python
files = [str(f) for f in Path(r"D:\corpora\wablieft").glob("*.txt")]
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/582/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/582/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/581
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/581/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/581/comments
|
https://api.github.com/repos/huggingface/datasets/issues/581/events
|
https://github.com/huggingface/datasets/issues/581
| 695,120,517 |
MDU6SXNzdWU2OTUxMjA1MTc=
| 581 |
Better error message when input file does not exist
|
{
"login": "BramVanroy",
"id": 2779410,
"node_id": "MDQ6VXNlcjI3Nzk0MTA=",
"avatar_url": "https://avatars.githubusercontent.com/u/2779410?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/BramVanroy",
"html_url": "https://github.com/BramVanroy",
"followers_url": "https://api.github.com/users/BramVanroy/followers",
"following_url": "https://api.github.com/users/BramVanroy/following{/other_user}",
"gists_url": "https://api.github.com/users/BramVanroy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/BramVanroy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/BramVanroy/subscriptions",
"organizations_url": "https://api.github.com/users/BramVanroy/orgs",
"repos_url": "https://api.github.com/users/BramVanroy/repos",
"events_url": "https://api.github.com/users/BramVanroy/events{/privacy}",
"received_events_url": "https://api.github.com/users/BramVanroy/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-09-07T13:47:59 | 2020-09-09T09:00:07 | 2020-09-09T09:00:07 |
CONTRIBUTOR
| null | null | null |
In the following scenario, when `data_files` is an empty list, the stack trace and error message could be improved. This can probably be solved by checking for each file whether it actually exists and/or whether the argument is not false-y.
```python
dataset = load_dataset("text", data_files=[])
```
Example error trace.
```
Using custom data configuration default
Downloading and preparing dataset text/default-d18f9b6611eb8e16 (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to C:\Users\bramv\.cache\huggingface\datasets\text\default-d18f9b6611eb8e16\0.0.0\3a79870d85f1982d6a2af884fde86a71c771747b4b161fd302d28ad22adf985b...
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 424, in incomplete_dir
yield tmp_dir
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 537, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 813, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\arrow_writer.py", line 217, in finalize
self.pa_writer.close()
AttributeError: 'NoneType' object has no attribute 'close'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:/dev/python/dutch-simplification/main.py", line 7, in <module>
dataset = load_dataset("text", data_files=files)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 470, in download_and_prepare
self._save_info()
File "c:\users\bramv\appdata\local\programs\python\python38\lib\contextlib.py", line 131, in __exit__
self.gen.throw(type, value, traceback)
File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 430, in incomplete_dir
shutil.rmtree(tmp_dir)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 737, in rmtree
return _rmtree_unsafe(path, onerror)
File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 615, in _rmtree_unsafe
onerror(os.unlink, fullname, sys.exc_info())
File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 613, in _rmtree_unsafe
os.unlink(fullname)
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\bramv\\.cache\\huggingface\\datasets\\text\\default-d18f9b6611eb8e16\\0.0.0\\3a79870d85f1982d6a2af884fde86a71c771747b4b161fd302d28ad22adf985b.incomplete\\text-train.arrow'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/581/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/581/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/580
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/580/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/580/comments
|
https://api.github.com/repos/huggingface/datasets/issues/580/events
|
https://github.com/huggingface/datasets/issues/580
| 694,954,551 |
MDU6SXNzdWU2OTQ5NTQ1NTE=
| 580 |
nlp re-creates already-there caches when using a script, but not within a shell
|
{
"login": "TevenLeScao",
"id": 26709476,
"node_id": "MDQ6VXNlcjI2NzA5NDc2",
"avatar_url": "https://avatars.githubusercontent.com/u/26709476?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/TevenLeScao",
"html_url": "https://github.com/TevenLeScao",
"followers_url": "https://api.github.com/users/TevenLeScao/followers",
"following_url": "https://api.github.com/users/TevenLeScao/following{/other_user}",
"gists_url": "https://api.github.com/users/TevenLeScao/gists{/gist_id}",
"starred_url": "https://api.github.com/users/TevenLeScao/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/TevenLeScao/subscriptions",
"organizations_url": "https://api.github.com/users/TevenLeScao/orgs",
"repos_url": "https://api.github.com/users/TevenLeScao/repos",
"events_url": "https://api.github.com/users/TevenLeScao/events{/privacy}",
"received_events_url": "https://api.github.com/users/TevenLeScao/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Couln't reproduce on my side :/ \r\nlet me know if you manage to reproduce on another env (colab for example)",
"Fixed with a clean re-install!"
] | 2020-09-07T10:23:50 | 2020-09-07T15:19:09 | 2020-09-07T14:26:41 |
CONTRIBUTOR
| null | null | null |
`nlp` keeps creating new caches for the same file when launching `filter` from a script, and behaves correctly from within the shell.
Example: try running
```
import nlp
hans_easy_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 0)
hans_hard_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 1)
```
twice. If launched from a `file.py` script, the cache will be re-created the second time. If launched as 3 shell/`ipython` commands, `nlp` will correctly re-use the cache.
As observed with @lhoestq.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/580/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/580/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/577
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/577/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/577/comments
|
https://api.github.com/repos/huggingface/datasets/issues/577/events
|
https://github.com/huggingface/datasets/issues/577
| 694,607,148 |
MDU6SXNzdWU2OTQ2MDcxNDg=
| 577 |
Some languages in wikipedia dataset are not loading
|
{
"login": "gaguilar",
"id": 5833357,
"node_id": "MDQ6VXNlcjU4MzMzNTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/5833357?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/gaguilar",
"html_url": "https://github.com/gaguilar",
"followers_url": "https://api.github.com/users/gaguilar/followers",
"following_url": "https://api.github.com/users/gaguilar/following{/other_user}",
"gists_url": "https://api.github.com/users/gaguilar/gists{/gist_id}",
"starred_url": "https://api.github.com/users/gaguilar/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/gaguilar/subscriptions",
"organizations_url": "https://api.github.com/users/gaguilar/orgs",
"repos_url": "https://api.github.com/users/gaguilar/repos",
"events_url": "https://api.github.com/users/gaguilar/events{/privacy}",
"received_events_url": "https://api.github.com/users/gaguilar/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Some wikipedia languages have already been processed by us and are hosted on our google storage. This is the case for \"fr\" and \"en\" for example.\r\n\r\nFor other smaller languages (in terms of bytes), they are directly downloaded and parsed from the wikipedia dump site.\r\nParsing can take some time for languages with hundreds of MB of xml.\r\n\r\nLet me know if you encounter an error or if you feel that is is taking too long for you.\r\nWe could process those that really take too much time",
"Ok, thanks for clarifying, that makes sense. I will time those examples later today and post back here.\r\n\r\nAlso, it seems that not all dumps should use the same date. For instance, I was checking the Spanish dump doing the following:\r\n```\r\ndata = nlp.load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner', split='train')\r\n```\r\n\r\nI got the error below because this URL does not exist: https://dumps.wikimedia.org/eswiki/20200501/dumpstatus.json. So I checked the actual available dates here https://dumps.wikimedia.org/eswiki/ and there is no 20200501. If one tries for a date available in the link, then the nlp library does not allow such a request because is not in the list of expected datasets.\r\n\r\n```\r\nDownloading and preparing dataset wikipedia/20200501.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguilar/.cache/huggingface/datasets/wikipedia/20200501.es/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/load.py\", line 548, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 462, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 965, in _download_and_prepare\r\n super(BeamBasedBuilder, self)._download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 518, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 422, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 220, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 155, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 163, in map_nested\r\n return {\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 164, in <dictcomp>\r\n k: map_nested(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 191, in map_nested\r\n return function(data_struct)\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 156, in <lambda>\r\n lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 191, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 356, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://dumps.wikimedia.org/eswiki/20200501/dumpstatus.json\r\n```",
"Thanks ! This will be very helpful.\r\n\r\nAbout the date issue, I think it's possible to use another date with\r\n\r\n```python\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n```\r\n\r\nHowever we've not processed wikipedia dumps for other dates than 20200501 (yet ?)\r\n\r\nOne more thing that is specific to 20200501.es: it was available once but the `mwparserfromhell` was not able to parse it for some reason, so we didn't manage to get a processed version of 20200501.es (see #321 )",
"Cool! Thanks for the trick regarding different dates!\r\n\r\nI checked the download/processing time for retrieving the Arabic Wikipedia dump, and it took about 3.2 hours. I think that this may be a bit impractical when it comes to working with multiple languages (although I understand that storing those datasets in your Google storage may not be very appealing either). \r\n\r\nFor the record, here's what I did:\r\n```python\r\nimport nlp\r\nimport time\r\n\r\ndef timeit(filename):\r\n elapsed = time.time()\r\n data = nlp.load_dataset('wikipedia', filename, beam_runner='DirectRunner', split='train')\r\n elapsed = time.time() - elapsed\r\n print(f\"Loading the '{filename}' data took {elapsed:,.1f} seconds...\")\r\n return data\r\n\r\ndata = timeit('20200501.ar')\r\n```\r\n\r\nHere's the output:\r\n```\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13.0k/13.0k [00:00<00:00, 8.34MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28.7k/28.7k [00:00<00:00, 954kB/s]\r\nDownloading and preparing dataset wikipedia/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguil20/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47.4k/47.4k [00:00<00:00, 1.40MB/s]\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79.8M/79.8M [00:15<00:00, 5.13MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 171M/171M [00:33<00:00, 5.13MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 103M/103M [00:20<00:00, 5.14MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 227M/227M [00:44<00:00, 5.06MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 140M/140M [00:28<00:00, 4.96MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 160M/160M [00:30<00:00, 5.20MB/s]\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 97.5M/97.5M [00:19<00:00, 5.06MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222M/222M [00:42<00:00, 5.21MB/s]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [03:16<00:00, 196.39s/sources]\r\nDataset wikipedia downloaded and prepared to /home/gaguil20/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50. Subsequent calls will reuse this data.\r\nLoading the '20200501.ar' data took 11,582.7 seconds...\r\n````",
"> About the date issue, I think it's possible to use another date with\r\n> ```python\r\n> load_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n> ```\r\n\r\nI tried your suggestion about the date and the function does not accept the language and date keywords. I tried both on `nlp` v0.4 and the new `datasets` library (v1.0.2):\r\n```\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"20200601\", beam_runner='DirectRunner', split='train')\r\n```\r\nFor now, my quick workaround to keep things moving was to simply change the date inside the library at this line: [https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py#L403](https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py#L403)\r\n\r\nNote that the date and languages are valid: [https://dumps.wikimedia.org/eswiki/20200601/dumpstatus.json](https://dumps.wikimedia.org/eswiki/20200601/dumpstatus.json)\r\n\r\nAny suggestion is welcome :) @lhoestq \r\n\r\n\r\n## **[UPDATE]**\r\n\r\nThe workaround I mentioned fetched the data, but then I faced another issue (even the log says to report this as bug):\r\n```\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\n```\r\n\r\nHere's the full stack (which says that there is a key error caused by this key: `KeyError: '000nbsp'`):\r\n\r\n```Downloading and preparing dataset wikipedia/20200601.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gustavoag/.cache/huggingface/datasets/wikipedia/20200601.es/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 74.7k/74.7k [00:00<00:00, 1.53MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 232M/232M [00:48<00:00, 4.75MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 442M/442M [01:39<00:00, 4.44MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 173M/173M [00:33<00:00, 5.12MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 344M/344M [01:14<00:00, 4.59MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 541M/541M [01:59<00:00, 4.52MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 476M/476M [01:31<00:00, 5.18MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 545M/545M [02:02<00:00, 4.46MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 299M/299M [01:01<00:00, 4.89MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.60M/9.60M [00:01<00:00, 4.84MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 423M/423M [01:36<00:00, 4.38MB/s]\r\nWARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['--lang', 'es', '--date', '20200601', '--tokenizer', 'bert-base-multilingual-cased', '--cache', 'train', 'valid', '--max_dataset_length', '200000', '10000']\r\n\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nTraceback (most recent call last):\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 500, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/wikicode.py\", line 643, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 63, in __strip__\r\n return self.normalize()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 178, in normalize\r\n return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"/raid/data/gustavoag/projects/char2subword/research/preprocessing/split_wiki.py\", line 96, in <module>\r\n main()\r\n File \"/raid/data/gustavoag/projects/char2subword/research/preprocessing/split_wiki.py\", line 65, in main\r\n data = nlp.load_dataset('wikipedia', f'{args.date}.{args.lang}', beam_runner='DirectRunner', split='train')\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/load.py\", line 548, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 462, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 969, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/pipeline.py\", line 534, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\", line 119, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 172, in run_pipeline\r\n self._latest_run_result = self.run_via_runner_api(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 183, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 338, in run_stages\r\n stage_results = self._run_stage(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 512, in _run_stage\r\n last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 556, in _run_bundle\r\n result, splits = bundle_manager.process_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 940, in process_bundle\r\n for result, split_result in executor.map(execute, zip(part_inputs, # pylint: disable=zip-builtin-not-iterating\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 611, in result_iterator\r\n yield fs.pop().result()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 439, in result\r\n return self.__get_result()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 388, in __get_result\r\n raise self._exception\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/utils/thread_pool_executor.py\", line 44, in run\r\n self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 932, in execute\r\n return bundle_manager.process_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 837, in process_bundle\r\n result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py\", line 352, in push\r\n response = self.worker.do_instruction(request)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 479, in do_instruction\r\n return getattr(self, request_type)(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 515, in process_bundle\r\n bundle_processor.process_bundle(instruction_id))\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 977, in process_bundle\r\n input_op_by_transform_id[element.transform_id].process_encoded(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 218, in process_encoded\r\n self.output(decoded_value)\r\n File \"apache_beam/runners/worker/operations.py\", line 330, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 332, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1045, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/future/utils/__init__.py\", line 446, in raise_with_traceback\r\n raise exc.with_traceback(traceback)\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 500, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/wikicode.py\", line 643, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 63, in __strip__\r\n return self.normalize()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 178, in normalize\r\n return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"```",
"@lhoestq Any updates on this? I have similar issues with the Romanian dump, tnx.",
"Hey @gaguilar ,\r\n\r\nI just found the [\"char2subword\" paper](https://arxiv.org/pdf/2010.12730.pdf) and I'm really interested in trying it out on own vocabs/datasets like for historical texts (I've already [trained some lms](https://github.com/stefan-it/europeana-bert) on newspaper articles with OCR errors).\r\n\r\nDo you plan to release the code for your paper or is it possible to get the implementation 🤔 Many thanks :hugs: ",
"Hi @stefan-it! Thanks for your interest in our work! We do plan to release the code, but we will make it available once the paper has been published at a conference. Sorry for the inconvenience!\r\n\r\nHi @lhoestq, do you have any insights for this issue by any chance? Thanks!",
"This is an issue on the `mwparserfromhell` side. You could try to update `mwparserfromhell` and see if it fixes the issue. If it doesn't we'll have to create an issue on their repo for them to fix it.\r\nBut first let's see if the latest version of `mwparserfromhell` does the job.",
"I think the work around as suggested in the issue [#886] is not working for several languages, such as `id`. For example, I tried all the dates to download dataset for `id` langauge from the following link: (https://github.com/huggingface/datasets/pull/886) [https://dumps.wikimedia.org/idwiki/](https://dumps.wikimedia.org/idwiki/ )\r\n\r\n> >>> dataset = load_dataset('wikipedia', language='id', date=\"20210501\", beam_runner='DirectRunner')\r\nWARNING:datasets.builder:Using custom data configuration 20210501.id-date=20210501,language=id\r\nDownloading and preparing dataset wikipedia/20210501.id (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/.cache/huggingface/datasets/wikipedia/20210501.id-date=20210501,language=id/0.0.0/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/load.py\", line 745, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 1139, in _download_and_prepare\r\n super(BeamBasedBuilder, self)._download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 630, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/Users/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 420, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 287, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 218, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 281, in cached_path\r\n output_path = get_from_cache(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 623, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://dumps.wikimedia.org/idwiki/20210501/dumpstatus.json\r\n\r\nMoreover the downloading speed for `non-en` language is very very slow. And interestingly the download stopped after approx a couple minutes due to the read time-out. I tried numerous times and the results is same. Is there any feasible way to download non-en language using huggingface?\r\n\r\n> File \"/Users/miislamg/opt/anaconda3/envs/proj-semlm/lib/python3.9/site-packages/requests/models.py\", line 760, in generate\r\n raise ConnectionError(e)\r\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='dumps.wikimedia.org', port=443): Read timed out.\r\nDownloading: 7%|████████▎ | 10.2M/153M [03:35<50:07, 47.4kB/s]",
"Hi ! The link https://dumps.wikimedia.org/idwiki/20210501/dumpstatus.json seems to be working fine for me.\r\n\r\nRegarding the time outs, it must come either from an issue on the wikimedia host side, or from your internet connection.\r\nFeel free to try again several times.",
"I was trying to download dataset for `es` language, however I am getting the following error:\r\n```\r\ndataset = load_dataset('wikipedia', language='es', date=\"20210320\", beam_runner='DirectRunner') \r\n```\r\n\r\n```\r\nDownloading and preparing dataset wikipedia/20210320.es (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /scratch/user_name/datasets/wikipedia/20210320.es-date=20210320,language=es/0.0.0/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 492, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/wikicode.py\", line 639, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 60, in __strip__\r\n return self.normalize()\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 150, in normalize\r\n return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"download_dataset_all.py\", line 8, in <module>\r\n dataset = load_dataset('wikipedia', language=language, date=\"20210320\", beam_runner='DirectRunner') \r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/load.py\", line 748, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/builder.py\", line 575, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/builder.py\", line 1152, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/pipeline.py\", line 564, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/direct/direct_runner.py\", line 131, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 190, in run_pipeline\r\n pipeline.to_runner_api(default_environment=self._default_environment))\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 200, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 366, in run_stages\r\n bundle_context_manager,\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 562, in _run_stage\r\n bundle_manager)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 602, in _run_bundle\r\n data_input, data_output, input_timers, expected_timer_output)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 903, in process_bundle\r\n result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py\", line 378, in push\r\n response = self.worker.do_instruction(request)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 610, in do_instruction\r\n getattr(request, request_type), request.instruction_id)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 647, in process_bundle\r\n bundle_processor.process_bundle(instruction_id))\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 1001, in process_bundle\r\n element.data)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 229, in process_encoded\r\n self.output(decoded_value)\r\n File \"apache_beam/runners/worker/operations.py\", line 356, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 358, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1315, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"/opt/conda/lib/python3.7/site-packages/future/utils/__init__.py\", line 446, in raise_with_traceback\r\n raise exc.with_traceback(traceback)\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 492, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/wikicode.py\", line 639, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 60, in __strip__\r\n return self.normalize()\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 150, in normalize\r\n return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"\r\n```",
"Hi ! This looks related to this issue: https://github.com/huggingface/datasets/issues/1994\r\nBasically the parser that is used (mwparserfromhell) has some issues for some pages in `es`.\r\nWe already reported some issues for `es` on their repo at https://github.com/earwig/mwparserfromhell/issues/247 but it looks like there are still a few issues. Might be a good idea to open a new issue on the mwparserfromhell repo",
"Any updates on this so far?",
"The issue:\r\n```\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"\r\n```\r\nreported in comments:\r\n- https://github.com/huggingface/datasets/issues/577#issuecomment-701890059 (by @gaguilar)\r\n- https://github.com/huggingface/datasets/issues/577#issuecomment-879513227 (by @mmiakashs)\r\n\r\nwas normally fixed in the `mwparserfromhell` library and will be accessible in their next release version `0.7`:\r\n- https://github.com/earwig/mwparserfromhell/issues/288",
"mwparserfromhell 0.7 has still not been released, but you might have luck with the dev version:\r\n`pip install git+https://github.com/earwig/mwparserfromhell.git@0f89f44`"
] | 2020-09-07T01:16:29 | 2023-04-11T22:50:48 | 2022-10-11T11:16:04 |
CONTRIBUTOR
| null | null | null |
Hi,
I am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:
```
import nlp
langs = ['ar'. 'af', 'an']
for lang in langs:
data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train')
print(lang, len(data))
```
Here's what I see for 'ar' (it gets stuck there):
```
Downloading and preparing dataset wikipedia/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguilar/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...
```
Note that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/577/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/577/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/575
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/575/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/575/comments
|
https://api.github.com/repos/huggingface/datasets/issues/575/events
|
https://github.com/huggingface/datasets/issues/575
| 693,691,611 |
MDU6SXNzdWU2OTM2OTE2MTE=
| 575 |
Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading.
|
{
"login": "sudarshan85",
"id": 488428,
"node_id": "MDQ6VXNlcjQ4ODQyOA==",
"avatar_url": "https://avatars.githubusercontent.com/u/488428?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/sudarshan85",
"html_url": "https://github.com/sudarshan85",
"followers_url": "https://api.github.com/users/sudarshan85/followers",
"following_url": "https://api.github.com/users/sudarshan85/following{/other_user}",
"gists_url": "https://api.github.com/users/sudarshan85/gists{/gist_id}",
"starred_url": "https://api.github.com/users/sudarshan85/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/sudarshan85/subscriptions",
"organizations_url": "https://api.github.com/users/sudarshan85/orgs",
"repos_url": "https://api.github.com/users/sudarshan85/repos",
"events_url": "https://api.github.com/users/sudarshan85/events{/privacy}",
"received_events_url": "https://api.github.com/users/sudarshan85/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Update:\r\n\r\nThe imdb download completed after a long time (about 45 mins). Ofcourse once download loading was instantaneous. Also, the loaded object was of type `arrow_dataset`. \r\n\r\nThe urls for glue still doesn't work though.",
"Thanks for the report, I'll give a look!",
"I am also seeing a similar error when running the following:\r\n\r\n```\r\nimport nlp\r\ndataset = load_dataset('cola')\r\n```\r\nError:\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/load.py\", line 509, in load_dataset\r\n module_path = prepare_module(path, download_config=download_config, dataset=True)\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/load.py\", line 248, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 191, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 356, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cola/cola.py\r\n```",
"@jeswan `\"cola\"` is not a valid dataset identifier (you can check the up-to-date list on https://huggingface.co/datasets) but you can find cola inside glue.",
"Ah right. Thanks!",
"Hi. Closing this one since #626 updated the glue urls.\r\n\r\n> 1. Why is it still blocking? Is it still downloading?\r\n\r\nAfter downloading it generates the arrow file by iterating through the examples.\r\nThe number of examples processed by second is shown during the processing (not sure why it was not the case for you)\r\n\r\n> 2. I specified split as train, so why is the test folder being populated?\r\n\r\nIt downloads every split\r\n\r\n\r\n\r\n"
] | 2020-09-04T21:46:25 | 2020-09-22T10:41:36 | 2020-09-22T10:41:36 |
NONE
| null | null | null |
Hi,
I'm following the [quick tour](https://huggingface.co/nlp/quicktour.html) and tried to load the glue dataset:
```
>>> from nlp import load_dataset
>>> dataset = load_dataset('glue', 'mrpc', split='train')
```
However, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):
```
/net/vaosl01/opt/NFS/su0/miniconda3/envs/hf/lib/python3.7/site-packages/nlp/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)
354 " to False."
355 )
--> 356 raise ConnectionError("Couldn't reach {}".format(url))
357
358 # From now on, connected is True.
ConnectionError: Couldn't reach https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc
```
I tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.
Since this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:
```
ds = load_dataset('imdb', split='train')
```
This downloads the data, but it just blocks after that:
```
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.56k/4.56k [00:00<00:00, 1.38MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.07k/2.07k [00:00<00:00, 1.15MB/s]
Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to /net/vaosl01/opt/NFS/su0/huggingface/datasets/imdb/plain_text/1.0.0/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 84.1M/84.1M [00:07<00:00, 11.1MB/s]
```
I checked the folder `$HF_HOME/datasets/downloads/extracted/<id>/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:
1. Why is it still blocking? Is it still downloading?
2. I specified split as train, so why is the test folder being populated?
3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?
Thanks.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/575/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/575/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/568
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/568/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/568/comments
|
https://api.github.com/repos/huggingface/datasets/issues/568/events
|
https://github.com/huggingface/datasets/issues/568
| 691,638,656 |
MDU6SXNzdWU2OTE2Mzg2NTY=
| 568 |
`metric.compute` throws `ArrowInvalid` error
|
{
"login": "ibeltagy",
"id": 2287797,
"node_id": "MDQ6VXNlcjIyODc3OTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/2287797?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ibeltagy",
"html_url": "https://github.com/ibeltagy",
"followers_url": "https://api.github.com/users/ibeltagy/followers",
"following_url": "https://api.github.com/users/ibeltagy/following{/other_user}",
"gists_url": "https://api.github.com/users/ibeltagy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ibeltagy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ibeltagy/subscriptions",
"organizations_url": "https://api.github.com/users/ibeltagy/orgs",
"repos_url": "https://api.github.com/users/ibeltagy/repos",
"events_url": "https://api.github.com/users/ibeltagy/events{/privacy}",
"received_events_url": "https://api.github.com/users/ibeltagy/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hmm might be related to what we are solving in #564",
"Could you try to update to `datasets>=1.0.0` (we changed the name of the library) and try again ?\r\nIf is was related to the distributed setup settings it must be fixed.\r\nIf it was related to empty metric inputs it's going to be fixed in #654 ",
"Closing this one as it was fixed in #654 \r\nFeel free to re-open if you have other questions"
] | 2020-09-03T04:56:57 | 2020-10-05T16:33:53 | 2020-10-05T16:33:53 |
NONE
| null | null | null |
I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`
```
File "/home/beltagy/trainer.py", line 92, in validation_step
rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 224, in compute
self.finalize(timeout=timeout)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 213, in finalize
self.data = Dataset(**reader.read_files(node_files))
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 217, in read_files
dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 162, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 276, in _get_dataset_from_filename
f = pa.ipc.open_stream(mmap)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 173, in open_stream
return RecordBatchStreamReader(source)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 64, in __init__
self._open(source)
File "pyarrow/ipc.pxi", line 469, in pyarrow.lib._RecordBatchStreamReader._open
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/568/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/568/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/565
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/565/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/565/comments
|
https://api.github.com/repos/huggingface/datasets/issues/565/events
|
https://github.com/huggingface/datasets/issues/565
| 691,039,121 |
MDU6SXNzdWU2OTEwMzkxMjE=
| 565 |
No module named 'nlp.logging'
|
{
"login": "melody-ju",
"id": 66633754,
"node_id": "MDQ6VXNlcjY2NjMzNzU0",
"avatar_url": "https://avatars.githubusercontent.com/u/66633754?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/melody-ju",
"html_url": "https://github.com/melody-ju",
"followers_url": "https://api.github.com/users/melody-ju/followers",
"following_url": "https://api.github.com/users/melody-ju/following{/other_user}",
"gists_url": "https://api.github.com/users/melody-ju/gists{/gist_id}",
"starred_url": "https://api.github.com/users/melody-ju/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/melody-ju/subscriptions",
"organizations_url": "https://api.github.com/users/melody-ju/orgs",
"repos_url": "https://api.github.com/users/melody-ju/repos",
"events_url": "https://api.github.com/users/melody-ju/events{/privacy}",
"received_events_url": "https://api.github.com/users/melody-ju/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Thanks for reporting.\r\n\r\nApparently this is a versioning issue: the lib downloaded the `bleurt` script from the master branch where we did this change recently. We'll fix that in a new release this week or early next week. Cc @thomwolf \r\n\r\nUntil that, I'd suggest you to download the right bleurt folder from github ([this one](https://github.com/huggingface/nlp/tree/0.4.0/metrics/bleurt)) and do\r\n\r\n```python\r\nfrom nlp import load_metric\r\n\r\nbleurt = load_metric(\"path/to/bleurt/folder\")\r\n```\r\n\r\nTo download it you can either clone the repo or download the `bleurt.py` file and place it in a folder named `bleurt` ",
"Actually we can fix this on our side, this script didn't had to be updated. I'll do it in a few minutes"
] | 2020-09-02T13:49:50 | 2020-09-03T07:29:50 | 2020-09-03T07:29:50 |
NONE
| null | null | null |
Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing?
```
>>> import nlp
2020-09-02 13:47:09.210310: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
>>> bleurt = nlp.load_metric("bleurt")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 443, in load_metric
metric_cls = import_main_class(module_path, dataset=False)
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 61, in import_main_class
module = importlib.import_module(module_path)
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 994, in _gcd_import
File "<frozen importlib._bootstrap>", line 971, in _find_and_load
File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 678, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/metrics/bleurt/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5/bleurt.py", line 20, in <module>
from nlp.logging import get_logger
ModuleNotFoundError: No module named 'nlp.logging'
```
Just to show once again that I can't import the logging module:
```
>>> import nlp
2020-09-02 13:48:38.190621: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
>>> nlp.__version__
'0.4.0'
>>> from nlp.logging import get_logger
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'nlp.logging'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/565/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/565/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/560
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/560/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/560/comments
|
https://api.github.com/repos/huggingface/datasets/issues/560/events
|
https://github.com/huggingface/datasets/issues/560
| 690,488,764 |
MDU6SXNzdWU2OTA0ODg3NjQ=
| 560 |
Using custom DownloadConfig results in an error
|
{
"login": "ynouri",
"id": 1789921,
"node_id": "MDQ6VXNlcjE3ODk5MjE=",
"avatar_url": "https://avatars.githubusercontent.com/u/1789921?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ynouri",
"html_url": "https://github.com/ynouri",
"followers_url": "https://api.github.com/users/ynouri/followers",
"following_url": "https://api.github.com/users/ynouri/following{/other_user}",
"gists_url": "https://api.github.com/users/ynouri/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ynouri/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ynouri/subscriptions",
"organizations_url": "https://api.github.com/users/ynouri/orgs",
"repos_url": "https://api.github.com/users/ynouri/repos",
"events_url": "https://api.github.com/users/ynouri/events{/privacy}",
"received_events_url": "https://api.github.com/users/ynouri/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"From my limited understanding, part of the issue seems related to the `prepare_module` and `download_and_prepare` functions each handling the case where no config is passed. For example, `prepare_module` does mutate the object passed and forces the flags `extract_compressed_file` and `force_extract` to `True`.\r\n\r\nSee:\r\n* https://github.com/huggingface/nlp/blob/5fb61e1012bda724a9b6b847307d90a1380abfa5/src/nlp/load.py#L227\r\n* https://github.com/huggingface/nlp/blob/5fb61e1012bda724a9b6b847307d90a1380abfa5/src/nlp/builder.py#L388\r\n\r\nMaybe a cleaner solution would be to always instantiate a default `DownloadConfig` object at the top-level, have it as non-optional for the lower-level functions and treat it as immutable. ",
"Thanks for the report, I'll take a look.\r\n\r\nWhat is your specific use-case for providing a DownloadConfig object?\r\n",
"Thanks. Our use case involves running a training job behind a corporate firewall with no access to any external resources (S3, GCP or other web resources).\r\n\r\nI was thinking about a 2-steps process:\r\n1) Download the resources / artifacts using some secure corporate channel, ie run `nlp.load_dataset()` without a specific `DownloadConfig`. After that, collect the files from the `$HF_HOME` folder\r\n2) Copy the `$HF_HOME` folder in the firewalled environment. Run `nlp.load_dataset()` with a custom config `DownloadConfig(local_files_only=True)`\r\n\r\nHowever this ends up a bit clunky in practice, even when solving the `DownloadConfig` issue above. For example, the `filename` hash computed in `get_from_cache()` differs in the `local_files_only=False` vs `local_files_only=True` case (local case defaults `etag` to `None`, which results in a different hash). So effectively step 2) above doesn't work because the hash computed differs from the hash in the cache folder. Some hacks / workaround are possible but this solution becomes very convoluted.\r\nhttps://github.com/huggingface/nlp/blob/c214aa5a4430c1df1bcd0619fd94d6abdf9d2da7/src/nlp/utils/file_utils.py#L417\r\n\r\nWould you recommend a different path?\r\n",
"I see.\r\n\r\nProbably the easiest way for you would be that we add simple serialization/deserialization methods to the Dataset and DatasetDict objects once the data files have been downloaded and all the dataset is processed.\r\n\r\nWhat do you think @lhoestq ?",
"This use-case will be solved with #571 ",
"Thank you very much @thomwolf and @lhoestq we will give it a try"
] | 2020-09-01T22:23:02 | 2022-10-04T17:23:45 | 2022-10-04T17:23:45 |
NONE
| null | null | null |
## Version / Environment
Ubuntu 18.04
Python 3.6.8
nlp 0.4.0
## Description
Loading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.
## How to reproduce
### Example without DownloadConfig --> works
```python
import os
os.environ["HF_HOME"] = "/data/hf-test-without-dl-config-01/"
import logging
import nlp
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
imdb = nlp.load_dataset(path="imdb")
```
### Example with DownloadConfig --> doesn't work
```python
import os
os.environ["HF_HOME"] = "/data/hf-test-with-dl-config-01/"
import logging
import nlp
from nlp.utils import DownloadConfig
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
download_config = DownloadConfig()
imdb = nlp.load_dataset(path="imdb", download_config=download_config)
```
Error traceback:
```
Traceback (most recent call last):
File "/.../example_with_dl_config.py", line 13, in <module>
imdb = nlp.load_dataset(path="imdb", download_config=download_config)
File "/.../python3.6/python3.6/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 518, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/.../python3.6/python3.6/site-packages/nlp/datasets/imdb/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743/imdb.py", line 86, in _split_generators
arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 220, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 158, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 108, in _record_sizes_checksums
self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)
File "/.../python3.6/python3.6/site-packages/nlp/utils/info_utils.py", line 79, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/data/hf-test-with-dl-config-01/datasets/extracted/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/560/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/560/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/554
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/554/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/554/comments
|
https://api.github.com/repos/huggingface/datasets/issues/554/events
|
https://github.com/huggingface/datasets/issues/554
| 690,173,214 |
MDU6SXNzdWU2OTAxNzMyMTQ=
| 554 |
nlp downloads to its module path
|
{
"login": "danieldk",
"id": 49398,
"node_id": "MDQ6VXNlcjQ5Mzk4",
"avatar_url": "https://avatars.githubusercontent.com/u/49398?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/danieldk",
"html_url": "https://github.com/danieldk",
"followers_url": "https://api.github.com/users/danieldk/followers",
"following_url": "https://api.github.com/users/danieldk/following{/other_user}",
"gists_url": "https://api.github.com/users/danieldk/gists{/gist_id}",
"starred_url": "https://api.github.com/users/danieldk/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/danieldk/subscriptions",
"organizations_url": "https://api.github.com/users/danieldk/orgs",
"repos_url": "https://api.github.com/users/danieldk/repos",
"events_url": "https://api.github.com/users/danieldk/events{/privacy}",
"received_events_url": "https://api.github.com/users/danieldk/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Indeed this is a known issue arising from the fact that we try to be compatible with cloupickle.\r\n\r\nDoes this also happen if you are installing in a virtual environment?",
"> Indeed this is a know issue with the fact that we try to be compatible with cloupickle.\r\n> \r\n> Does this also happen if you are installing in a virtual environment?\r\n\r\nThen it would work, because the package is in a writable path.",
"If it's fine for you then this is the recommended way to solve this issue.",
"> If it's fine for you then this is the recommended way to solve this issue.\r\n\r\nI don't want to use a virtual environment, because Nix is fully reproducible, and virtual environments are not. And I am the maintainer of the `transformers` in nixpkgs, so sooner or later I will have to package `nlp`, since it is becoming a dependency of `transformers` ;).",
"Ok interesting. We could have another check to see if it's possible to download and import the datasets script at another location than the module path. I think this would probably involve tweaking the python system path dynamically.\r\n\r\nI don't know anything about Nix so if you want to give this a try your self we can guide you or you can give us more information on your general project and how this works.\r\n\r\nRegarding `nlp` and `transformers`, we are not sure `nlp` will become a required dependency for `transformers`. It will probably be used a lot in the examples but I think it probably won't be a required dependency for the main package since we try to keep it as light as possible in terms of deps.\r\n\r\nHappy to help you make all these things work better for your use-case ",
"@danieldk modules are now installed in a different location (by default in the cache directory of the lib, in `~/.cache/huggingface/modules`). You can also change that using the environment variable `HF_MODULES_PATH`\r\n\r\nFeel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\nWe plan to do a release in the next coming days",
"Awesome! I’ll hopefully have some time in the coming days to try this.",
"> Feel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\n> We plan to do a release in the next coming days\r\n\r\nThanks for making this change! I just packaged the latest commit on master and it works like a charm now! :partying_face: "
] | 2020-09-01T14:06:14 | 2020-09-11T06:19:24 | 2020-09-11T06:19:24 |
NONE
| null | null | null |
I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:
```>>> import nlp
>>> squad_dataset = nlp.load_dataset('squad')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 530, in load_dataset
module_path, hash = prepare_module(path, download_config=download_config, dataset=True)
File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 329, in prepare_module
os.makedirs(main_folder_path, exist_ok=True)
File "/nix/store/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5/lib/python3.8/os.py", line 223, in makedirs
mkdir(name, mode)
OSError: [Errno 30] Read-only file system: '/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/datasets/squad'
```
Do you have any suggested workaround for this issue?
Perhaps overriding the default value for `force_local_path` of `prepare_module`?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/554/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/554/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/546
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/546/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/546/comments
|
https://api.github.com/repos/huggingface/datasets/issues/546/events
|
https://github.com/huggingface/datasets/issues/546
| 689,186,526 |
MDU6SXNzdWU2ODkxODY1MjY=
| 546 |
Very slow data loading on large dataset
|
{
"login": "agemagician",
"id": 6087313,
"node_id": "MDQ6VXNlcjYwODczMTM=",
"avatar_url": "https://avatars.githubusercontent.com/u/6087313?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/agemagician",
"html_url": "https://github.com/agemagician",
"followers_url": "https://api.github.com/users/agemagician/followers",
"following_url": "https://api.github.com/users/agemagician/following{/other_user}",
"gists_url": "https://api.github.com/users/agemagician/gists{/gist_id}",
"starred_url": "https://api.github.com/users/agemagician/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/agemagician/subscriptions",
"organizations_url": "https://api.github.com/users/agemagician/orgs",
"repos_url": "https://api.github.com/users/agemagician/repos",
"events_url": "https://api.github.com/users/agemagician/events{/privacy}",
"received_events_url": "https://api.github.com/users/agemagician/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"When you load a text file for the first time with `nlp`, the file is converted into Apache Arrow format. Arrow allows to use memory-mapping, which means that you can load an arbitrary large dataset.\r\n\r\nNote that as soon as the conversion has been done once, the next time you'll load the dataset it will be much faster.\r\n\r\nHowever for a 1TB dataset, the conversion can indeed take time. You could try to load parts of it in parallel, and then use `nlp.concatenate_datasets` to get your full dataset.",
"Humm, we can give a look at these large scale datasets indeed.\r\n\r\nDo you mind sharing a few stats on your dataset so I can try to test on a similar one?\r\n\r\nIn particular some orders of magnitudes for the number of files, number of lines per files, line lengths.",
"@lhoestq Yes, I understand that the first time requires more time. The concatenate_datasets seems to be a workaround, but I believe a multi-processing method should be integrated into load_dataset to make it easier and more efficient for users.\r\n\r\n@thomwolf Sure, here are the statistics:\r\nNumber of lines: 4.2 Billion\r\nNumber of files: 6K\r\nNumber of tokens: 800 Billion\r\nThe number of lines is distributed equally across these 6k files.\r\nThe line length varies between 100 tokens to 40k tokens.\r\n",
"@agemagician you can give a try at a multithreaded version if you want (currently on the #548).\r\n\r\nTo test it, you just need to copy the new `text` processing script which is [here](https://github.com/huggingface/nlp/blob/07d92a82b7594498ff702f3cca55c074e2052257/datasets/text/text.py) somewhere on your drive and give it's local path instead of `text` to `load_dataset`. E.g. in your example:\r\n```python\r\ntrain_files = glob.glob(\"xxx/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('./datasets/text.py', # path to where you've dowloaded the multi-threaded text loading script\r\n data_files=train_files,\r\n name=\"customDataset\",\r\n version=\"1.0.0\",\r\n cache_dir=\"xxx/nlp\")\r\n```",
"I have already generated the dataset, but now I tried to reload it and it is still very slow.\r\n\r\nI also have installed your commit and it is slow, even after the dataset was already generated.\r\n`pip install git+https://github.com/huggingface/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257`\r\n\r\nIt uses only a single thread.\r\n\r\nDid I miss something ?",
"As mentioned in #548 , each time you call `load_dataset` with `data_files=`, they are hashed to get the cache directory name. Hashing can be too slow with 1TB of data. I feel like we should have a faster way of getting a hash that identifies the input data files",
"I believe this is really a very important feature, otherwise, we will still have the issue of too slow loading problems even if the data cache generation is fast.",
"Hmm ok then maybe it's the hashing step indeed.\r\n\r\nLet's see if we can improve this as well.\r\n\r\n(you will very likely have to regenerate your dataset if we change this part of the lib though since I expect modifications on this part of the lib to results in new hashes)",
"Also, @agemagician you have to follow the step I indicate in my previous message [here](https://github.com/huggingface/nlp/issues/546#issuecomment-684648927) to use the new text loading script.\r\n\r\nJust doing `pip install git+https://github.com/huggingface/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257` like you did won't use the new script (they are not inside the library but hosted on our hub).",
"No problem, I will regenerate it. This will make us see if we solved both issues and now both the data generation step, as well as the hashing step, is fast.",
"Any news for the hashing ?",
"I'm working on it today :)",
"Ok so now the text files won't be hashed.\r\n\r\nI also updated #548 to include this change.\r\nLet us know if it helps @agemagician :)",
"Perfect thanks for your amazing work.",
"Right now, for caching 18Gb data, it is taking 1 hour 10 minute. Is that proper expected time? @lhoestq @agemagician \r\nIn this rate (assuming large file will caching at the same rate) caching full mC4 (27TB) requires a month (~26 days). \r\n",
"Hi ! Currently it is that slow because we haven't implemented parallelism for the dataset generation yet.\r\nThough we will definitely work on this :)\r\n\r\nFor now I'd recommend loading the dataset shard by shard in parallel, and then concatenate them:\r\n```python\r\n# in one process, load first 100 files for english\r\nshard1 = load_dataset(\"allenai/c4\", data_files=\"multilingual/c4-en.tfrecord-000**.json.gz\")\r\n# in another process load next 100 files for english\r\nshard2 = load_dataset(\"allenai/c4\", data_files=\"multilingual/c4-en.tfrecord-001**.json.gz\")\r\n\r\n# finally\r\nconcatenate_datasets([shard1, shard2, ...])",
"Thanks for the help..!!!",
"Sorry to write on a closed issue but, has there been any progress on parallelizing the `load_dataset` function?",
"Hi ! No but this is in our plans (probably a few weeks)",
"I'm literally crying waiting for the trainer to restart from checkpoint. It's getting stuck at `get_train_dataloader` and I think this is to do with the same issue... has there been any progress on this?",
"> I'm literally crying waiting for the trainer to restart from checkpoint. It's getting stuck at get_train_dataloader and I think this is to do with the same issue...\r\n\r\nOnce the dataset is cached once, it's not regenerated again. Your issue seems different",
"hmmm, yes. I'll come back with details on this, fairly easy to reproduce. Takes about 30 minutes to get from checkpoint loading to starting training...",
"@lhoestq yo, any news in making it possible to download large datasets faster?",
"@lhoestq For some reason [setting num_proc](https://discuss.huggingface.co/t/how-can-i-multithreadedly-download-a-huggingface-dataset/56178/2?u=kopyl) does not work at all... My dataset has 58 parquet files and i was hoping passing `num_proc` to `load_dataset` would spawn 58 Python processes each downloading its own parquet so I can load my dataset in 1 minutes instead of 50...",
"It does spawn `num_proc` processes. Note that when you download in parallel you're often bounded by your bandwidth at one point, so 50 processes is unlikely to get you a x50 download speed up but a bit less",
"> It does spawn `num_proc` processes. Note that when you download in parallel you're often bounded by your bandwidth at one point, so 50 processes is unlikely to get you a x50 download speed up but a bit less\r\n\r\nwhy then i see only 1 parquet file download progress bar at a time?",
"Ah indeed parallel downloads are not enabled yet for some datasets.\r\nI opened https://github.com/huggingface/datasets/pull/6551 to fix this.\r\n",
"@lhoestq thank you very much, i though for a second i'm just tripping."
] | 2020-08-31T12:57:23 | 2024-01-02T20:26:24 | 2020-09-08T10:19:57 |
NONE
| null | null | null |
I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.
It has been 8 hours and still, it is on the loading steps.
It does work when the text dataset size is small about 1 GB, but it doesn't scale.
It also uses a single thread during the data loading step.
```
train_files = glob.glob("xxx/*.txt",recursive=True)
random.shuffle(train_files)
print(train_files)
dataset = nlp.load_dataset('text',
data_files=train_files,
name="customDataset",
version="1.0.0",
cache_dir="xxx/nlp")
```
Is there something that I am missing ?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/546/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/546/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/545
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/545/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/545/comments
|
https://api.github.com/repos/huggingface/datasets/issues/545/events
|
https://github.com/huggingface/datasets/issues/545
| 689,138,878 |
MDU6SXNzdWU2ODkxMzg4Nzg=
| 545 |
New release coming up for this library
|
{
"login": "thomwolf",
"id": 7353373,
"node_id": "MDQ6VXNlcjczNTMzNzM=",
"avatar_url": "https://avatars.githubusercontent.com/u/7353373?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/thomwolf",
"html_url": "https://github.com/thomwolf",
"followers_url": "https://api.github.com/users/thomwolf/followers",
"following_url": "https://api.github.com/users/thomwolf/following{/other_user}",
"gists_url": "https://api.github.com/users/thomwolf/gists{/gist_id}",
"starred_url": "https://api.github.com/users/thomwolf/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/thomwolf/subscriptions",
"organizations_url": "https://api.github.com/users/thomwolf/orgs",
"repos_url": "https://api.github.com/users/thomwolf/repos",
"events_url": "https://api.github.com/users/thomwolf/events{/privacy}",
"received_events_url": "https://api.github.com/users/thomwolf/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Update: release is planed mid-next week."
] | 2020-08-31T11:37:38 | 2021-01-13T10:59:04 | 2021-01-13T10:59:04 |
MEMBER
| null | null | null |
Hi all,
A few words on the roadmap for this library.
The next release will be a big one and is planed at the end of this week.
In addition to the support for indexed datasets (useful for non-parametric models like REALM, RAG, DPR, knn-LM and many other fast dataset retrieval technics), it will:
- have support for multi-modal datasets
- include various significant improvements on speed for standard processing (map, shuffling, ...)
- have a better support for metrics (better caching, and a robust API) and a bigger focus on reproductibility
- change the name to the final name (voted by the community): `datasets`
- be the 1.0.0 release as we think the API will be mostly stabilized from now on
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/545/reactions",
"total_count": 4,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 4,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/545/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/543
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/543/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/543/comments
|
https://api.github.com/repos/huggingface/datasets/issues/543/events
|
https://github.com/huggingface/datasets/issues/543
| 688,644,407 |
MDU6SXNzdWU2ODg2NDQ0MDc=
| 543 |
nlp.load_dataset is not safe for multi processes when loading from local files
|
{
"login": "luyug",
"id": 55288513,
"node_id": "MDQ6VXNlcjU1Mjg4NTEz",
"avatar_url": "https://avatars.githubusercontent.com/u/55288513?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/luyug",
"html_url": "https://github.com/luyug",
"followers_url": "https://api.github.com/users/luyug/followers",
"following_url": "https://api.github.com/users/luyug/following{/other_user}",
"gists_url": "https://api.github.com/users/luyug/gists{/gist_id}",
"starred_url": "https://api.github.com/users/luyug/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/luyug/subscriptions",
"organizations_url": "https://api.github.com/users/luyug/orgs",
"repos_url": "https://api.github.com/users/luyug/repos",
"events_url": "https://api.github.com/users/luyug/events{/privacy}",
"received_events_url": "https://api.github.com/users/luyug/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I'll take a look!"
] | 2020-08-30T03:20:34 | 2020-08-31T11:15:10 | 2020-08-31T11:15:10 |
NONE
| null | null | null |
Loading from local files, e.g., `dataset = nlp.load_dataset('csv', data_files=['file_1.csv', 'file_2.csv'])`
concurrently from multiple processes, will raise `FileExistsError` from builder's line 430, https://github.com/huggingface/nlp/blob/6655008c738cb613c522deb3bd18e35a67b2a7e5/src/nlp/builder.py#L423-L438
Likely because multiple processes step into download_and_prepare, https://github.com/huggingface/nlp/blob/6655008c738cb613c522deb3bd18e35a67b2a7e5/src/nlp/load.py#L550-L554
This can happen when launching distributed training with commands like `python -m torch.distributed.launch --nproc_per_node 4` on a new collection of files never loaded before.
I can create a PR that puts in some file locks. It would be helpful if I can be informed of the convention for naming and placement of the lock.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/543/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/543/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/541
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/541/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/541/comments
|
https://api.github.com/repos/huggingface/datasets/issues/541/events
|
https://github.com/huggingface/datasets/issues/541
| 688,521,224 |
MDU6SXNzdWU2ODg1MjEyMjQ=
| 541 |
Best practices for training tokenizers with nlp
|
{
"login": "moskomule",
"id": 11806234,
"node_id": "MDQ6VXNlcjExODA2MjM0",
"avatar_url": "https://avatars.githubusercontent.com/u/11806234?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/moskomule",
"html_url": "https://github.com/moskomule",
"followers_url": "https://api.github.com/users/moskomule/followers",
"following_url": "https://api.github.com/users/moskomule/following{/other_user}",
"gists_url": "https://api.github.com/users/moskomule/gists{/gist_id}",
"starred_url": "https://api.github.com/users/moskomule/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/moskomule/subscriptions",
"organizations_url": "https://api.github.com/users/moskomule/orgs",
"repos_url": "https://api.github.com/users/moskomule/repos",
"events_url": "https://api.github.com/users/moskomule/events{/privacy}",
"received_events_url": "https://api.github.com/users/moskomule/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Docs that explain how to train a tokenizer with `datasets` are available here: https://huggingface.co/docs/tokenizers/training_from_memory#using-the-datasets-library"
] | 2020-08-29T12:06:49 | 2022-10-04T17:28:04 | 2022-10-04T17:28:04 |
NONE
| null | null | null |
Hi, thank you for developing this library.
What do you think are the best practices for training tokenizers using `nlp`? In the document and examples, I could only find pre-trained tokenizers used.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/541/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/541/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/539
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/539/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/539/comments
|
https://api.github.com/repos/huggingface/datasets/issues/539/events
|
https://github.com/huggingface/datasets/issues/539
| 688,323,602 |
MDU6SXNzdWU2ODgzMjM2MDI=
| 539 |
[Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data
|
{
"login": "gaguilar",
"id": 5833357,
"node_id": "MDQ6VXNlcjU4MzMzNTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/5833357?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/gaguilar",
"html_url": "https://github.com/gaguilar",
"followers_url": "https://api.github.com/users/gaguilar/followers",
"following_url": "https://api.github.com/users/gaguilar/following{/other_user}",
"gists_url": "https://api.github.com/users/gaguilar/gists{/gist_id}",
"starred_url": "https://api.github.com/users/gaguilar/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/gaguilar/subscriptions",
"organizations_url": "https://api.github.com/users/gaguilar/orgs",
"repos_url": "https://api.github.com/users/gaguilar/repos",
"events_url": "https://api.github.com/users/gaguilar/events{/privacy}",
"received_events_url": "https://api.github.com/users/gaguilar/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi @gaguilar \r\n\r\nIf you want to take care of this, it very simple, you just need to regenerate the `dataset_infos.json` file as indicated [in the doc](https://huggingface.co/nlp/share_dataset.html#adding-metadata) by [installing from source](https://huggingface.co/nlp/installation.html#installing-from-source) and running the following command from the root of the repo:\r\n```bash\r\npython nlp-cli test ./datasets/lince --save_infos --all_configs\r\n```\r\nAnd then you can open a pull-request with the updated json file.\r\n\r\nOtherwise we'll do it sometime this week.",
"Hi @thomwolf \r\n\r\nThanks for the details! I just created a PR with the updated `dataset_infos.json` file (#550).",
"Thanks for updating the json file. Closing this one"
] | 2020-08-28T19:55:51 | 2020-09-03T16:34:02 | 2020-09-03T16:34:01 |
CONTRIBUTOR
| null | null | null |
Hi,
There is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset.
How can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https://huggingface.co/nlp/viewer/?dataset=lince&config=lid_msaea):
```python
import nlp
nlp.load_dataset('lince', 'lid_msaea')
```
Output:
```
NonMatchingChecksumError: ['https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/lid_msaea.zip']
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 196, in <module>
dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp-viewer/run.py", line 150, in get
builder_instance.download_and_prepare()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
download_config.force_download = download_mode == FORCE_REDOWNLOAD
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 469, in _download_and_prepare
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 36, in verify_checksums
raise NonMatchingChecksumError(str(bad_urls))
```
Thank you in advance!
@lhoestq
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/539/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/539/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/537
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/537/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/537/comments
|
https://api.github.com/repos/huggingface/datasets/issues/537/events
|
https://github.com/huggingface/datasets/issues/537
| 687,614,699 |
MDU6SXNzdWU2ODc2MTQ2OTk=
| 537 |
[Dataset] RACE dataset Checksums error
|
{
"login": "abarbosa94",
"id": 6608232,
"node_id": "MDQ6VXNlcjY2MDgyMzI=",
"avatar_url": "https://avatars.githubusercontent.com/u/6608232?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/abarbosa94",
"html_url": "https://github.com/abarbosa94",
"followers_url": "https://api.github.com/users/abarbosa94/followers",
"following_url": "https://api.github.com/users/abarbosa94/following{/other_user}",
"gists_url": "https://api.github.com/users/abarbosa94/gists{/gist_id}",
"starred_url": "https://api.github.com/users/abarbosa94/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/abarbosa94/subscriptions",
"organizations_url": "https://api.github.com/users/abarbosa94/orgs",
"repos_url": "https://api.github.com/users/abarbosa94/repos",
"events_url": "https://api.github.com/users/abarbosa94/events{/privacy}",
"received_events_url": "https://api.github.com/users/abarbosa94/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067388877,
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug",
"name": "dataset bug",
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library"
}
] |
closed
| false | null |
[] | null |
[
"`NonMatchingChecksumError` means that the checksum of the downloaded file is not the expected one.\r\nEither the file you downloaded was corrupted along the way, or the host updated the file.\r\nCould you try to clear your cache and run `load_dataset` again ? If the error is still there, it means that there was an update in the data, and we may have to update the expected checksum value.",
"I just cleared the cache an run it again. The error persists ):\r\n\r\n```\r\n nlp (master) $ rm -rf /Users/abarbosa/.cache/huggingface/\r\n nlp (master) $ python\r\nPython 3.8.5 (default, Aug 5 2020, 03:39:04)\r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import nlp\r\n>>> dataset = nlp.load_dataset(\"race\")\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.39k/4.39k [00:00<00:00, 661kB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.81k/1.81k [00:00<00:00, 644kB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset race/default (download: 84.52 MiB, generated: 132.61 MiB, post-processed: Unknown size, total: 217.13 MiB) to /Users/abarbosa/.cache/huggingface/datasets/race/default/0.1.0/5461327f1a83549ca0d845a3159c806d2baf4f8d0d8f7d657157ce7cdf3899c2...\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25.4M/25.4M [01:03<00:00, 401kB/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/load.py\", line 550, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/builder.py\", line 471, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/builder.py\", line 530, in _download_and_prepare\r\n verify_checksums(\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/utils/info_utils.py\", line 38, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz']\r\n>>>\r\n```",
"Dealing with the same issue please update the checksum on nlp library end. The data seems to have changed on their end.",
"We have a discussion on this datasets here: https://github.com/huggingface/nlp/pull/540\r\n\r\nFeel free to participate if you have some opinion on the scope of data which should be included in this dataset.",
"At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\n",
"> At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\nCould you upload this please?",
"> > At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n> \r\n> Could you upload this please?\r\n\r\nNot sure if I can upload it according to their license (\"You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purpose, any portion of the contexts and any portion of derived data.\").",
"I managed to fix it in #540 :)",
"Closing since @540 is merged\r\n\r\nThanks again @abarbosa94 "
] | 2020-08-27T23:58:16 | 2020-09-18T12:07:04 | 2020-09-18T12:07:04 |
CONTRIBUTOR
| null | null | null |
Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:
```
dataset = nlp.load_dataset("race")
len(dataset["train"]), len(dataset["validation"])
```
But then I got the following error:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-15-8bf7603ce0ed> in <module>
----> 1 dataset = nlp.load_dataset("race")
2 len(dataset["train"]), len(dataset["validation"])
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
546
547 # Download and prepare data
--> 548 builder_instance.download_and_prepare(
549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
550 )
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
460 logger.info("Dataset not on Hf google storage. Downloading and preparing it from source")
461 if not downloaded_from_gcs:
--> 462 self._download_and_prepare(
463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
464 )
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
519 # Checksums verification
520 if verify_infos:
--> 521 verify_checksums(
522 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
523 )
~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
36 if len(bad_urls) > 0:
37 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 38 raise NonMatchingChecksumError(error_msg + str(bad_urls))
39 logger.info("All the checksums matched successfully" + for_verification_name)
40
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz']
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/537/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/537/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/534
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/534/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/534/comments
|
https://api.github.com/repos/huggingface/datasets/issues/534/events
|
https://github.com/huggingface/datasets/issues/534
| 686,115,912 |
MDU6SXNzdWU2ODYxMTU5MTI=
| 534 |
`list_datasets()` is broken.
|
{
"login": "ashutosh-dwivedi-e3502",
"id": 314169,
"node_id": "MDQ6VXNlcjMxNDE2OQ==",
"avatar_url": "https://avatars.githubusercontent.com/u/314169?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ashutosh-dwivedi-e3502",
"html_url": "https://github.com/ashutosh-dwivedi-e3502",
"followers_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/followers",
"following_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/following{/other_user}",
"gists_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/subscriptions",
"organizations_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/orgs",
"repos_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/repos",
"events_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/events{/privacy}",
"received_events_url": "https://api.github.com/users/ashutosh-dwivedi-e3502/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Thanks for reporting !\r\nThis has been fixed in #475 and the fix will be available in the next release",
"What you can do instead to get the list of the datasets is call\r\n\r\n```python\r\nprint([dataset.id for dataset in nlp.list_datasets()])\r\n```",
"Thanks @lhoestq . "
] | 2020-08-26T08:19:01 | 2020-08-27T06:31:11 | 2020-08-27T06:31:11 |
NONE
| null | null | null |
version = '0.4.0'
`list_datasets()` is broken. It results in the following error :
```
In [3]: nlp.list_datasets()
Out[3]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
375 if cls in self.type_pprinters:
376 # printer registered in self.type_pprinters
--> 377 return self.type_pprinters[cls](obj, self, cycle)
378 else:
379 # deferred printer
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in inner(obj, p, cycle)
553 p.text(',')
554 p.breakable()
--> 555 p.pretty(x)
556 if len(obj) == 1 and type(obj) is tuple:
557 # Special case for 1-item tuples.
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698 """A pprint that just redirects to the normal repr function."""
699 # Find newlines and replace them with p.break_()
--> 700 output = repr(obj)
701 lines = output.splitlines()
702 with p.group():
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/nlp/hf_api.py in __repr__(self)
110
111 def __repr__(self):
--> 112 single_line_description = self.description.replace("\n", "")
113 return f"nlp.ObjectInfo(id='{self.id}', description='{single_line_description}', files={self.siblings})"
114
AttributeError: 'NoneType' object has no attribute 'replace'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/534/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/534/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/532
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/532/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/532/comments
|
https://api.github.com/repos/huggingface/datasets/issues/532/events
|
https://github.com/huggingface/datasets/issues/532
| 685,540,614 |
MDU6SXNzdWU2ODU1NDA2MTQ=
| 532 |
File exists error when used with TPU
|
{
"login": "go-inoue",
"id": 20531705,
"node_id": "MDQ6VXNlcjIwNTMxNzA1",
"avatar_url": "https://avatars.githubusercontent.com/u/20531705?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/go-inoue",
"html_url": "https://github.com/go-inoue",
"followers_url": "https://api.github.com/users/go-inoue/followers",
"following_url": "https://api.github.com/users/go-inoue/following{/other_user}",
"gists_url": "https://api.github.com/users/go-inoue/gists{/gist_id}",
"starred_url": "https://api.github.com/users/go-inoue/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/go-inoue/subscriptions",
"organizations_url": "https://api.github.com/users/go-inoue/orgs",
"repos_url": "https://api.github.com/users/go-inoue/repos",
"events_url": "https://api.github.com/users/go-inoue/events{/privacy}",
"received_events_url": "https://api.github.com/users/go-inoue/received_events",
"type": "User",
"site_admin": false
}
|
[] |
open
| false | null |
[] | null |
[
"I am facing probably facing similar issues with \r\n\r\n`wiki40b_en_100_0`",
"Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the dataset is already created it should be fine",
"Thanks! I tested on 328MB text data on `n1-standard-8 (8 vCPUs, 30 GB memory)`. The main script ran without any issue, but it seems to require a huge space in the drive.\r\n\r\nAs suggested, I ran the following script before running the pre-training command with `xla_spawn.py`.\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n\r\nfile_path=\"your_file_name\"\r\nload_dataset(\"text\", data_files=file_path, split=\"train\")\r\n```\r\nThis will create `text-train.arrow` under the default cache directory. Then, I run the script with `xla_spawn.py`. It will load data from the cached file. My understanding is that there's no other way but to do this two-step process with the current version (0.4) of `nlp`.\r\n\r\nDuring another caching process that happens in the main script:\r\n\r\n```\r\n08/26/2020 09:19:51 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 09:19:53 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-f90f341e5308a7469\r\n8d872bcc88f9c0e.arrow\r\n```\r\n\r\n`nlp` generates a temporary file per core, each of which is three times larger than the original text data. If each process is actually writing on the disk, you will need a huge amount of space in your drive. (Maybe I'm missing something.)\r\n\r\n```\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp0k43sazw\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp7sxs9mj5\r\n-rw------- 1 ***** ***** 939M Aug 26 09:31 tmpbbiqw2vp\r\n-rw------- 1 ***** ***** 937M Aug 26 09:31 tmpjxb5ptyu\r\n-rw------- 1 ***** ***** 933M Aug 26 09:31 tmpk3hkdh0e\r\n-rw------- 1 ***** ***** 944M Aug 26 09:31 tmpnoalwftz\r\n-rw------- 1 ***** ***** 931M Aug 26 09:31 tmpuxdr_dz3\r\n-rw------- 1 ***** ***** 945M Aug 26 09:31 tmpxjyuy6dk\r\n```\r\nAfter the caching process, they seem to be merged into one file.\r\n\r\n```\r\n-rw------- 1 ***** ***** 989M Aug 26 09:32 cache-f90f341e5308a74698d872bcc88f9c0e.arrow\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n```",
"Again it looks like every process tries to tokenize the full dataset at the same time.\r\nIf you do the tokenization before calling `xla_spawn.py` once, then each process will then use the tokenized cached file `cache-f90f341e5308a74698d872bcc88f9c0e.arrow` and not recompute it.\r\n\r\nNot sure if there's a better way to do that cc @julien-c @thomwolf ",
"I wrote a separate script just for preparing a cached file, including tokenization. Each process did use the tokenized cached file.\r\n\r\nCurrently I'm testing the pipeline on 24GB text data. It took about 1.5 hour to create a cached file on `n1-highmem-16 (16 vCPUs, 104 GB memory)`. I assume loading this cached file in the main script with `xla_spawn.py` won't be an issue (even if there are 8 processes).\r\n\r\n```\r\ntotal 98G\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 13:38 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 12:24 ..\r\n-rw------- 1 ***** ***** 74G Aug 26 13:38 cache-a7aa04134ba7b1aff5d9710f14a4e334.arrow\r\n-rw-r--r-- 1 ***** ***** 681 Aug 26 12:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 12:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 25G Aug 26 12:24 text-train.arrow\r\n```",
"Yes loading the cached file should be fine from different processes",
"Sorry, I thought it was working, but actually the second call doesn't use the cached file that was generated separately, and it will generate another cache-****.arrorw file with a different name. If I run the training script again (with `xla_spawn.py`), it will use the second cached file, which was generated by the training script itself in the previous run.\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:35 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:29 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:35 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:29 cache-69633651476e943b93c89ace715f9487.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:33 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:33 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:29 text-train.arrow\r\n```",
"So if I understand correctly it means that the cached file generated by your separated script is different by the one used by the training script ?",
"Yes.\r\n\r\n1. `cache-69633651476e943b93c89ace715f9487.arrow` was generated with a separate script. \r\n2. I ran the entire script with `xla_spawn.py`.\r\n3. `cache-69633651476e943b93c89ace715f9487.arrow` is not used.\r\n4. `cache-0d77dfce704493dbe63f071eed6a5431.arrow` is created.\r\n5. training starts...\r\n\r\nNow, if I kill the process at step 5, and do the step 2 again, it will use `cache-0d77dfce704493dbe63f071eed6a5431.arrow` (cached file created at step 4) without any issue.\r\n\r\nI used the following to generate the first cached file.\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```",
"1. Here's the log from the first step.\r\n```\r\nDownloading and preparing dataset text/default-e84dd29acc4ad9ef (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDataset text downloaded and prepared to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d. Subsequent calls will reuse this data.\r\n```\r\nThere's a file named `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow`, so it did create a cached file.\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:59 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:58 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:58 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n2. Ideally, `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow` should be used in `run_language_modeling.py` (modified version using `nlp`) with `xla_spawn.py`. But it looks like it's creating a new cached file.\r\n\r\n```\r\n08/26/2020 16:13:03 - INFO - filelock - Lock 139635836351096 released on /home/*****/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.202fa4f84f552bff1f5400ae012663839c61efb3de068c6c8722d34ac0ea6192\r\n.py.lock\r\n08/26/2020 16:13:03 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce704493dbe\r\n63f071eed6a5431.arrow\r\n^M 0%| | 0/100 [00:00<?, ?it/s]08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6\r\nfe661fe4d070d380d/cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n```\r\n\r\nThere are two cached files in the directory:\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 16:14 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 16:14 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 16:13 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 16:13 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n\r\nIf I kill the process, and run it again, it will use the second cached file.\r\n\r\n```\r\n08/26/2020 16:19:52 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:19:52 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Loading cached processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce70\r\n4493dbe63f071eed6a5431.arrow\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Set __getitem__(key) output type to torch for ['input_ids'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```",
"Thanks for all the details.\r\nThe two cached files are supposed to be the same. I suspect that the caching has a problem with the tokenizer.\r\nWhich tokenizer did you use ?",
"I trained a byte-level BPE tokenizer on my data with `tokenziers` library following this [example](https://github.com/huggingface/tokenizers/blob/master/bindings/python/examples/train_bytelevel_bpe.py).\r\n\r\nAnd I put these model files in a directory named `\"model_name\"`. I also put config.json, which is the original RoBERTa config file.\r\n\r\n```bash\r\n%ls model_name\r\nconfig.json merges.txt vocab.json\r\n```\r\n\r\n[This](https://github.com/huggingface/transformers/blob/4bd7be9a4268221d2a0000c7e8033aaeb365c03b/examples/language-modeling/run_language_modeling.py#L196) is the line where `run_language_modeling.py` loads the tokenier.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\n\r\nI use `\"model_name\"` for `model_args.tokenizer_name`. I don't specify `model_args.cache_dir`. It is 'None' by default.",
"In my separated script for caching, I'm using `use_fast=True` when initializing a tokenizer.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(args.config_name, use_fast=True)\r\n```\r\nI wasn't using that option in the main script. That could be the reason...",
"Yea it could definitely explain why you have two different cache files.\r\nLet me know if using the same tokenizers on both sides fixes the issue",
"It still creates a new file even if I remove `use_fast=True`... \r\n\r\nHere's the script used to create a cached file.\r\n```python \r\n#!/usr/bin/env python3\r\n\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\n\r\nfrom nlp import load_dataset\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--config_name', type=str, help='Pretrained config name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(args.config_name)\r\n\r\n dataset = load_dataset(\"text\", data_files=args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nHere's how the data is loaded in the modified `run_language_modeling.py`. [[original function](https://github.com/huggingface/transformers/blob/971d1802d009d9996b36a34a34477cee849ef39f/examples/language-modeling/run_language_modeling.py#L128-L135)]\r\n\r\n```python\r\ndef get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):\r\n file_path = args.eval_data_file if evaluate else args.train_data_file\r\n split = \"validation\" if evaluate else \"train\"\r\n if args.line_by_line:\r\n # return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n return dataset\r\n\r\n else:\r\n return TextDataset(\r\n tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache\r\n )\r\n```\r\n\r\nProbably I don't need this part in the main script,\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nand simply do this?\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\nreturn dataset\r\n```",
"You need this part in the main script or it will use the dataset that is not tokenized\r\n\r\n",
"I can see that the tokenizer in `run_language_modeling.py` is not instantiated the same way as in your separated script.\r\nIndeed we can see L196:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\nCould you try to make it so they are instantiated the exact same way please ?",
"I updated my separated script, but it's creating a cached file again. If I don't use the `model_args.cache_dir`, both will get `None`, so they should be the same.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n\r\n model_args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n\r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nIs there a way to specify the cache file to load, and skip the re-computation?",
"Could you also check that the `args.block_size` used in the lambda function is the same as well ?",
"Here's a minimal working example to reproduce this issue.\r\n\r\nAssumption:\r\n- You have access to TPU.\r\n- You have installed `transformers` and `nlp`.\r\n- You have tokenizer files (`config.json`, `merges.txt`, `vocab.json`) under the directory named `model_name`.\r\n- You have `xla_spawn.py` (Download from https://github.com/huggingface/transformers/blob/master/examples/xla_spawn.py).\r\n- You have saved the following script as `prepare_cached_dataset.py`.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n parser.add_argument('--tpu_num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8). For xla_apwan.py')\r\n model_args = parser.parse_args()\r\n \r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=True)\r\n \r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\ndef _mp_fn(index):\r\n # For xla_spawn (TPUs)\r\n main()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\n- Run the following command. Replace `your_training_data` with some text file.\r\n\r\n```bash\r\nexport TRAIN_DATA=your_training_data\r\n\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:08 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:08 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n\r\n- Run the same script again. (The output should be just `Using custom data configuration default`.)\r\n```\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:20 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:20 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n- The cached file (`cache-bfc7cb0702426d19242db5e8c079f04b.arrow`) is reused.\r\n- Now, run this script with `xla_spawn.py`. Ideally, it should reuse the cached file, however, you will see each process is creating a cache file again.\r\n\r\n```bash\r\npython xla_spawn.py --num_cores 8 \\\r\nprepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n\r\n- Check the cached directory. There are two arrrow files.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 230M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:25 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw------- 1 ***** ***** 99M Aug 28 13:25 cache-e0e2313e49c8a110aafcc8133154c19a.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n",
"I ended up specifying the `cache_file_name` argument when I call `map` function.\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size),\r\n batched=True,\r\n cache_file_name=cache_file_name)\r\n```\r\n\r\nNote:\r\n- `text` dataset in `nlp` does not strip `\"\\n\"`. If you want the same output as in [`LineByLineTextDataset`](https://github.com/huggingface/transformers/blob/afc4ece462ad83a090af620ff4da099a0272e171/src/transformers/data/datasets/language_modeling.py#L88-L111), you would need to create your own dataset class where you replace `line` to `line.strip()` [here](https://github.com/huggingface/nlp/blob/master/datasets/text/text.py#L35).\r\n"
] | 2020-08-25T14:36:38 | 2020-09-01T12:14:56 | null |
NONE
| null | null | null |
Hi,
I'm getting a "File exists" error when I use [text dataset](https://github.com/huggingface/nlp/tree/master/datasets/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).
I modified [line 131 in the original `run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py#L131) as follows:
```python
# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
dataset = load_dataset("text", data_files=file_path, split="train")
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
truncation=True, max_length=args.block_size), batched=True)
dataset.set_format(type='torch', columns=['input_ids'])
return dataset
```
When I run this with [`xla_spawn.py`](https://github.com/huggingface/transformers/blob/master/examples/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).
It seems the current version doesn't take into account distributed training processes as in [this example](https://github.com/huggingface/transformers/blob/a573777901e662ec2e565be312ffaeedef6effec/src/transformers/data/datasets/language_modeling.py#L35-L38)?
```
08/25/2020 13:59:41 - WARNING - nlp.builder - Using custom data configuration default
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
08/25/2020 13:59:43 - INFO - nlp.builder - Generating dataset text (/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Exception in device=TPU:6: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:4: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:1: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Exception in device=TPU:7: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:3: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Downloading and preparing dataset text/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/
447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...
Exception in device=TPU:2: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Exception in device=TPU:0: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
Traceback (most recent call last):
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 231, in _start_fn
fn(gindex, *args)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 300, in _mp_fn
main()
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 240, in main
train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
File "/home/*****/huggingface_roberta/run_language_modeling.py", line 134, in get_dataset
dataset = load_dataset("text", data_files=file_path, split="train")
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/load.py", line 546, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 450, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/anaconda3/envs/torch-xla-1.6/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/532/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/532/timeline
| null | null |
https://api.github.com/repos/huggingface/datasets/issues/525
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/525/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/525/comments
|
https://api.github.com/repos/huggingface/datasets/issues/525/events
|
https://github.com/huggingface/datasets/issues/525
| 683,875,483 |
MDU6SXNzdWU2ODM4NzU0ODM=
| 525 |
wmt download speed example
|
{
"login": "sshleifer",
"id": 6045025,
"node_id": "MDQ6VXNlcjYwNDUwMjU=",
"avatar_url": "https://avatars.githubusercontent.com/u/6045025?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/sshleifer",
"html_url": "https://github.com/sshleifer",
"followers_url": "https://api.github.com/users/sshleifer/followers",
"following_url": "https://api.github.com/users/sshleifer/following{/other_user}",
"gists_url": "https://api.github.com/users/sshleifer/gists{/gist_id}",
"starred_url": "https://api.github.com/users/sshleifer/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/sshleifer/subscriptions",
"organizations_url": "https://api.github.com/users/sshleifer/orgs",
"repos_url": "https://api.github.com/users/sshleifer/repos",
"events_url": "https://api.github.com/users/sshleifer/events{/privacy}",
"received_events_url": "https://api.github.com/users/sshleifer/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Thanks for creating the issue :)\r\nThe download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.\r\nIs this mirror official ?\r\n\r\nAlso it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool\r\n\r\nAlso cc @patrickvonplaten ",
"Mirror is not official.",
"Shall we host the files ourselves or it is fine to use this mirror in your opinion ?",
"Should we add an argument in `load_dataset` to override some URL with a custom URL (e.g. mirror) or a local path?\r\n\r\nThis could also be used to provide local files instead of the original files as requested by some users (e.g. when you made a dataset with the same format than SQuAD and what to use it instead of the official dataset files).",
"@lhoestq I think we should host it ourselves. I'll put the subset of wmt (without preprocessed files) that we need on s3 and post a link over the weekend.",
"Is there a solution yet? The download speed is still too slow. 60-70kbps download for wmt16 and around 100kbps for wmt19. @sshleifer ",
"I'm working on mirror links which will provide high download speed :)\r\nSee https://github.com/huggingface/datasets/issues/1892",
"Resolved via https://github.com/huggingface/datasets/pull/1912"
] | 2020-08-21T23:29:06 | 2022-10-04T17:45:39 | 2022-10-04T17:45:39 |
CONTRIBUTOR
| null | null | null |
Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.
```
import nlp
nlp.load_dataset('wmt16', 'de-en')
```
Downloads at 49.1 KB/S
Whereas
```
pip install gdown # download from google drive
!gdown https://drive.google.com/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj
```
Downloads at 127 MB/s. (The file is a copy of wmt-en-de raw).
```
nlp.load_dataset('wmt16', 'ro-en')
```
goes at 27 MB/s, much faster.
if we wget the same data from s3 is the same download speed, but ¼ the file size:
```
wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro_packed_200_rand.tgz
```
Finally,
```
nlp.load_dataset('wmt19', 'zh-en')
```
Starts fast, but broken. (duplicate of #493 )
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/525/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/525/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/524
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/524/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/524/comments
|
https://api.github.com/repos/huggingface/datasets/issues/524/events
|
https://github.com/huggingface/datasets/issues/524
| 683,686,359 |
MDU6SXNzdWU2ODM2ODYzNTk=
| 524 |
Some docs are missing parameter names
|
{
"login": "jarednielsen",
"id": 4564897,
"node_id": "MDQ6VXNlcjQ1NjQ4OTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/4564897?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jarednielsen",
"html_url": "https://github.com/jarednielsen",
"followers_url": "https://api.github.com/users/jarednielsen/followers",
"following_url": "https://api.github.com/users/jarednielsen/following{/other_user}",
"gists_url": "https://api.github.com/users/jarednielsen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jarednielsen/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jarednielsen/subscriptions",
"organizations_url": "https://api.github.com/users/jarednielsen/orgs",
"repos_url": "https://api.github.com/users/jarednielsen/repos",
"events_url": "https://api.github.com/users/jarednielsen/events{/privacy}",
"received_events_url": "https://api.github.com/users/jarednielsen/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Indeed, good catch!"
] | 2020-08-21T16:47:34 | 2020-08-25T09:04:03 | 2020-08-25T09:04:03 |
CONTRIBUTOR
| null | null | null |
See https://huggingface.co/nlp/master/package_reference/main_classes.html#nlp.Dataset.map. I believe this is because the parameter names are enclosed in backticks in the docstrings, maybe it's an old docstring format that doesn't work with the current Sphinx version.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/524/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/524/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/522
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/522/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/522/comments
|
https://api.github.com/repos/huggingface/datasets/issues/522/events
|
https://github.com/huggingface/datasets/issues/522
| 682,478,833 |
MDU6SXNzdWU2ODI0Nzg4MzM=
| 522 |
dictionnary typo in docs
|
{
"login": "yonigottesman",
"id": 4004127,
"node_id": "MDQ6VXNlcjQwMDQxMjc=",
"avatar_url": "https://avatars.githubusercontent.com/u/4004127?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/yonigottesman",
"html_url": "https://github.com/yonigottesman",
"followers_url": "https://api.github.com/users/yonigottesman/followers",
"following_url": "https://api.github.com/users/yonigottesman/following{/other_user}",
"gists_url": "https://api.github.com/users/yonigottesman/gists{/gist_id}",
"starred_url": "https://api.github.com/users/yonigottesman/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/yonigottesman/subscriptions",
"organizations_url": "https://api.github.com/users/yonigottesman/orgs",
"repos_url": "https://api.github.com/users/yonigottesman/repos",
"events_url": "https://api.github.com/users/yonigottesman/events{/privacy}",
"received_events_url": "https://api.github.com/users/yonigottesman/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Thanks!"
] | 2020-08-20T07:11:05 | 2020-08-20T07:52:14 | 2020-08-20T07:52:13 |
CONTRIBUTOR
| null | null | null |
Many places dictionary is spelled dictionnary, not sure if its on purpose or not.
Fixed in this pr:
https://github.com/huggingface/nlp/pull/521
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/522/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/522/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/519
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/519/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/519/comments
|
https://api.github.com/repos/huggingface/datasets/issues/519/events
|
https://github.com/huggingface/datasets/issues/519
| 682,193,882 |
MDU6SXNzdWU2ODIxOTM4ODI=
| 519 |
[BUG] Metrics throwing new error on master since 0.4.0
|
{
"login": "jbragg",
"id": 2238344,
"node_id": "MDQ6VXNlcjIyMzgzNDQ=",
"avatar_url": "https://avatars.githubusercontent.com/u/2238344?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jbragg",
"html_url": "https://github.com/jbragg",
"followers_url": "https://api.github.com/users/jbragg/followers",
"following_url": "https://api.github.com/users/jbragg/following{/other_user}",
"gists_url": "https://api.github.com/users/jbragg/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jbragg/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jbragg/subscriptions",
"organizations_url": "https://api.github.com/users/jbragg/orgs",
"repos_url": "https://api.github.com/users/jbragg/repos",
"events_url": "https://api.github.com/users/jbragg/events{/privacy}",
"received_events_url": "https://api.github.com/users/jbragg/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric",
"Closing - seems to be just forgetting to tokenize. And found the helpful discussion in huggingface/evaluate#105 "
] | 2020-08-19T21:29:15 | 2022-06-02T16:41:01 | 2020-08-19T22:04:40 |
CONTRIBUTOR
| null | null | null |
The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu.
Wasn't happening on 0.4.0 but happening now on master.
```
File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 226, in compute
self.add_batch(predictions=predictions, references=references)
File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 242, in add_batch
batch = self.info.features.encode_batch(batch)
File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in encode_batch
encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]
File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in <listcomp>
encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]
File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 456, in encode_nested_example
raise ValueError("Got a string but expected a list instead: '{}'".format(obj))
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/519/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/519/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/517
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/517/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/517/comments
|
https://api.github.com/repos/huggingface/datasets/issues/517/events
|
https://github.com/huggingface/datasets/issues/517
| 681,896,944 |
MDU6SXNzdWU2ODE4OTY5NDQ=
| 517 |
add MLDoc dataset
|
{
"login": "jxmorris12",
"id": 13238952,
"node_id": "MDQ6VXNlcjEzMjM4OTUy",
"avatar_url": "https://avatars.githubusercontent.com/u/13238952?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jxmorris12",
"html_url": "https://github.com/jxmorris12",
"followers_url": "https://api.github.com/users/jxmorris12/followers",
"following_url": "https://api.github.com/users/jxmorris12/following{/other_user}",
"gists_url": "https://api.github.com/users/jxmorris12/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jxmorris12/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jxmorris12/subscriptions",
"organizations_url": "https://api.github.com/users/jxmorris12/orgs",
"repos_url": "https://api.github.com/users/jxmorris12/repos",
"events_url": "https://api.github.com/users/jxmorris12/events{/privacy}",
"received_events_url": "https://api.github.com/users/jxmorris12/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067376369,
"node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request",
"name": "dataset request",
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset"
}
] |
open
| false | null |
[] | null |
[
"Any updates on this?",
"This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies."
] | 2020-08-19T14:41:59 | 2021-08-03T05:59:33 | null |
CONTRIBUTOR
| null | null | null |
Hi,
I am recommending that someone add MLDoc, a multilingual news topic classification dataset.
- Here's a link to the Github: https://github.com/facebookresearch/MLDoc
- and the paper: http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf
Looks like the dataset contains news stories in multiple languages that can be classified into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). There are 13 languages: Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/517/reactions",
"total_count": 4,
"+1": 4,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/517/timeline
| null | null |
https://api.github.com/repos/huggingface/datasets/issues/514
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/514/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/514/comments
|
https://api.github.com/repos/huggingface/datasets/issues/514/events
|
https://github.com/huggingface/datasets/issues/514
| 681,256,348 |
MDU6SXNzdWU2ODEyNTYzNDg=
| 514 |
dataset.shuffle(keep_in_memory=True) is never allowed
|
{
"login": "vegarab",
"id": 24683907,
"node_id": "MDQ6VXNlcjI0NjgzOTA3",
"avatar_url": "https://avatars.githubusercontent.com/u/24683907?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/vegarab",
"html_url": "https://github.com/vegarab",
"followers_url": "https://api.github.com/users/vegarab/followers",
"following_url": "https://api.github.com/users/vegarab/following{/other_user}",
"gists_url": "https://api.github.com/users/vegarab/gists{/gist_id}",
"starred_url": "https://api.github.com/users/vegarab/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/vegarab/subscriptions",
"organizations_url": "https://api.github.com/users/vegarab/orgs",
"repos_url": "https://api.github.com/users/vegarab/repos",
"events_url": "https://api.github.com/users/vegarab/events{/privacy}",
"received_events_url": "https://api.github.com/users/vegarab/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 1935892877,
"node_id": "MDU6TGFiZWwxOTM1ODkyODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue",
"name": "good first issue",
"color": "7057ff",
"default": true,
"description": "Good for newcomers"
},
{
"id": 4614514401,
"node_id": "LA_kwDODunzps8AAAABEwvm4Q",
"url": "https://api.github.com/repos/huggingface/datasets/labels/hacktoberfest",
"name": "hacktoberfest",
"color": "DF8D62",
"default": false,
"description": ""
}
] |
closed
| false | null |
[] | null |
[
"This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf ",
"Maybe I'm a bit tired but I fail to see the issue here.\r\n\r\nSince `cache_file_name` is `None` by default, if you set `keep_in_memory` to `True`, the assert should pass, no?",
"I failed to realise that this only applies to `shuffle()`. Whenever `keep_in_memory` is set to True, this is passed on to the `select()` function. However, if `cache_file_name` is None, it will be defined in the `shuffle()` function before it is passed on to `select()`. \r\n\r\nThus, `select()` is called with `keep_in_memory=True` and a not None value for `cache_file_name`. \r\nThis is essentially fixed in #513 \r\n\r\nEasily reproducible:\r\n```python\r\n>>> import nlp\r\n>>> data = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> data.shuffle(keep_in_memory=True)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1398, in shuffle\r\n verbose=verbose,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1178, in select\r\n ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\nAssertionError: Please use either `keep_in_memory` or `cache_file_name` but not both.\r\n>>>data.select([0], keep_in_memory=True)\r\n# No error\r\n```",
"Oh yes ok got it thanks. Should be fixed if we are happy with #513 indeed.",
"My bad. This is actually not fixed in #513. Sorry about that...\r\nThe new `indices_cache_file_name` is set to a non-None value in the new `shuffle()` as well. \r\n\r\nThe buffer and caching mechanisms used in the `select()` function are too intricate for me to understand why the check is there at all. I've removed it in my local build and it seems to be working fine for my project, without really considering other implications of the change. \r\n\r\n",
"Ok I'll investigate and add a series of tests on the `keep_in_memory=True` settings which is under-tested atm",
"Hey, still seeing this issue with the latest version.",
"The same :(",
"These are the steps needed to fix this issue:\r\n1. add the following check to `Dataset.shuffle`:\r\n```python\r\nif keep_in_memory and indices_cache_file_name is not None:\r\n raise ValueError(\"Please use either `keep_in_memory` or `indices_cache_file_name` but not both.\")\r\n```\r\n2. set `indices_cache_file_name` to `None` if `keep_in_memory` is True in the call to `select`\r\n3. add a test with `shuffle(keep_in_memory=True)`",
"Hi @mariosasko , I have opened this PR #5082 "
] | 2020-08-18T18:47:40 | 2022-10-10T12:21:58 | 2022-10-10T12:21:58 |
CONTRIBUTOR
| null | null | null |
As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`
The commit added the lines
```python
# lines 994-996 in src/nlp/arrow_dataset.py
assert (
not keep_in_memory or cache_file_name is None
), "Please use either `keep_in_memory` or `cache_file_name` but not both."
```
This affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check.
I'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/514/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/514/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/511
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/511/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/511/comments
|
https://api.github.com/repos/huggingface/datasets/issues/511/events
|
https://github.com/huggingface/datasets/issues/511
| 681,055,553 |
MDU6SXNzdWU2ODEwNTU1NTM=
| 511 |
dataset.shuffle() and select() resets format. Intended?
|
{
"login": "vegarab",
"id": 24683907,
"node_id": "MDQ6VXNlcjI0NjgzOTA3",
"avatar_url": "https://avatars.githubusercontent.com/u/24683907?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/vegarab",
"html_url": "https://github.com/vegarab",
"followers_url": "https://api.github.com/users/vegarab/followers",
"following_url": "https://api.github.com/users/vegarab/following{/other_user}",
"gists_url": "https://api.github.com/users/vegarab/gists{/gist_id}",
"starred_url": "https://api.github.com/users/vegarab/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/vegarab/subscriptions",
"organizations_url": "https://api.github.com/users/vegarab/orgs",
"repos_url": "https://api.github.com/users/vegarab/repos",
"events_url": "https://api.github.com/users/vegarab/events{/privacy}",
"received_events_url": "https://api.github.com/users/vegarab/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi @vegarab yes feel free to open a discussion here.\r\n\r\nThis design choice was not very much thought about.\r\n\r\nSince `dataset.select()` (like all the method without a trailing underscore) is non-destructive and returns a new dataset it has most of its properties initialized from scratch (except the table and infos).\r\n\r\nThinking about it I don't see a strong reason against transmitting the format from the parent dataset to its newly created child. It's probably what's expected by the user in most cases. What do you think @lhoestq?\r\n\r\nBy the way, I've been working today on a refactoring of all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`). The idea is to speed them up by a lot (like, really a lot) by working as much as possible with an indices mapping table instead of doing a deep copy of the full dataset as we've been doing currently. You can give it a look and try it here: https://github.com/huggingface/nlp/pull/513\r\nFeedbacks are very much welcome",
"I think it's ok to keep the format.\r\nIf we want to have this behavior for `.map` too we just have to make sure it doesn't keep a column that's been removed.",
"Shall we have this in the coming release by the way @lhoestq ?",
"Yes sure !",
"Since datasets 1.0.0 the format is not reset anymore.\r\nClosing this one, but feel free to re-open if you have other questions"
] | 2020-08-18T13:46:01 | 2020-09-14T08:45:38 | 2020-09-14T08:45:38 |
CONTRIBUTOR
| null | null | null |
Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?
When working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save("dataset.pt")`. Later loading the dataset object using `torch.load("dataset.pt")`, which conserves the defined format before saving.
I do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset.
The obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.
_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_
#### How to reproduce:
```python
import nlp
from transformers import T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("t5-base")
def create_features(batch):
context_encoding = tokenizer.batch_encode_plus(batch["context"])
return {"input_ids": context_encoding["input_ids"]}
dataset = nlp.load_dataset("cosmos_qa", split="train")
dataset = dataset.map(create_features, batched=True)
dataset.set_format(type="torch", columns=["input_ids"])
dataset[0]
# {'input_ids': tensor([ 1804, 3525, 1602, ... 0, 0])}
dataset = dataset.shuffle()
dataset[0]
# {'id': '3Q9(...)20', 'context': "Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/511/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/511/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/510
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/510/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/510/comments
|
https://api.github.com/repos/huggingface/datasets/issues/510/events
|
https://github.com/huggingface/datasets/issues/510
| 680,823,644 |
MDU6SXNzdWU2ODA4MjM2NDQ=
| 510 |
Version of numpy to use the library
|
{
"login": "isspek",
"id": 6966175,
"node_id": "MDQ6VXNlcjY5NjYxNzU=",
"avatar_url": "https://avatars.githubusercontent.com/u/6966175?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/isspek",
"html_url": "https://github.com/isspek",
"followers_url": "https://api.github.com/users/isspek/followers",
"following_url": "https://api.github.com/users/isspek/following{/other_user}",
"gists_url": "https://api.github.com/users/isspek/gists{/gist_id}",
"starred_url": "https://api.github.com/users/isspek/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/isspek/subscriptions",
"organizations_url": "https://api.github.com/users/isspek/orgs",
"repos_url": "https://api.github.com/users/isspek/repos",
"events_url": "https://api.github.com/users/isspek/events{/privacy}",
"received_events_url": "https://api.github.com/users/isspek/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Seems like this method was added in 1.17. I'll add a requirement on this.",
"Thank you so much. After upgrading the numpy library, it worked."
] | 2020-08-18T08:59:13 | 2020-08-19T18:35:56 | 2020-08-19T18:35:56 |
NONE
| null | null | null |
Thank you so much for your excellent work! I would like to use nlp library in my project. While importing nlp, I am receiving the following error `AttributeError: module 'numpy.random' has no attribute 'Generator'` Numpy version in my project is 1.16.0. May I learn which numpy version is used for the nlp library.
Thanks in advance.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/510/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/510/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/509
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/509/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/509/comments
|
https://api.github.com/repos/huggingface/datasets/issues/509/events
|
https://github.com/huggingface/datasets/issues/509
| 679,711,585 |
MDU6SXNzdWU2Nzk3MTE1ODU=
| 509 |
Converting TensorFlow dataset example
|
{
"login": "saareliad",
"id": 22762845,
"node_id": "MDQ6VXNlcjIyNzYyODQ1",
"avatar_url": "https://avatars.githubusercontent.com/u/22762845?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/saareliad",
"html_url": "https://github.com/saareliad",
"followers_url": "https://api.github.com/users/saareliad/followers",
"following_url": "https://api.github.com/users/saareliad/following{/other_user}",
"gists_url": "https://api.github.com/users/saareliad/gists{/gist_id}",
"starred_url": "https://api.github.com/users/saareliad/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/saareliad/subscriptions",
"organizations_url": "https://api.github.com/users/saareliad/orgs",
"repos_url": "https://api.github.com/users/saareliad/repos",
"events_url": "https://api.github.com/users/saareliad/events{/privacy}",
"received_events_url": "https://api.github.com/users/saareliad/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Do you want to convert a dataset script to the tfds format ?\r\nIf so, we currently have a comversion script nlp/commands/convert.py but it is a conversion script that goes from tfds to nlp.\r\nI think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).\r\nIf you manage to make it work in reverse, feel free to open a PR to share it with the community :)",
"In our docs: [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)."
] | 2020-08-16T08:05:20 | 2021-08-03T06:01:18 | 2021-08-03T06:01:17 |
NONE
| null | null | null |
Hi,
I want to use TensorFlow datasets with this repo, I noticed you made some conversion script,
can you give a simple example of using it?
Thanks
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/509/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/509/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/508
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/508/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/508/comments
|
https://api.github.com/repos/huggingface/datasets/issues/508/events
|
https://github.com/huggingface/datasets/issues/508
| 679,705,734 |
MDU6SXNzdWU2Nzk3MDU3MzQ=
| 508 |
TypeError: Receiver() takes no arguments
|
{
"login": "sebastiantomac",
"id": 1225851,
"node_id": "MDQ6VXNlcjEyMjU4NTE=",
"avatar_url": "https://avatars.githubusercontent.com/u/1225851?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/sebastiantomac",
"html_url": "https://github.com/sebastiantomac",
"followers_url": "https://api.github.com/users/sebastiantomac/followers",
"following_url": "https://api.github.com/users/sebastiantomac/following{/other_user}",
"gists_url": "https://api.github.com/users/sebastiantomac/gists{/gist_id}",
"starred_url": "https://api.github.com/users/sebastiantomac/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/sebastiantomac/subscriptions",
"organizations_url": "https://api.github.com/users/sebastiantomac/orgs",
"repos_url": "https://api.github.com/users/sebastiantomac/repos",
"events_url": "https://api.github.com/users/sebastiantomac/events{/privacy}",
"received_events_url": "https://api.github.com/users/sebastiantomac/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Which version of Apache Beam do you have (can you copy your full environment info here)?",
"apache-beam==2.23.0\r\nnlp==0.4.0\r\n\r\nFor me this was resolved by running the same python script on Linux (or really WSL). ",
"Do you manage to run a dummy beam pipeline with python on windows ? \r\nYou can test a dummy pipeline with [this code](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/wordcount_minimal.py)\r\n\r\nIf you get the same error, it means that the issue comes from apache beam.\r\nOtherwise we'll investigate what went wrong here",
"Still, same error, so I guess it is on apache beam then. \r\nThanks for the investigation.",
"Thanks for trying\r\nLet us know if you find clues of what caused this issue, or if you find a fix"
] | 2020-08-16T07:18:16 | 2020-09-01T14:53:33 | 2020-09-01T14:49:03 |
NONE
| null | null | null |
I am trying to load a wikipedia data set
```
import nlp
from nlp import load_dataset
dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=data_path, beam_runner='DirectRunner')
#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')
```
This fails in the apache beam runner.
```
Traceback (most recent call last):
File "D:/ML/wikiembedding/gpt2_sv.py", line 36, in <module>
dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=my_cache_dir, beam_runner='DirectRunner')
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 969, in _download_and_prepare
pipeline_results = pipeline.run()
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\pipeline.py", line 534, in run
return self.runner.run_pipeline(self, self._options)
....
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 218, in process_encoded
self.output(decoded_value)
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\operations.py", line 332, in output
cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)
File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\Cython\Shadow.py", line 167, in cast
return type(*args)
TypeError: Receiver() takes no arguments
```
This is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/508/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/508/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/507
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/507/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/507/comments
|
https://api.github.com/repos/huggingface/datasets/issues/507/events
|
https://github.com/huggingface/datasets/issues/507
| 679,400,683 |
MDU6SXNzdWU2Nzk0MDA2ODM=
| 507 |
Errors when I use
|
{
"login": "mchari",
"id": 30506151,
"node_id": "MDQ6VXNlcjMwNTA2MTUx",
"avatar_url": "https://avatars.githubusercontent.com/u/30506151?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/mchari",
"html_url": "https://github.com/mchari",
"followers_url": "https://api.github.com/users/mchari/followers",
"following_url": "https://api.github.com/users/mchari/following{/other_user}",
"gists_url": "https://api.github.com/users/mchari/gists{/gist_id}",
"starred_url": "https://api.github.com/users/mchari/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/mchari/subscriptions",
"organizations_url": "https://api.github.com/users/mchari/orgs",
"repos_url": "https://api.github.com/users/mchari/repos",
"events_url": "https://api.github.com/users/mchari/events{/privacy}",
"received_events_url": "https://api.github.com/users/mchari/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Looks like an issue with 3.0.2 transformers version. Works fine when I use \"master\" version of transformers."
] | 2020-08-14T21:03:57 | 2020-08-14T21:39:10 | 2020-08-14T21:39:10 |
NONE
| null | null | null |
I tried the following example code from https://huggingface.co/deepset/roberta-base-squad2 and got errors
I am using **transformers 3.0.2** code .
from transformers.pipelines import pipeline
from transformers.modeling_auto import AutoModelForQuestionAnswering
from transformers.tokenization_auto import AutoTokenizer
model_name = "deepset/roberta-base-squad2"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)
The errors are :
res = nlp(QA_input)
File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in __call__
for s, e, score in zip(starts, ends, scores)
File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in <listcomp>
for s, e, score in zip(starts, ends, scores)
KeyError: 0
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/507/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/507/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/501
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/501/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/501/comments
|
https://api.github.com/repos/huggingface/datasets/issues/501/events
|
https://github.com/huggingface/datasets/issues/501
| 677,952,893 |
MDU6SXNzdWU2Nzc5NTI4OTM=
| 501 |
Caching doesn't work for map (non-deterministic)
|
{
"login": "wulu473",
"id": 8149933,
"node_id": "MDQ6VXNlcjgxNDk5MzM=",
"avatar_url": "https://avatars.githubusercontent.com/u/8149933?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/wulu473",
"html_url": "https://github.com/wulu473",
"followers_url": "https://api.github.com/users/wulu473/followers",
"following_url": "https://api.github.com/users/wulu473/following{/other_user}",
"gists_url": "https://api.github.com/users/wulu473/gists{/gist_id}",
"starred_url": "https://api.github.com/users/wulu473/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/wulu473/subscriptions",
"organizations_url": "https://api.github.com/users/wulu473/orgs",
"repos_url": "https://api.github.com/users/wulu473/repos",
"events_url": "https://api.github.com/users/wulu473/events{/privacy}",
"received_events_url": "https://api.github.com/users/wulu473/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"Thanks for reporting !\r\n\r\nTo store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.\r\nThe hash doesn't seem to stay the same over sessions for the tokenizer.\r\nApparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing function.\r\n\r\nI'm working on a fix",
"Thanks everyone. Works great now.",
"Hi. I believe the fix was for the nlp library. Is there a solution to handle compiled regex expressions in .map() with the caching. I want to run a simple regex pattern on a big dataset, but I am running into the issue of compiled expression not being cached. \r\n\r\nInstead of opening a new issue, I thought I would put my query here. Let me know if a new issue would be more suitable. Thanks",
"Hi @MaveriQ! This fix is also included in the `datasets` library. Can you provide a reproducer?"
] | 2020-08-12T20:20:07 | 2022-08-08T11:02:23 | 2020-08-24T16:34:35 |
NONE
| null | null | null |
The caching functionality doesn't work reliably when tokenizing a dataset. Here's a small example to reproduce it.
```python
import nlp
import transformers
def main():
ds = nlp.load_dataset("reddit", split="train[:500]")
tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2")
def convert_to_features(example_batch):
input_str = example_batch["body"]
encodings = tokenizer(input_str, add_special_tokens=True, truncation=True)
return encodings
ds = ds.map(convert_to_features, batched=True)
if __name__ == "__main__":
main()
```
Roughly 3/10 times, this example recomputes the tokenization.
Is this expected behaviour?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/501/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/501/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/492
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/492/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/492/comments
|
https://api.github.com/repos/huggingface/datasets/issues/492/events
|
https://github.com/huggingface/datasets/issues/492
| 676,495,064 |
MDU6SXNzdWU2NzY0OTUwNjQ=
| 492 |
nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema
|
{
"login": "jarednielsen",
"id": 4564897,
"node_id": "MDQ6VXNlcjQ1NjQ4OTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/4564897?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jarednielsen",
"html_url": "https://github.com/jarednielsen",
"followers_url": "https://api.github.com/users/jarednielsen/followers",
"following_url": "https://api.github.com/users/jarednielsen/following{/other_user}",
"gists_url": "https://api.github.com/users/jarednielsen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jarednielsen/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jarednielsen/subscriptions",
"organizations_url": "https://api.github.com/users/jarednielsen/orgs",
"repos_url": "https://api.github.com/users/jarednielsen/repos",
"events_url": "https://api.github.com/users/jarednielsen/events{/privacy}",
"received_events_url": "https://api.github.com/users/jarednielsen/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.",
"Or maybe the assertion comes from elsewhere ?",
"I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.\r\n\r\nSince `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.",
"I'm doing a refactor of type inference in #363 . Both text fields should match after that",
"By default nullable will be set to True",
"It should be good now. I was able to run\r\n\r\n```python\r\n>>> from nlp import concatenate_datasets, load_dataset\r\n>>>\r\n>>> bookcorpus = load_dataset(\"bookcorpus\", split=\"train\")\r\n>>> wiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\n>>> wiki.remove_columns_(\"title\") # only keep the text\r\n>>>\r\n>>> assert bookcorpus.features.type == wiki.features.type\r\n>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])\r\n```",
"Thanks!"
] | 2020-08-11T00:27:46 | 2020-08-26T16:17:19 | 2020-08-26T16:17:19 |
CONTRIBUTOR
| null | null | null |
Here's the code I'm trying to run:
```python
dset_wikipedia = nlp.load_dataset("wikipedia", "20200501.en", split="train", cache_dir=args.cache_dir)
dset_wikipedia.drop(columns=["title"])
dset_wikipedia.features.pop("title")
dset_books = nlp.load_dataset("bookcorpus", split="train", cache_dir=args.cache_dir)
dset = nlp.concatenate_datasets([dset_wikipedia, dset_books])
```
This fails because they have different schemas, despite having identical features.
```python
assert dset_wikipedia.features == dset_books.features # True
assert dset_wikipedia._data.schema == dset_books._data.schema # False
```
The Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.
```python
dset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/492/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/492/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/491
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/491/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/491/comments
|
https://api.github.com/repos/huggingface/datasets/issues/491/events
|
https://github.com/huggingface/datasets/issues/491
| 676,486,275 |
MDU6SXNzdWU2NzY0ODYyNzU=
| 491 |
No 0.4.0 release on GitHub
|
{
"login": "jarednielsen",
"id": 4564897,
"node_id": "MDQ6VXNlcjQ1NjQ4OTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/4564897?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jarednielsen",
"html_url": "https://github.com/jarednielsen",
"followers_url": "https://api.github.com/users/jarednielsen/followers",
"following_url": "https://api.github.com/users/jarednielsen/following{/other_user}",
"gists_url": "https://api.github.com/users/jarednielsen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jarednielsen/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jarednielsen/subscriptions",
"organizations_url": "https://api.github.com/users/jarednielsen/orgs",
"repos_url": "https://api.github.com/users/jarednielsen/repos",
"events_url": "https://api.github.com/users/jarednielsen/events{/privacy}",
"received_events_url": "https://api.github.com/users/jarednielsen/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I did the release on github, and updated the doc :)\r\nSorry for the delay",
"Thanks!"
] | 2020-08-10T23:59:57 | 2020-08-11T16:50:07 | 2020-08-11T16:50:07 |
CONTRIBUTOR
| null | null | null |
0.4.0 was released on PyPi, but not on GitHub. This means [the documentation](https://huggingface.co/nlp/) is still displaying from 0.3.0, and that there's no tag to easily clone the 0.4.0 version of the repo.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/491/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/491/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/490
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/490/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/490/comments
|
https://api.github.com/repos/huggingface/datasets/issues/490/events
|
https://github.com/huggingface/datasets/issues/490
| 676,482,242 |
MDU6SXNzdWU2NzY0ODIyNDI=
| 490 |
Loading preprocessed Wikipedia dataset requires apache_beam
|
{
"login": "jarednielsen",
"id": 4564897,
"node_id": "MDQ6VXNlcjQ1NjQ4OTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/4564897?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jarednielsen",
"html_url": "https://github.com/jarednielsen",
"followers_url": "https://api.github.com/users/jarednielsen/followers",
"following_url": "https://api.github.com/users/jarednielsen/following{/other_user}",
"gists_url": "https://api.github.com/users/jarednielsen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jarednielsen/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jarednielsen/subscriptions",
"organizations_url": "https://api.github.com/users/jarednielsen/orgs",
"repos_url": "https://api.github.com/users/jarednielsen/repos",
"events_url": "https://api.github.com/users/jarednielsen/events{/privacy}",
"received_events_url": "https://api.github.com/users/jarednielsen/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-08-10T23:46:50 | 2020-08-14T13:17:20 | 2020-08-14T13:17:20 |
CONTRIBUTOR
| null | null | null |
Running
`nlp.load_dataset("wikipedia", "20200501.en", split="train", dir="/tmp/wikipedia")`
gives an error if apache_beam is not installed, stemming from
https://github.com/huggingface/nlp/blob/38eb2413de54ee804b0be81781bd65ac4a748ced/src/nlp/builder.py#L981-L988
This succeeded without the dependency in version 0.3.0. This seems like an unnecessary dependency to process some dataset info if you're using the already-preprocessed version. Could it be removed?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/490/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/490/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/489
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/489/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/489/comments
|
https://api.github.com/repos/huggingface/datasets/issues/489/events
|
https://github.com/huggingface/datasets/issues/489
| 676,456,257 |
MDU6SXNzdWU2NzY0NTYyNTc=
| 489 |
ug
|
{
"login": "timothyjlaurent",
"id": 2000204,
"node_id": "MDQ6VXNlcjIwMDAyMDQ=",
"avatar_url": "https://avatars.githubusercontent.com/u/2000204?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/timothyjlaurent",
"html_url": "https://github.com/timothyjlaurent",
"followers_url": "https://api.github.com/users/timothyjlaurent/followers",
"following_url": "https://api.github.com/users/timothyjlaurent/following{/other_user}",
"gists_url": "https://api.github.com/users/timothyjlaurent/gists{/gist_id}",
"starred_url": "https://api.github.com/users/timothyjlaurent/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/timothyjlaurent/subscriptions",
"organizations_url": "https://api.github.com/users/timothyjlaurent/orgs",
"repos_url": "https://api.github.com/users/timothyjlaurent/repos",
"events_url": "https://api.github.com/users/timothyjlaurent/events{/privacy}",
"received_events_url": "https://api.github.com/users/timothyjlaurent/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"whoops",
"please delete this"
] | 2020-08-10T22:33:03 | 2020-08-10T22:55:14 | 2020-08-10T22:33:40 |
NONE
| null | null | null |
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/489/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/489/timeline
| null |
completed
|
|
https://api.github.com/repos/huggingface/datasets/issues/488
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/488/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/488/comments
|
https://api.github.com/repos/huggingface/datasets/issues/488/events
|
https://github.com/huggingface/datasets/issues/488
| 676,299,993 |
MDU6SXNzdWU2NzYyOTk5OTM=
| 488 |
issues with downloading datasets for wmt16 and wmt19
|
{
"login": "stas00",
"id": 10676103,
"node_id": "MDQ6VXNlcjEwNjc2MTAz",
"avatar_url": "https://avatars.githubusercontent.com/u/10676103?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/stas00",
"html_url": "https://github.com/stas00",
"followers_url": "https://api.github.com/users/stas00/followers",
"following_url": "https://api.github.com/users/stas00/following{/other_user}",
"gists_url": "https://api.github.com/users/stas00/gists{/gist_id}",
"starred_url": "https://api.github.com/users/stas00/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/stas00/subscriptions",
"organizations_url": "https://api.github.com/users/stas00/orgs",
"repos_url": "https://api.github.com/users/stas00/repos",
"events_url": "https://api.github.com/users/stas00/events{/privacy}",
"received_events_url": "https://api.github.com/users/stas00/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/ is gone",
"Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh/t @sshleifer ",
"Fixed in https://github.com/huggingface/datasets/pull/1912"
] | 2020-08-10T17:32:51 | 2022-10-04T17:46:59 | 2022-10-04T17:46:58 |
CONTRIBUTOR
| null | null | null |
I have encountered multiple issues while trying to:
```
import nlp
dataset = nlp.load_dataset('wmt16', 'ru-en')
metric = nlp.load_metric('wmt16')
```
1. I had to do `pip install -e ".[dev]" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that `pip install -e ".[dev]" ` fixed.
2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for.
I tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)
3. my machine has crushed and when I retried I got:
```
Traceback (most recent call last):
File "./download.py", line 9, in <module>
dataset = nlp.load_dataset('wmt16', 'ru-en')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 449, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/stas/anaconda3/envs/main/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/stas/anaconda3/envs/main/lib/python3.7/os.py", line 221, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/stas/.cache/huggingface/datasets/wmt16/ru-en/1.0.0/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'
```
it can't handle resumes. but neither allows a new start. Had to delete it manually.
4. and finally when it downloaded the dataset, it then failed to fetch the metrics:
```
Traceback (most recent call last):
File "./download.py", line 15, in <module>
metric = nlp.load_metric('wmt16')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 442, in load_metric
module_path, hash = prepare_module(path, download_config=download_config, dataset=False)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 258, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 198, in cached_path
local_files_only=download_config.local_files_only,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 356, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/metrics/wmt16/wmt16.py
```
5. If I run the same code with `wmt19`, it fails too:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/488/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/488/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/486
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/486/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/486/comments
|
https://api.github.com/repos/huggingface/datasets/issues/486/events
|
https://github.com/huggingface/datasets/issues/486
| 675,649,034 |
MDU6SXNzdWU2NzU2NDkwMzQ=
| 486 |
Bookcorpus data contains pretokenized text
|
{
"login": "orsharir",
"id": 99543,
"node_id": "MDQ6VXNlcjk5NTQz",
"avatar_url": "https://avatars.githubusercontent.com/u/99543?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/orsharir",
"html_url": "https://github.com/orsharir",
"followers_url": "https://api.github.com/users/orsharir/followers",
"following_url": "https://api.github.com/users/orsharir/following{/other_user}",
"gists_url": "https://api.github.com/users/orsharir/gists{/gist_id}",
"starred_url": "https://api.github.com/users/orsharir/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/orsharir/subscriptions",
"organizations_url": "https://api.github.com/users/orsharir/orgs",
"repos_url": "https://api.github.com/users/orsharir/repos",
"events_url": "https://api.github.com/users/orsharir/events{/privacy}",
"received_events_url": "https://api.github.com/users/orsharir/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do it. Could you provide more details ?",
"I'm afraid that I don't know how to obtain the original BookCorpus data. I believe this version came from an anonymous Google Drive link posted in another issue.\r\n\r\nGoing through the raw text in this version, it's apparent that NLTK's TreebankWordTokenizer was applied on it (I gave some examples in my original post), followed by:\r\n`' '.join(tokens)`\r\nYou can retrieve the tokenization by splitting on whitespace. You can then \"detokenize\" it with TreebankWordDetokenizer class of NLTK (though, as I suggested, use the fixed version in my repo). This will bring the text closer to its original form, but some steps of TreebankWordTokenizer are destructive, so it wouldn't be one-to-one. Something along the lines of the following should work:\r\n```\r\ntreebank_detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()\r\ndb = nlp.load_dataset('bookcorpus', split=nlp.Split.TRAIN)\r\ndb = db.map(lambda x: treebank_detokenizer.detokenize(x['text'].split()))\r\n```\r\n\r\nRegarding other issues beyond the above, I'm afraid that I can't help with that.",
"Ok I get it, that would be very cool indeed\r\n\r\nWhat kinds of patterns the detokenizer can't retrieve ?",
"The TreebankTokenizer makes some assumptions about whitespace, parentheses, quotation marks, etc. For instance, while tokenizing the following text:\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nwill result in:\r\n```\r\nDwayne `` The Rock '' Johnson\r\n```\r\nwhere the left and right quotation marks are turned into distinct symbols. Upon reconstruction, we can attach the left part to its token on the right, and respectively for the right part. However, the following texts would be tokenized exactly the same:\r\n```\r\nDwayne \" The Rock \" Johnson\r\nDwayne \" The Rock\" Johnson\r\nDwayne \" The Rock\" Johnson\r\n...\r\n```\r\nIn the above examples, the detokenizer would correct these inputs into the canonical text\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nHowever, there are cases where there the solution cannot easily be inferred (at least without a true LM - this tokenizer is just a bunch of regexes). For instance, in cases where you have a fragment that contains the end of quote, but not its beginning, plus an accidental space:\r\n```\r\n... and it sounds fantastic, \" he said.\r\n```\r\nIn the above case, the tokenizer would assume that the quotes refer to the next token, and so upon detokenization it will result in the following mistake:\r\n```\r\n... and it sounds fantastic, \"he said.\r\n```\r\n\r\nWhile these are all odd edge cases (the basic assumptions do make sense), in noisy data they can occur, which is why I mentioned that the detokenizer cannot restore the original perfectly.\r\n",
"To confirm, since this is preprocessed, this was not the exact version of the Book Corpus used to actually train the models described here (particularly Distilbert)? https://huggingface.co/datasets/bookcorpus\r\n\r\nOr does this preprocessing exactly match that of the papers?",
"I believe these are just artifacts of this particular source. It might be better to crawl it again, or use another preprocessed source, as found here: https://github.com/soskek/bookcorpus ",
"Yes actually the BookCorpus on hugginface is based on [this](https://github.com/soskek/bookcorpus/issues/24#issuecomment-643933352). And I kind of regret naming it as \"BookCorpus\" instead of something like \"BookCorpusLike\".\r\n\r\nBut there is a good news ! @shawwn has replicated BookCorpus in his way, and also provided a link to download the plain text files. see [here](https://github.com/soskek/bookcorpus/issues/27). There is chance we can have a \"OpenBookCorpus\" !",
"Resolved via #856"
] | 2020-08-09T06:53:24 | 2022-10-04T17:44:33 | 2022-10-04T17:44:33 |
CONTRIBUTOR
| null | null | null |
It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, "didn't" becomes "did" + "n't", and double quotes are changed to `` and '' for start and end quotes, respectively.
On my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https://github.com/nltk/nltk/pull/2575
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/486/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/486/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/485
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/485/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/485/comments
|
https://api.github.com/repos/huggingface/datasets/issues/485/events
|
https://github.com/huggingface/datasets/issues/485
| 675,595,393 |
MDU6SXNzdWU2NzU1OTUzOTM=
| 485 |
PAWS dataset first item is header
|
{
"login": "jxmorris12",
"id": 13238952,
"node_id": "MDQ6VXNlcjEzMjM4OTUy",
"avatar_url": "https://avatars.githubusercontent.com/u/13238952?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jxmorris12",
"html_url": "https://github.com/jxmorris12",
"followers_url": "https://api.github.com/users/jxmorris12/followers",
"following_url": "https://api.github.com/users/jxmorris12/following{/other_user}",
"gists_url": "https://api.github.com/users/jxmorris12/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jxmorris12/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jxmorris12/subscriptions",
"organizations_url": "https://api.github.com/users/jxmorris12/orgs",
"repos_url": "https://api.github.com/users/jxmorris12/repos",
"events_url": "https://api.github.com/users/jxmorris12/events{/privacy}",
"received_events_url": "https://api.github.com/users/jxmorris12/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-08-08T22:05:25 | 2020-08-19T09:50:01 | 2020-08-19T09:50:01 |
CONTRIBUTOR
| null | null | null |
```
import nlp
dataset = nlp.load_dataset('xtreme', 'PAWS-X.en')
dataset['test'][0]
```
prints the following
```
{'label': 'label', 'sentence1': 'sentence1', 'sentence2': 'sentence2'}
```
dataset['test'][0] should probably be the first item in the dataset, not just a dictionary mapping the column names to themselves. Probably just need to ignore the first row in the dataset by default or something like that.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/485/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/485/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/483
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/483/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/483/comments
|
https://api.github.com/repos/huggingface/datasets/issues/483/events
|
https://github.com/huggingface/datasets/issues/483
| 675,080,694 |
MDU6SXNzdWU2NzUwODA2OTQ=
| 483 |
rotten tomatoes movie review dataset taken down
|
{
"login": "jxmorris12",
"id": 13238952,
"node_id": "MDQ6VXNlcjEzMjM4OTUy",
"avatar_url": "https://avatars.githubusercontent.com/u/13238952?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jxmorris12",
"html_url": "https://github.com/jxmorris12",
"followers_url": "https://api.github.com/users/jxmorris12/followers",
"following_url": "https://api.github.com/users/jxmorris12/following{/other_user}",
"gists_url": "https://api.github.com/users/jxmorris12/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jxmorris12/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jxmorris12/subscriptions",
"organizations_url": "https://api.github.com/users/jxmorris12/orgs",
"repos_url": "https://api.github.com/users/jxmorris12/repos",
"events_url": "https://api.github.com/users/jxmorris12/events{/privacy}",
"received_events_url": "https://api.github.com/users/jxmorris12/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz",
"fixed in #484 ",
"Closing this one. Thanks again @jxmorris12 for taking care of this :)"
] | 2020-08-07T15:12:01 | 2020-09-08T09:36:34 | 2020-09-08T09:36:33 |
CONTRIBUTOR
| null | null | null |
In an interesting twist of events, the individual who created the movie review seems to have left Cornell, and their webpage has been removed, along with the movie review dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz). It's not downloadable anymore.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/483/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/483/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/482
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/482/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/482/comments
|
https://api.github.com/repos/huggingface/datasets/issues/482/events
|
https://github.com/huggingface/datasets/issues/482
| 674,851,147 |
MDU6SXNzdWU2NzQ4NTExNDc=
| 482 |
Bugs : dataset.map() is frozen on ELI5
|
{
"login": "ratthachat",
"id": 56621342,
"node_id": "MDQ6VXNlcjU2NjIxMzQy",
"avatar_url": "https://avatars.githubusercontent.com/u/56621342?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ratthachat",
"html_url": "https://github.com/ratthachat",
"followers_url": "https://api.github.com/users/ratthachat/followers",
"following_url": "https://api.github.com/users/ratthachat/following{/other_user}",
"gists_url": "https://api.github.com/users/ratthachat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ratthachat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ratthachat/subscriptions",
"organizations_url": "https://api.github.com/users/ratthachat/orgs",
"repos_url": "https://api.github.com/users/ratthachat/repos",
"events_url": "https://api.github.com/users/ratthachat/events{/privacy}",
"received_events_url": "https://api.github.com/users/ratthachat/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look",
"I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip install git+https://github.com/huggingface/nlp.git@fix-bad-type-in-overflow-check\r\n```\r\n\r\nAlso I noticed that the first 1000 examples have an empty list in the `title_urls` field. The feature type inference in `.map` will consider it `null` because of that, and it will crash when it encounter the next example with a `title_urls` that is not empty.\r\n\r\nTherefore to fix that, what you can do for now is increase the writer batch size so that the feature inference will take into account at least one example with a non-empty `title_urls`:\r\n\r\n```python\r\n# default batch size is 1_000 and it's not enough for feature type inference because of empty lists\r\nvalid_dataset = valid_dataset.map(make_input_target, writer_batch_size=3_000) \r\n```\r\n\r\nI was able to run the frozen cell with these changes.",
"@lhoestq Perfect and thank you very much!!\r\nClose the issue.",
"@lhoestq mapping the function `make_input_target` was passed by your fixing.\r\n\r\nHowever, there is another error in the final step of `valid_dataset.map(convert_to_features, batched=True)`\r\n\r\n`ArrowInvalid: Could not convert Thepiratebay.vg with type str: converting to null type`\r\n(The [same colab notebook above with new error message](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing#scrollTo=5sRrJ3_C8rLt))\r\n\r\nDo you have some ideas? (I am really sorry I could not debug it by myself since I never used `pyarrow` before) \r\nNote that `train_dataset.map(convert_to_features, batched=True)` can be run successfully even though train_dataset is 27x bigger than `valid_dataset` so I believe the problem lies in some field of `valid_dataset` again .",
"I got this issue too and fixed it by specifying `writer_batch_size=3_000` in `.map`.\r\nThis is because Arrow didn't expect `Thepiratebay.vg` in `title_urls `, as all previous examples have empty lists in `title_urls `",
"I am clear now . Thank so much again Quentin!",
"I'm getting a hanging `dataset.map()` when running a gradio app with `gradio` for auto-reloading instead of `python`",
"Maybe this is an issue with gradio, could you open an issue on their repo ? `Dataset.map` simply uses `multiprocess.Pool` for multiprocessing\r\n\r\nIf you interrupt the program mayeb the stack trace would give some information of where it was hanging in the code (maybe a lock somewhere ?)"
] | 2020-08-07T08:23:35 | 2023-04-06T09:39:59 | 2020-08-11T23:55:15 |
NONE
| null | null | null |
Hi Huggingface Team!
Thank you guys once again for this amazing repo.
I have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)
However, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 / 0.17.0 / 1.0.0 also have the same frozen process.
Reproducible code can be found on [this colab notebook ](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.
----------------------------------------
**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)
I also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/482/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/482/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/478
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/478/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/478/comments
|
https://api.github.com/repos/huggingface/datasets/issues/478/events
|
https://github.com/huggingface/datasets/issues/478
| 673,178,317 |
MDU6SXNzdWU2NzMxNzgzMTc=
| 478 |
Export TFRecord to GCP bucket
|
{
"login": "astariul",
"id": 43774355,
"node_id": "MDQ6VXNlcjQzNzc0MzU1",
"avatar_url": "https://avatars.githubusercontent.com/u/43774355?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/astariul",
"html_url": "https://github.com/astariul",
"followers_url": "https://api.github.com/users/astariul/followers",
"following_url": "https://api.github.com/users/astariul/following{/other_user}",
"gists_url": "https://api.github.com/users/astariul/gists{/gist_id}",
"starred_url": "https://api.github.com/users/astariul/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/astariul/subscriptions",
"organizations_url": "https://api.github.com/users/astariul/orgs",
"repos_url": "https://api.github.com/users/astariul/repos",
"events_url": "https://api.github.com/users/astariul/events{/privacy}",
"received_events_url": "https://api.github.com/users/astariul/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)"
] | 2020-08-05T01:08:32 | 2020-08-05T01:21:37 | 2020-08-05T01:21:36 |
NONE
| null | null | null |
Previously, I was writing TFRecords manually to GCP bucket with : `with tf.io.TFRecordWriter('gs://my_bucket/x.tfrecord')`
Since `0.4.0` is out with the `export()` function, I tried it. But it seems TFRecords cannot be directly written to GCP bucket.
`dataset.export('local.tfrecord')` works fine,
but `dataset.export('gs://my_bucket/x.tfrecord')` does not work.
There is no error message, I just can't find the file on my bucket...
---
Looking at the code, `nlp` is using `tf.data.experimental.TFRecordWriter`, while I was using `tf.io.TFRecordWriter`.
**What's the difference between those 2 ? How can I write TFRecords files directly to GCP bucket ?**
@jarednielsen @lhoestq
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/478/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/478/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/477
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/477/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/477/comments
|
https://api.github.com/repos/huggingface/datasets/issues/477/events
|
https://github.com/huggingface/datasets/issues/477
| 673,142,143 |
MDU6SXNzdWU2NzMxNDIxNDM=
| 477 |
Overview.ipynb throws exceptions with nlp 0.4.0
|
{
"login": "mandy-li",
"id": 23109219,
"node_id": "MDQ6VXNlcjIzMTA5MjE5",
"avatar_url": "https://avatars.githubusercontent.com/u/23109219?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/mandy-li",
"html_url": "https://github.com/mandy-li",
"followers_url": "https://api.github.com/users/mandy-li/followers",
"following_url": "https://api.github.com/users/mandy-li/following{/other_user}",
"gists_url": "https://api.github.com/users/mandy-li/gists{/gist_id}",
"starred_url": "https://api.github.com/users/mandy-li/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/mandy-li/subscriptions",
"organizations_url": "https://api.github.com/users/mandy-li/orgs",
"repos_url": "https://api.github.com/users/mandy-li/repos",
"events_url": "https://api.github.com/users/mandy-li/events{/privacy}",
"received_events_url": "https://api.github.com/users/mandy-li/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Thanks for reporting this issue\r\n\r\nThere was a bug where numpy arrays would get returned instead of tensorflow tensors.\r\nThis is fixed on master.\r\n\r\nI tried to re-run the colab and encountered this error instead:\r\n\r\n```\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'to_tensor'\r\n```\r\n\r\nThis is because the dataset returns a Tensor and not a RaggedTensor.\r\nBut I think we should always return a RaggedTensor unless the length of the sequence is fixed (it that case they can be stack into a Tensor).",
"Hi, I got another error (on Colab):\r\n\r\n```python\r\n# You can read a few attributes of the datasets before loading them (they are python dataclasses)\r\nfrom dataclasses import asdict\r\n\r\nfor key, value in asdict(datasets[6]).items():\r\n print('👉 ' + key + ': ' + str(value))\r\n\r\n---------------------------------------------------------------------------\r\n\r\nTypeError Traceback (most recent call last)\r\n\r\n<ipython-input-6-b8ace6c227a2> in <module>()\r\n 2 from dataclasses import asdict\r\n 3 \r\n----> 4 for key, value in asdict(datasets[6]).items():\r\n 5 print('👉 ' + key + ': ' + str(value))\r\n\r\n/usr/local/lib/python3.6/dist-packages/dataclasses.py in asdict(obj, dict_factory)\r\n 1008 \"\"\"\r\n 1009 if not _is_dataclass_instance(obj):\r\n-> 1010 raise TypeError(\"asdict() should be called on dataclass instances\")\r\n 1011 return _asdict_inner(obj, dict_factory)\r\n 1012 \r\n\r\nTypeError: asdict() should be called on dataclass instances\r\n```",
"Indeed we'll update the cola with the new release coming up this week."
] | 2020-08-04T23:18:15 | 2021-08-03T06:02:15 | 2021-08-03T06:02:15 |
NONE
| null | null | null |
with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-48907f2ad433> in <module>
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
<ipython-input-5-48907f2ad433> in <dictcomp>(.0)
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
AttributeError: 'numpy.ndarray' object has no attribute 'to_tensor'
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/477/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/477/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/474
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/474/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/474/comments
|
https://api.github.com/repos/huggingface/datasets/issues/474/events
|
https://github.com/huggingface/datasets/issues/474
| 672,407,330 |
MDU6SXNzdWU2NzI0MDczMzA=
| 474 |
test_load_real_dataset when config has BUILDER_CONFIGS that matter
|
{
"login": "marcotcr",
"id": 698010,
"node_id": "MDQ6VXNlcjY5ODAxMA==",
"avatar_url": "https://avatars.githubusercontent.com/u/698010?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/marcotcr",
"html_url": "https://github.com/marcotcr",
"followers_url": "https://api.github.com/users/marcotcr/followers",
"following_url": "https://api.github.com/users/marcotcr/following{/other_user}",
"gists_url": "https://api.github.com/users/marcotcr/gists{/gist_id}",
"starred_url": "https://api.github.com/users/marcotcr/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/marcotcr/subscriptions",
"organizations_url": "https://api.github.com/users/marcotcr/orgs",
"repos_url": "https://api.github.com/users/marcotcr/repos",
"events_url": "https://api.github.com/users/marcotcr/events{/privacy}",
"received_events_url": "https://api.github.com/users/marcotcr/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`\r\n\r\nAs mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in BUILDER_CONFIGS",
"This was fixed in #527 \r\n\r\nClosing this one, but feel free to re-open if you have other questions"
] | 2020-08-03T23:46:36 | 2020-09-07T14:53:13 | 2020-09-07T14:53:13 |
NONE
| null | null | null |
It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.
I think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https://github.com/huggingface/nlp/blob/master/tests/test_dataset_common.py#L200)). This causes [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.
For an example, you can try running the test for lince:
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`
which yields
> E TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column'
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/474/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/474/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/469
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/469/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/469/comments
|
https://api.github.com/repos/huggingface/datasets/issues/469/events
|
https://github.com/huggingface/datasets/issues/469
| 671,876,963 |
MDU6SXNzdWU2NzE4NzY5NjM=
| 469 |
invalid data type 'str' at _convert_outputs in arrow_dataset.py
|
{
"login": "Murgates",
"id": 30617486,
"node_id": "MDQ6VXNlcjMwNjE3NDg2",
"avatar_url": "https://avatars.githubusercontent.com/u/30617486?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/Murgates",
"html_url": "https://github.com/Murgates",
"followers_url": "https://api.github.com/users/Murgates/followers",
"following_url": "https://api.github.com/users/Murgates/following{/other_user}",
"gists_url": "https://api.github.com/users/Murgates/gists{/gist_id}",
"starred_url": "https://api.github.com/users/Murgates/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/Murgates/subscriptions",
"organizations_url": "https://api.github.com/users/Murgates/orgs",
"repos_url": "https://api.github.com/users/Murgates/repos",
"events_url": "https://api.github.com/users/Murgates/events{/privacy}",
"received_events_url": "https://api.github.com/users/Murgates/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)` (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the list of columns you want to keep (`input_ids` for example)",
"Hello . Yes, I did set the output format as below for the two columns \r\n\r\n `train_dataset.set_format('torch',columns=['Text','Label'])`\r\n ",
"I think you're having this issue because you try to format strings as pytorch tensors, which is not possible.\r\nIndeed by having \"Text\" in `columns=['Text','Label']`, you try to convert the text values to pytorch tensors.\r\n\r\nInstead I recommend you to first tokenize your dataset using a tokenizer from transformers. For example\r\n\r\n```python\r\nfrom transformers import BertTokenizer\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ntrain_dataset.map(lambda x: tokenizer(x[\"Text\"]), batched=True)\r\ntrain_dataset.set_format(\"torch\", column=[\"input_ids\"])\r\n```\r\n\r\nAnother way to fix your issue would be to not set the format to pytorch, and leave the dataset as it is by default. In that case, the strings are returned normally when you get examples from your dataloader. It means that you would have to tokenize the examples in the training loop (or using a data collator) though.\r\n\r\nLet me know if you have other questions",
"Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\nI dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n\r\n\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-145-ca218223c9fc> in <module>()\r\n----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n 2 val_f1 = f1_score_func(predictions, true_val)\r\n 3 tqdm.write(f'Validation loss: {val_loss}')\r\n 4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n\r\n6 frames\r\n/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataset.py in <genexpr>(.0)\r\n 160 \r\n 161 def __getitem__(self, index):\r\n--> 162 return tuple(tensor[index] for tensor in self.tensors)\r\n 163 \r\n 164 def __len__(self):\r\n\r\nTypeError: new(): invalid data type 'str' ",
"> Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\n> I dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n> \r\n> TypeError Traceback (most recent call last)\r\n> in ()\r\n> ----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n> 2 val_f1 = f1_score_func(predictions, true_val)\r\n> 3 tqdm.write(f'Validation loss: {val_loss}')\r\n> 4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n> \r\n> 6 frames\r\n> /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataset.py in (.0)\r\n> 160\r\n> 161 def **getitem**(self, index):\r\n> --> 162 return tuple(tensor[index] for tensor in self.tensors)\r\n> 163\r\n> 164 def **len**(self):\r\n> \r\n> TypeError: new(): invalid data type 'str'\r\n\r\nI got the same error and fix it .\r\nyou can check your input where there may be string contained.\r\nsuch as\r\n```\r\na = [1,2,3,4,'<unk>']\r\ntorch.tensor(a)\r\n```",
"I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?",
"> I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?\r\n\r\ni'm sorry that i met this issue in another place (not in huggingface repo). ",
"@akhilkapil do you have strings in your dataset ? When you set the dataset format to \"pytorch\" you should exclude columns with strings as pytorch can't make tensors out of strings",
"Closing due to inactivity."
] | 2020-08-03T07:48:29 | 2023-07-20T15:54:17 | 2023-07-20T15:54:17 |
NONE
| null | null | null |
I trying to build multi label text classifier model using Transformers lib.
I'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error
File "C:\***\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
I'm using pyarrow 1.0.0. And I have simple custom data set with Text and Integer Label.
Ex: Data
Text , Label #Column Header
I'm facing an Network issue, 1
I forgot my password, 2
Error StackTrace:
File "C:\**\transformers\trainer.py", line 492, in train
for step, inputs in enumerate(epoch_iterator):
File "C:\**\tqdm\std.py", line 1104, in __iter__
for obj in iterable:
File "C:\**\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\**\torch\utils\data\dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\nlp\arrow_dataset.py", line 414, in __getitem__
output_all_columns=self._output_all_columns,
File "C:\**\nlp\arrow_dataset.py", line 403, in _getitem
outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns
File "C:\**\nlp\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/469/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/469/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/468
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/468/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/468/comments
|
https://api.github.com/repos/huggingface/datasets/issues/468/events
|
https://github.com/huggingface/datasets/issues/468
| 671,622,441 |
MDU6SXNzdWU2NzE2MjI0NDE=
| 468 |
UnicodeDecodeError while loading PAN-X task of XTREME dataset
|
{
"login": "lewtun",
"id": 26859204,
"node_id": "MDQ6VXNlcjI2ODU5MjA0",
"avatar_url": "https://avatars.githubusercontent.com/u/26859204?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lewtun",
"html_url": "https://github.com/lewtun",
"followers_url": "https://api.github.com/users/lewtun/followers",
"following_url": "https://api.github.com/users/lewtun/following{/other_user}",
"gists_url": "https://api.github.com/users/lewtun/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lewtun/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lewtun/subscriptions",
"organizations_url": "https://api.github.com/users/lewtun/orgs",
"repos_url": "https://api.github.com/users/lewtun/repos",
"events_url": "https://api.github.com/users/lewtun/events{/privacy}",
"received_events_url": "https://api.github.com/users/lewtun/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the future.",
"I'm happy to tackle the broader problem - will open a PR when it's ready!",
"That would be awesome!",
"I've created a simple function that seems to do the trick:\r\n\r\n```python\r\ndef apply_encoding_on_file_open(filepath: str):\r\n \"\"\"Apply UTF-8 encoding for all instances where a non-binary file is opened.\"\"\"\r\n \r\n with open(filepath, 'r', encoding='utf-8') as input_file:\r\n regexp = re.compile(r\"\"\"\r\n (?!.*\\b(?:encoding|rb|wb|wb+|ab|ab+)\\b)\r\n (open)\r\n \\((.*)\\)\r\n \"\"\")\r\n input_text = input_file.read()\r\n match = regexp.search(input_text)\r\n \r\n if match:\r\n print('Found match!', match.group())\r\n # append utf-8 encoding to matching groups in-place\r\n output = regexp.sub(lambda m: m.group()[:-1]+', encoding=\"utf-8\")', input_text)\r\n with open(filepath, 'w', encoding='utf-8') as output_file:\r\n output_file.write(output)\r\n else:\r\n print(\"No match found!\")\r\n```\r\n\r\nThe regexp does a negative lookahead to avoid matching on cases where the encoding is already specified or when binary files are involved.\r\n\r\nFrom an implementation perspective:\r\n\r\n* Would it make sense to include this function in `nlp-cli` so that we can run something like\r\n```\r\nnlp-cli fix_encoding path/to/folder\r\n```\r\nand the command recursively fixes all files in the target?\r\n* What is the desired behaviour in the CI test? Here we could either have a simple script that we run as a `job` in the CI and raises an error if a missing encoding is detected. Alternatively we could incorporate this behaviour into the CLI and run that in the CI.\r\n\r\nPlease let me know what you prefer among the alternatives.\r\n",
"I realised I was overthinking the problem, so decided to just run the regexp over the codebase and make the PR. In other words, we can ignore my comments about using the CLI 😸 "
] | 2020-08-02T14:05:10 | 2020-08-20T08:16:08 | 2020-08-20T08:16:08 |
MEMBER
| null | null | null |
Hi 🤗 team!
## Description of the problem
I'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset:
```
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-5-1d61f439b843> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
528 ignore_verifications = ignore_verifications or save_infos
529 # Download/copy dataset processing script
--> 530 module_path, hash = prepare_module(path, download_config=download_config, dataset=True)
531
532 # Get dataset builder class from the processing script
/usr/local/lib/python3.6/dist-packages/nlp/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)
265
266 # Download external imports if needed
--> 267 imports = get_imports(local_path)
268 local_imports = []
269 library_imports = []
/usr/local/lib/python3.6/dist-packages/nlp/load.py in get_imports(file_path)
156 lines = []
157 with open(file_path, mode="r") as f:
--> 158 lines.extend(f.readlines())
159
160 logger.info("Checking %s for additional imports.", file_path)
/usr/lib/python3.6/encodings/ascii.py in decode(self, input, final)
24 class IncrementalDecoder(codecs.IncrementalDecoder):
25 def decode(self, input, final=False):
---> 26 return codecs.ascii_decode(input, self.errors)[0]
27
28 class StreamWriter(Codec,codecs.StreamWriter):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)
```
## Steps to reproduce
Install from nlp's master branch
```python
pip install git+https://github.com/huggingface/nlp.git
```
then run
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
```
## OS / platform details
- `nlp` version: latest from master
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: False
## Proposed solution
Either change [line 762](https://github.com/huggingface/nlp/blob/7ada00b1d62f94eee22a7df38c6b01e3f27194b7/datasets/xtreme/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:
```
# old
with open(filepath) as f
# new
with open(filepath, encoding='utf-8') as f
```
or raise a warning that suggests setting the locale explicitly, e.g.
```python
import locale
locale.setlocale(locale.LC_ALL, 'C.UTF-8')
```
I have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix!
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/468/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/468/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/445
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/445/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/445/comments
|
https://api.github.com/repos/huggingface/datasets/issues/445/events
|
https://github.com/huggingface/datasets/issues/445
| 666,836,658 |
MDU6SXNzdWU2NjY4MzY2NTg=
| 445 |
DEFAULT_TOKENIZER import error in sacrebleu
|
{
"login": "idoh",
"id": 5303103,
"node_id": "MDQ6VXNlcjUzMDMxMDM=",
"avatar_url": "https://avatars.githubusercontent.com/u/5303103?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/idoh",
"html_url": "https://github.com/idoh",
"followers_url": "https://api.github.com/users/idoh/followers",
"following_url": "https://api.github.com/users/idoh/following{/other_user}",
"gists_url": "https://api.github.com/users/idoh/gists{/gist_id}",
"starred_url": "https://api.github.com/users/idoh/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/idoh/subscriptions",
"organizations_url": "https://api.github.com/users/idoh/orgs",
"repos_url": "https://api.github.com/users/idoh/repos",
"events_url": "https://api.github.com/users/idoh/events{/privacy}",
"received_events_url": "https://api.github.com/users/idoh/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"This issue was resolved by #447 "
] | 2020-07-28T07:31:30 | 2020-07-28T12:58:56 | 2020-07-28T12:58:56 |
CONTRIBUTOR
| null | null | null |
Latest Version 0.3.0
When loading the metric "sacrebleu" there is an import error due to the wrong path
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/445/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/445/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/444
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/444/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/444/comments
|
https://api.github.com/repos/huggingface/datasets/issues/444/events
|
https://github.com/huggingface/datasets/issues/444
| 666,280,842 |
MDU6SXNzdWU2NjYyODA4NDI=
| 444 |
Keep loading old file even I specify a new file in load_dataset
|
{
"login": "joshhu",
"id": 10594453,
"node_id": "MDQ6VXNlcjEwNTk0NDUz",
"avatar_url": "https://avatars.githubusercontent.com/u/10594453?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/joshhu",
"html_url": "https://github.com/joshhu",
"followers_url": "https://api.github.com/users/joshhu/followers",
"following_url": "https://api.github.com/users/joshhu/following{/other_user}",
"gists_url": "https://api.github.com/users/joshhu/gists{/gist_id}",
"starred_url": "https://api.github.com/users/joshhu/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/joshhu/subscriptions",
"organizations_url": "https://api.github.com/users/joshhu/orgs",
"repos_url": "https://api.github.com/users/joshhu/repos",
"events_url": "https://api.github.com/users/joshhu/events{/privacy}",
"received_events_url": "https://api.github.com/users/joshhu/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067388877,
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug",
"name": "dataset bug",
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library"
}
] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"Same here !",
"This is the only fix I could come up with without touching the repo's code.\r\n```python\r\nfrom nlp.builder import FORCE_REDOWNLOAD\r\ndataset = load_dataset('csv', data_file='./a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')\r\n```\r\nYou'll have to change the version each time you want to load a different csv file.\r\nIf you're willing to add a ```print```, you can go to ```nlp.load``` and add ```print(builder_instance.cache_dir)``` right before the ```return ds``` in the ```load_dataset``` method. It'll print the cache folder, and you'll just have to erase it (and then you won't need the change here above)."
] | 2020-07-27T13:08:06 | 2020-07-29T13:57:22 | 2020-07-29T13:57:22 |
NONE
| null | null | null |
I used load a file called 'a.csv' by
```
dataset = load_dataset('csv', data_file='./a.csv')
```
And after a while, I tried to load another csv called 'b.csv'
```
dataset = load_dataset('csv', data_file='./b.csv')
```
However, the new dataset seems to remain the old 'a.csv' and not loading new csv file.
Even worse, after I load a.csv, the load_dataset function keeps loading the 'a.csv' afterward.
Is this a cache problem?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/444/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/444/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/443
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/443/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/443/comments
|
https://api.github.com/repos/huggingface/datasets/issues/443/events
|
https://github.com/huggingface/datasets/issues/443
| 666,246,716 |
MDU6SXNzdWU2NjYyNDY3MTY=
| 443 |
Cannot unpickle saved .pt dataset with torch.save()/load()
|
{
"login": "vegarab",
"id": 24683907,
"node_id": "MDQ6VXNlcjI0NjgzOTA3",
"avatar_url": "https://avatars.githubusercontent.com/u/24683907?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/vegarab",
"html_url": "https://github.com/vegarab",
"followers_url": "https://api.github.com/users/vegarab/followers",
"following_url": "https://api.github.com/users/vegarab/following{/other_user}",
"gists_url": "https://api.github.com/users/vegarab/gists{/gist_id}",
"starred_url": "https://api.github.com/users/vegarab/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/vegarab/subscriptions",
"organizations_url": "https://api.github.com/users/vegarab/orgs",
"repos_url": "https://api.github.com/users/vegarab/repos",
"events_url": "https://api.github.com/users/vegarab/events{/privacy}",
"received_events_url": "https://api.github.com/users/vegarab/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"This seems to be fixed in a non-released version. \r\n\r\nInstalling nlp from source\r\n```\r\ngit clone https://github.com/huggingface/nlp\r\ncd nlp\r\npip install .\r\n```\r\nsolves the issue. "
] | 2020-07-27T12:13:37 | 2020-07-27T13:05:11 | 2020-07-27T13:05:11 |
CONTRIBUTOR
| null | null | null |
Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling:
```python
>>> import torch
>>> import nlp
>>> squad = nlp.load_dataset("squad.py", split="train")
>>> squad
Dataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype='string', id=None)}, num_rows: 87599)
>>> squad = squad.map(create_features, batched=True)
>>> squad.set_format(type="torch", columns=["source_ids", "target_ids", "attention_mask"])
>>> torch.save(squad, "squad.pt")
>>> squad_pt = torch.load("squad.pt")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 593, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 773, in _legacy_load
result = unpickler.load()
File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/splits.py", line 493, in __setitem__
raise ValueError("Cannot add elem. Use .add() instead.")
ValueError: Cannot add elem. Use .add() instead.
```
where `create_features` is a function that tokenizes the data using `batch_encode_plus` and returns a Dict with `input_ids`, `target_ids` and `attention_mask`.
```python
def create_features(batch):
source_text_encoding = tokenizer.batch_encode_plus(
batch["source_text"],
max_length=max_source_length,
pad_to_max_length=True,
truncation=True)
target_text_encoding = tokenizer.batch_encode_plus(
batch["target_text"],
max_length=max_target_length,
pad_to_max_length=True,
truncation=True)
features = {
"source_ids": source_text_encoding["input_ids"],
"target_ids": target_text_encoding["input_ids"],
"attention_mask": source_text_encoding["attention_mask"]
}
return features
```
I found a similar issue in [issue 5267 in the huggingface/transformers repo](https://github.com/huggingface/transformers/issues/5267) which was solved by downgrading to `nlp==0.2.0`. That did not solve this problem, however.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/443/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/443/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/442
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/442/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/442/comments
|
https://api.github.com/repos/huggingface/datasets/issues/442/events
|
https://github.com/huggingface/datasets/issues/442
| 666,201,810 |
MDU6SXNzdWU2NjYyMDE4MTA=
| 442 |
[Suggestion] Glue Diagnostic Data with Labels
|
{
"login": "ggbetz",
"id": 3662782,
"node_id": "MDQ6VXNlcjM2NjI3ODI=",
"avatar_url": "https://avatars.githubusercontent.com/u/3662782?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ggbetz",
"html_url": "https://github.com/ggbetz",
"followers_url": "https://api.github.com/users/ggbetz/followers",
"following_url": "https://api.github.com/users/ggbetz/following{/other_user}",
"gists_url": "https://api.github.com/users/ggbetz/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ggbetz/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ggbetz/subscriptions",
"organizations_url": "https://api.github.com/users/ggbetz/orgs",
"repos_url": "https://api.github.com/users/ggbetz/repos",
"events_url": "https://api.github.com/users/ggbetz/events{/privacy}",
"received_events_url": "https://api.github.com/users/ggbetz/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067401494,
"node_id": "MDU6TGFiZWwyMDY3NDAxNDk0",
"url": "https://api.github.com/repos/huggingface/datasets/labels/Dataset%20discussion",
"name": "Dataset discussion",
"color": "72f99f",
"default": false,
"description": "Discussions on the datasets"
}
] |
open
| false | null |
[] | null |
[] | 2020-07-27T10:59:58 | 2020-08-24T15:13:20 | null |
NONE
| null | null | null |
Hello! First of all, thanks for setting up this useful project!
I've just realised you provide the the [Glue Diagnostics Data](https://huggingface.co/nlp/viewer/?dataset=glue&config=ax) without labels, indicating in the `GlueConfig` that you've only a test set.
Yet, the data with labels is available, too (see also [here](https://gluebenchmark.com/diagnostics#introduction)):
https://www.dropbox.com/s/ju7d95ifb072q9f/diagnostic-full.tsv?dl=1
Have you considered incorporating it?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/442/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/442/timeline
| null | null |
https://api.github.com/repos/huggingface/datasets/issues/439
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/439/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/439/comments
|
https://api.github.com/repos/huggingface/datasets/issues/439/events
|
https://github.com/huggingface/datasets/issues/439
| 665,964,673 |
MDU6SXNzdWU2NjU5NjQ2NzM=
| 439 |
Issues: Adding a FAISS or Elastic Search index to a Dataset
|
{
"login": "nsankar",
"id": 431890,
"node_id": "MDQ6VXNlcjQzMTg5MA==",
"avatar_url": "https://avatars.githubusercontent.com/u/431890?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/nsankar",
"html_url": "https://github.com/nsankar",
"followers_url": "https://api.github.com/users/nsankar/followers",
"following_url": "https://api.github.com/users/nsankar/following{/other_user}",
"gists_url": "https://api.github.com/users/nsankar/gists{/gist_id}",
"starred_url": "https://api.github.com/users/nsankar/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/nsankar/subscriptions",
"organizations_url": "https://api.github.com/users/nsankar/orgs",
"repos_url": "https://api.github.com/users/nsankar/repos",
"events_url": "https://api.github.com/users/nsankar/events{/privacy}",
"received_events_url": "https://api.github.com/users/nsankar/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).\r\n\r\nMoreover all the indexing features will also be available in the next release of `nlp`.",
"@lhoestq Thanks for the info ",
"@lhoestq I tried installing transformer from the master branch. Python imports for DPR again didnt' work. Anyways, Looking forward to trying it in the next release of nlp ",
"@nsankar have you tried with the latest version of the library?",
"@yjernite it worked. Thanks"
] | 2020-07-27T04:25:17 | 2020-10-28T01:46:24 | 2020-10-28T01:46:24 |
NONE
| null | null | null |
It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https://huggingface.co/nlp/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab. Is there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/439/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/439/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/438
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/438/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/438/comments
|
https://api.github.com/repos/huggingface/datasets/issues/438/events
|
https://github.com/huggingface/datasets/issues/438
| 665,865,490 |
MDU6SXNzdWU2NjU4NjU0OTA=
| 438 |
New Datasets: IWSLT15+, ITTB
|
{
"login": "sshleifer",
"id": 6045025,
"node_id": "MDQ6VXNlcjYwNDUwMjU=",
"avatar_url": "https://avatars.githubusercontent.com/u/6045025?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/sshleifer",
"html_url": "https://github.com/sshleifer",
"followers_url": "https://api.github.com/users/sshleifer/followers",
"following_url": "https://api.github.com/users/sshleifer/following{/other_user}",
"gists_url": "https://api.github.com/users/sshleifer/gists{/gist_id}",
"starred_url": "https://api.github.com/users/sshleifer/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/sshleifer/subscriptions",
"organizations_url": "https://api.github.com/users/sshleifer/orgs",
"repos_url": "https://api.github.com/users/sshleifer/repos",
"events_url": "https://api.github.com/users/sshleifer/events{/privacy}",
"received_events_url": "https://api.github.com/users/sshleifer/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067376369,
"node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request",
"name": "dataset request",
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset"
}
] |
open
| false | null |
[] | null |
[
"Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?\r\nThe tutorial on writing a new dataset loading script is here: https://huggingface.co/nlp/add_dataset.html\r\nAnd the part on how to share a new dataset is here: https://huggingface.co/nlp/share_dataset.html",
"Hi @sshleifer, I'm trying to add IWSLT using the link you provided but the download urls are not working. Only `[en, de]` pair is working. For others language pairs it throws a `404` error.\r\n\r\n"
] | 2020-07-26T21:43:04 | 2020-08-24T15:12:15 | null |
CONTRIBUTOR
| null | null | null |
**Links:**
[iwslt](https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/iwslt.html)
Don't know if that link is up to date.
[ittb](http://www.cfilt.iitb.ac.in/iitb_parallel/)
**Motivation**: replicate mbart finetuning results (table below)
For future readers, we already have the following language pairs in the wmt namespaces:
```
wmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']
wmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']
wmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']
wmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']
wmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']
wmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/438/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/438/timeline
| null | null |
https://api.github.com/repos/huggingface/datasets/issues/436
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/436/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/436/comments
|
https://api.github.com/repos/huggingface/datasets/issues/436/events
|
https://github.com/huggingface/datasets/issues/436
| 665,582,167 |
MDU6SXNzdWU2NjU1ODIxNjc=
| 436 |
Google Colab - load_dataset - PyArrow exception
|
{
"login": "nsankar",
"id": 431890,
"node_id": "MDQ6VXNlcjQzMTg5MA==",
"avatar_url": "https://avatars.githubusercontent.com/u/431890?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/nsankar",
"html_url": "https://github.com/nsankar",
"followers_url": "https://api.github.com/users/nsankar/followers",
"following_url": "https://api.github.com/users/nsankar/following{/other_user}",
"gists_url": "https://api.github.com/users/nsankar/gists{/gist_id}",
"starred_url": "https://api.github.com/users/nsankar/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/nsankar/subscriptions",
"organizations_url": "https://api.github.com/users/nsankar/orgs",
"repos_url": "https://api.github.com/users/nsankar/repos",
"events_url": "https://api.github.com/users/nsankar/events{/privacy}",
"received_events_url": "https://api.github.com/users/nsankar/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq ",
"+1! this is the reason our tests are failing at [TextAttack](https://github.com/QData/TextAttack) \r\n\r\n(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case we'll just wait for you all to update)",
"Came to raise this issue, great to see other already have and it's being fixed so soon!\r\n\r\nAs an aside, since no one wrote this already, it seems like the version check only looks at the second part of the version number making sure it is >16, but pyarrow newest version is 1.0.0 so the second past is 0!",
"> Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq\r\n\r\nYes definitely",
"please fix this on pypi! @lhoestq ",
"Is this issue fixed ?",
"We’ll release the new version later today. Apologies for the delay.",
"I just pushed the new version on pypi :)",
"Thanks for the update."
] | 2020-07-25T13:05:20 | 2020-08-20T08:08:18 | 2020-08-20T08:08:18 |
NONE
| null | null | null |
With latest PyArrow 1.0.0 installed, I get the following exception . Restarting colab has the same issue
ImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.
The error goes only when I install version 0.16.0
i.e. !pip install pyarrow==0.16.0
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/436/reactions",
"total_count": 1,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 1
}
|
https://api.github.com/repos/huggingface/datasets/issues/436/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/435
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/435/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/435/comments
|
https://api.github.com/repos/huggingface/datasets/issues/435/events
|
https://github.com/huggingface/datasets/issues/435
| 665,507,141 |
MDU6SXNzdWU2NjU1MDcxNDE=
| 435 |
ImportWarning for pyarrow 1.0.0
|
{
"login": "HanGuo97",
"id": 18187806,
"node_id": "MDQ6VXNlcjE4MTg3ODA2",
"avatar_url": "https://avatars.githubusercontent.com/u/18187806?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/HanGuo97",
"html_url": "https://github.com/HanGuo97",
"followers_url": "https://api.github.com/users/HanGuo97/followers",
"following_url": "https://api.github.com/users/HanGuo97/following{/other_user}",
"gists_url": "https://api.github.com/users/HanGuo97/gists{/gist_id}",
"starred_url": "https://api.github.com/users/HanGuo97/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/HanGuo97/subscriptions",
"organizations_url": "https://api.github.com/users/HanGuo97/orgs",
"repos_url": "https://api.github.com/users/HanGuo97/repos",
"events_url": "https://api.github.com/users/HanGuo97/events{/privacy}",
"received_events_url": "https://api.github.com/users/HanGuo97/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"This was fixed in #434 \r\nWe'll do a release later this week to include this fix.\r\nThanks for reporting",
"I dont know if the fix was made but the problem is still present : \r\nInstaled with pip : NLP 0.3.0 // pyarrow 1.0.0 \r\nOS : archlinux with kernel zen 5.8.5",
"Yes it was fixed in `nlp>=0.4.0`\r\nYou can update with pip",
"Sorry, I didn't got the updated version, all is now working perfectly thanks"
] | 2020-07-25T03:44:39 | 2020-09-08T17:57:15 | 2020-08-03T16:37:32 |
NONE
| null | null | null |
The following PR raised ImportWarning at `pyarrow ==1.0.0` https://github.com/huggingface/nlp/pull/265/files
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/435/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/435/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/433
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/433/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/433/comments
|
https://api.github.com/repos/huggingface/datasets/issues/433/events
|
https://github.com/huggingface/datasets/issues/433
| 665,311,025 |
MDU6SXNzdWU2NjUzMTEwMjU=
| 433 |
How to reuse functionality of a (generic) dataset?
|
{
"login": "ArneBinder",
"id": 3375489,
"node_id": "MDQ6VXNlcjMzNzU0ODk=",
"avatar_url": "https://avatars.githubusercontent.com/u/3375489?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ArneBinder",
"html_url": "https://github.com/ArneBinder",
"followers_url": "https://api.github.com/users/ArneBinder/followers",
"following_url": "https://api.github.com/users/ArneBinder/following{/other_user}",
"gists_url": "https://api.github.com/users/ArneBinder/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ArneBinder/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ArneBinder/subscriptions",
"organizations_url": "https://api.github.com/users/ArneBinder/orgs",
"repos_url": "https://api.github.com/users/ArneBinder/repos",
"events_url": "https://api.github.com/users/ArneBinder/events{/privacy}",
"received_events_url": "https://api.github.com/users/ArneBinder/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https://github.com/huggingface/nlp/tree/master/datasets/csv\r\n- json: https://github.com/huggingface/nlp/tree/master/datasets/json\r\n- text: https://github.com/huggingface/nlp/tree/master/datasets/text\r\n\r\nYou can find more details about this way to load datasets here in the documentation: https://huggingface.co/nlp/loading_datasets.html#from-local-files\r\n\r\nMaybe your brat loading script could be shared in a similar fashion?",
"> Maybe your brat loading script could be shared in a similar fashion?\r\n\r\n@thomwolf that was also my first idea and I think I will tackle that in the next days. I separated the code and created a real abstract class `AbstractBrat` to allow to inherit from that (I've just seen that the dataset_loader loads the first non abstract class), now `Brat` is very similar in its functionality to https://github.com/huggingface/nlp/tree/master/datasets/text but inherits from `AbstractBrat`.\r\n\r\nHowever, it is still not clear to me how to add a specific dataset (as explained in https://huggingface.co/nlp/add_dataset.html) to your repo that uses this format/abstract class, i.e. re-using the `features` entry of the `DatasetInfo` object and `_generate_examples()`. Again, by doing so, the only remaining entries/functions to define would be `_DESCRIPTION`, `_CITATION`, `homepage` and `_URL` (which is all copy-paste stuff) and `_split_generators()`.\r\n \r\nIn a lack of better ideas, I tried sth like below, but of course it does not work outside `nlp` (`AbstractBrat` is currently defined in [datasets/brat.py](https://github.com/ArneBinder/nlp/blob/5e81fb8710546ee7be3353a7f02a3045e9a8351e/datasets/brat/brat.py)):\r\n```python\r\nfrom __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\n\r\nimport nlp\r\n\r\nfrom datasets.brat.brat import AbstractBrat\r\n\r\n_CITATION = \"\"\"\r\n@inproceedings{lauscher2018b,\r\n title = {An argument-annotated corpus of scientific publications},\r\n booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},\r\n publisher = {Association for Computational Linguistics},\r\n author = {Lauscher, Anne and Glava\\v{s}, Goran and Ponzetto, Simone Paolo},\r\n address = {Brussels, Belgium},\r\n year = {2018},\r\n pages = {40–46}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThis dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \r\nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \r\npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \r\nscientific writing.\r\n\"\"\"\r\n\r\n_URL = \"http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip\"\r\n\r\n\r\nclass Sciarg(AbstractBrat):\r\n\r\n VERSION = nlp.Version(\"1.0.0\")\r\n\r\n def _info(self):\r\n\r\n brat_features = super()._info().features\r\n return nlp.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=_DESCRIPTION,\r\n # nlp.features.FeatureConnectors\r\n features=brat_features,\r\n # If there's a common (input, target) tuple from the features,\r\n # specify them here. They'll be used if as_supervised=True in\r\n # builder.as_dataset.\r\n #supervised_keys=None,\r\n # Homepage of the dataset for documentation\r\n homepage=\"https://github.com/anlausch/ArguminSci\",\r\n citation=_CITATION,\r\n )\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n # TODO: Downloads the data and defines the splits\r\n # dl_manager is a nlp.download.DownloadManager that can be used to\r\n # download and extract URLs\r\n dl_dir = dl_manager.download_and_extract(_URL)\r\n data_dir = os.path.join(dl_dir, \"compiled_corpus\")\r\n print(f'data_dir: {data_dir}')\r\n return [\r\n nlp.SplitGenerator(\r\n name=nlp.Split.TRAIN,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n \"directory\": data_dir,\r\n },\r\n ),\r\n ]\r\n``` \r\n\r\nNevertheless, many thanks for tackling the dataset accessibility problem with this great library!",
"As temporary fix I've created [ArneBinder/nlp-formats](https://github.com/ArneBinder/nlp-formats) (contributions welcome).",
"Hi! You can either copy&paste the builder script and import the builder from there or use `datasets.load_dataset_builder` inside the script and call the methods of the returned builder object."
] | 2020-07-24T17:27:37 | 2022-10-04T17:59:34 | 2022-10-04T17:59:33 |
NONE
| null | null | null |
I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https://brat.nlplab.org/standoff.html), [dataset code](https://github.com/ArneBinder/nlp/blob/brat/datasets/brat/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?
In my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/433/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/433/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/426
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/426/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/426/comments
|
https://api.github.com/repos/huggingface/datasets/issues/426/events
|
https://github.com/huggingface/datasets/issues/426
| 664,203,897 |
MDU6SXNzdWU2NjQyMDM4OTc=
| 426 |
[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter
|
{
"login": "timothyjlaurent",
"id": 2000204,
"node_id": "MDQ6VXNlcjIwMDAyMDQ=",
"avatar_url": "https://avatars.githubusercontent.com/u/2000204?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/timothyjlaurent",
"html_url": "https://github.com/timothyjlaurent",
"followers_url": "https://api.github.com/users/timothyjlaurent/followers",
"following_url": "https://api.github.com/users/timothyjlaurent/following{/other_user}",
"gists_url": "https://api.github.com/users/timothyjlaurent/gists{/gist_id}",
"starred_url": "https://api.github.com/users/timothyjlaurent/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/timothyjlaurent/subscriptions",
"organizations_url": "https://api.github.com/users/timothyjlaurent/orgs",
"repos_url": "https://api.github.com/users/timothyjlaurent/repos",
"events_url": "https://api.github.com/users/timothyjlaurent/events{/privacy}",
"received_events_url": "https://api.github.com/users/timothyjlaurent/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] |
closed
| false | null |
[] | null |
[
"Yes that's definitely something we plan to add ^^",
"Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.",
"So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/data/ops/dataset_ops.py#L1623).\r\n\r\nThere, `num_parallel_calls` is turned into a tensor and and fed to `gen_dataset_ops.parallel_map_dataset` where it looks like tensorflow takes over.\r\n\r\nWe could start with something simple like a thread or process pool that `imap`s over some shards.\r\n ",
"Multiprocessing was added in #552 . You can set the number of processes with `.map(..., num_proc=...)`. It also works for `filter`\r\n\r\nClosing this one, but feel free to reo-open if you have other questions",
"@lhoestq Great feature implemented! Do you have plans to add it to official tutorials [Processing data in a Dataset](https://huggingface.co/docs/datasets/processing.html?highlight=save#augmenting-the-dataset)? It took me sometime to find this parallel processing api.",
"Thanks for the heads up !\r\n\r\nI just added a paragraph about multiprocessing:\r\nhttps://huggingface.co/docs/datasets/master/processing.html#multiprocessing"
] | 2020-07-23T05:00:41 | 2021-03-12T09:34:12 | 2020-09-07T14:48:04 |
NONE
| null | null | null |
It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process/thread/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/426/reactions",
"total_count": 3,
"+1": 3,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/426/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/425
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/425/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/425/comments
|
https://api.github.com/repos/huggingface/datasets/issues/425/events
|
https://github.com/huggingface/datasets/issues/425
| 664,029,848 |
MDU6SXNzdWU2NjQwMjk4NDg=
| 425 |
Correct data structure for PAN-X task in XTREME dataset?
|
{
"login": "lewtun",
"id": 26859204,
"node_id": "MDQ6VXNlcjI2ODU5MjA0",
"avatar_url": "https://avatars.githubusercontent.com/u/26859204?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lewtun",
"html_url": "https://github.com/lewtun",
"followers_url": "https://api.github.com/users/lewtun/followers",
"following_url": "https://api.github.com/users/lewtun/following{/other_user}",
"gists_url": "https://api.github.com/users/lewtun/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lewtun/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lewtun/subscriptions",
"organizations_url": "https://api.github.com/users/lewtun/orgs",
"repos_url": "https://api.github.com/users/lewtun/repos",
"events_url": "https://api.github.com/users/lewtun/events{/privacy}",
"received_events_url": "https://api.github.com/users/lewtun/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Thanks for noticing ! This looks more reasonable indeed.\r\nFeel free to open a PR",
"Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file. More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\r\n```json\r\n\"features\":{\r\n \"word\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n },\r\n \"ner_tag\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n },\r\n \"lang\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n }\r\n}\r\n```\r\nTo fit the code above the fields `\"word\"`, `\"ner_tag\"`, and `\"lang\"` would become `\"words\"`, `ner_tags\"` and `\"langs\"`. In addition the `dtype` should be changed from `\"string\"` to `\"list\"`.\r\n\r\n I made this changes but when trying to test this locally with `dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='./data')` I face the issue that the `dataset_info.json` file is always overwritten by a downloaded version with the old settings, which then throws an error because the schema does not match. This makes it hard to test the changes locally. Do you have any suggestions on how to deal with that?\r\n",
"Hi !\r\n\r\nYou have to point to your local script.\r\nFirst clone the repo and then:\r\n\r\n```python\r\ndataset = load_dataset(\"./datasets/xtreme\", \"PAN-X.en\")\r\n```\r\nThe \"xtreme\" directory contains \"xtreme.py\".\r\n\r\nYou also have to change the features definition in the `_info` method. You could use:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": [nlp.Value(\"string\")],\r\n \"ner_tags\": [nlp.Value(\"string\")],\r\n \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nHope this helps !\r\nLet me know if you have other questions.",
"Thanks, I am making progress. I got a new error `NonMatchingSplitsSizesError ` (see traceback below), which I suspect is due to the fact that number of rows in the dataset changed (one row per word --> one row per sentence) as well as the number of bytes due to the slightly updated data structure. \r\n\r\n```python\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=1756492, num_examples=80536, dataset_name='xtreme'), 'recorded': SplitInfo(name='validation', num_bytes=1837109, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='test', num_bytes=1752572, num_examples=80326, dataset_name='xtreme'), 'recorded': SplitInfo(name='test', num_bytes=1833214, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='train', num_bytes=3496832, num_examples=160394, dataset_name='xtreme'), 'recorded': SplitInfo(name='train', num_bytes=3658428, num_examples=20000, dataset_name='xtreme')}]\r\n```\r\nI can fix the error by replacing the values in the `datasets_infos.json` file, which I tested for English. However, to update this for all 40 datasets manually is slightly painful. Is there a better way to update the expected values for all datasets?",
"You can update the json file by calling\r\n```\r\nnlp-cli test ./datasets/xtreme --save_infos --all_configs\r\n```",
"One more thing about features. I mentioned\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": [nlp.Value(\"string\")],\r\n \"ner_tags\": [nlp.Value(\"string\")],\r\n \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nbut it's actually not consistent with the way we write datasets. Something like this is simpler to read and more consistent with the way we define datasets:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": nlp.Sequence(nlp.Value(\"string\")),\r\n \"ner_tags\": nlp.Sequence(nlp.Value(\"string\")),\r\n \"langs\": nlp.Sequence(nlp.Value(\"string\")),\r\n})\r\n```\r\n\r\nSorry about that",
"Closing this since PR #437 fixed the problem and has been merged to `master`. "
] | 2020-07-22T20:29:20 | 2020-08-02T13:30:34 | 2020-08-02T13:30:34 |
MEMBER
| null | null | null |
Hi 🤗 team!
## Description of the problem
Thanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
dataset_train = dataset['train']
```
However, I am not sure that `load_dataset()` is returning the correct data structure for NER.
Currently, every row in `dataset_train` is of the form
```python
{'word': str, 'ner_tag': str, 'lang': str}
```
but I think we actually want something like
```python
{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}
```
so that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.
Indeed, [this line](https://github.com/google-research/xtreme/blob/522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.
## Proposed solution
Replace
```python
with open(filepath) as f:
data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for id_, row in enumerate(data):
if row:
lang, word = row[0].split(":")[0], row[0].split(":")[1]
tag = row[1]
yield id_, {"word": word, "ner_tag": tag, "lang": lang}
```
from [these lines](https://github.com/huggingface/nlp/blob/ce7d3a1d630b78fe27188d1706f3ea980e8eec43/datasets/xtreme/xtreme.py#L881-L887) of the `_generate_examples()` function with something like
```python
guid_index = 1
with open(filepath, encoding="utf-8") as f:
words = []
ner_tags = []
langs = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words:
yield guid_index, {"words": words, "ner_tags": ner_tags, "langs": langs}
guid_index += 1
words = []
ner_tags = []
else:
# pan-x data is tab separated
splits = line.split("\t")
# strip out en: prefix
langs.append(splits[0][:2])
words.append(splits[0][3:])
if len(splits) > 1:
labels.append(splits[-1].replace("\n", ""))
else:
# examples have no label in test set
labels.append("O")
```
If you agree, me or @lvwerra would be happy to implement this and create a PR.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/425/reactions",
"total_count": 2,
"+1": 2,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/425/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/418
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/418/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/418/comments
|
https://api.github.com/repos/huggingface/datasets/issues/418/events
|
https://github.com/huggingface/datasets/issues/418
| 661,914,873 |
MDU6SXNzdWU2NjE5MTQ4NzM=
| 418 |
Addition of google drive links to dl_manager
|
{
"login": "lordtt13",
"id": 35500534,
"node_id": "MDQ6VXNlcjM1NTAwNTM0",
"avatar_url": "https://avatars.githubusercontent.com/u/35500534?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lordtt13",
"html_url": "https://github.com/lordtt13",
"followers_url": "https://api.github.com/users/lordtt13/followers",
"following_url": "https://api.github.com/users/lordtt13/following{/other_user}",
"gists_url": "https://api.github.com/users/lordtt13/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lordtt13/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lordtt13/subscriptions",
"organizations_url": "https://api.github.com/users/lordtt13/orgs",
"repos_url": "https://api.github.com/users/lordtt13/repos",
"events_url": "https://api.github.com/users/lordtt13/events{/privacy}",
"received_events_url": "https://api.github.com/users/lordtt13/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I think the problem is the way you wrote your urls. Try the following structure to see `https://drive.google.com/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq ",
"Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`? it should work with google drive links.\r\n",
"Yes it worked, thank you!"
] | 2020-07-20T14:52:02 | 2020-07-20T15:39:32 | 2020-07-20T15:39:32 |
CONTRIBUTOR
| null | null | null |
Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.
This is the script for me:
```python
class EmoConfig(nlp.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for EmoContext.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(EmoConfig, self).__init__(**kwargs)
_TEST_URL = "https://drive.google.com/file/d/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb/view?usp=sharing"
_TRAIN_URL = "https://drive.google.com/file/d/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X/view?usp=sharing"
class EmoDataset(nlp.GeneratorBasedBuilder):
""" SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 """
VERSION = nlp.Version("1.0.0")
force = False
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION,
features=nlp.Features(
{
"text": nlp.Value("string"),
"label": nlp.features.ClassLabel(names=["others", "happy", "sad", "angry"]),
}
),
supervised_keys=None,
homepage="https://www.aclweb.org/anthology/S19-2005/",
citation=_CITATION,
)
def _get_drive_url(self, url):
base_url = 'https://drive.google.com/uc?id='
split_url = url.split('/')
return base_url + split_url[5]
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
if(not os.path.exists("emo-train.json") or self.force):
gdown.download(self._get_drive_url(_TRAIN_URL), "emo-train.json", quiet = True)
if(not os.path.exists("emo-test.json") or self.force):
gdown.download(self._get_drive_url(_TEST_URL), "emo-test.json", quiet = True)
return [
nlp.SplitGenerator(
name=nlp.Split.TRAIN,
gen_kwargs={
"filepath": "emo-train.json",
"split": "train",
},
),
nlp.SplitGenerator(
name=nlp.Split.TEST,
gen_kwargs={"filepath": "emo-test.json", "split": "test"},
),
]
def _generate_examples(self, filepath, split):
""" Yields examples. """
with open(filepath, 'rb') as f:
data = json.load(f)
for id_, text, label in zip(data["text"].keys(), data["text"].values(), data["Label"].values()):
yield id_, {
"text": text,
"label": label,
}
```
Can someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/418/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/418/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/415
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/415/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/415/comments
|
https://api.github.com/repos/huggingface/datasets/issues/415/events
|
https://github.com/huggingface/datasets/issues/415
| 660,687,076 |
MDU6SXNzdWU2NjA2ODcwNzY=
| 415 |
Something is wrong with WMT 19 kk-en dataset
|
{
"login": "ChenghaoMou",
"id": 32014649,
"node_id": "MDQ6VXNlcjMyMDE0NjQ5",
"avatar_url": "https://avatars.githubusercontent.com/u/32014649?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ChenghaoMou",
"html_url": "https://github.com/ChenghaoMou",
"followers_url": "https://api.github.com/users/ChenghaoMou/followers",
"following_url": "https://api.github.com/users/ChenghaoMou/following{/other_user}",
"gists_url": "https://api.github.com/users/ChenghaoMou/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ChenghaoMou/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ChenghaoMou/subscriptions",
"organizations_url": "https://api.github.com/users/ChenghaoMou/orgs",
"repos_url": "https://api.github.com/users/ChenghaoMou/repos",
"events_url": "https://api.github.com/users/ChenghaoMou/events{/privacy}",
"received_events_url": "https://api.github.com/users/ChenghaoMou/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067388877,
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug",
"name": "dataset bug",
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library"
}
] |
open
| false | null |
[] | null |
[] | 2020-07-19T08:18:51 | 2020-07-20T09:54:26 | null |
NONE
| null | null | null |
The translation in the `train` set does not look right:
```
>>>import nlp
>>>from nlp import load_dataset
>>>dataset = load_dataset('wmt19', 'kk-en')
>>>dataset["train"]["translation"][0]
{'kk': 'Trumpian Uncertainty', 'en': 'Трамптық белгісіздік'}
>>>dataset["validation"]["translation"][0]
{'kk': 'Ақша-несие саясатының сценарийін қайта жазсақ', 'en': 'Rewriting the Monetary-Policy Script'}
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/415/reactions",
"total_count": 1,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 1,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/415/timeline
| null | null |
https://api.github.com/repos/huggingface/datasets/issues/414
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/414/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/414/comments
|
https://api.github.com/repos/huggingface/datasets/issues/414/events
|
https://github.com/huggingface/datasets/issues/414
| 660,654,013 |
MDU6SXNzdWU2NjA2NTQwMTM=
| 414 |
from_dict delete?
|
{
"login": "hackerxiaobai",
"id": 22817243,
"node_id": "MDQ6VXNlcjIyODE3MjQz",
"avatar_url": "https://avatars.githubusercontent.com/u/22817243?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/hackerxiaobai",
"html_url": "https://github.com/hackerxiaobai",
"followers_url": "https://api.github.com/users/hackerxiaobai/followers",
"following_url": "https://api.github.com/users/hackerxiaobai/following{/other_user}",
"gists_url": "https://api.github.com/users/hackerxiaobai/gists{/gist_id}",
"starred_url": "https://api.github.com/users/hackerxiaobai/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/hackerxiaobai/subscriptions",
"organizations_url": "https://api.github.com/users/hackerxiaobai/orgs",
"repos_url": "https://api.github.com/users/hackerxiaobai/repos",
"events_url": "https://api.github.com/users/hackerxiaobai/events{/privacy}",
"received_events_url": "https://api.github.com/users/hackerxiaobai/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"`from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\nRight now if you want to use `from_dict` you have to install the package from the master branch\r\n```\r\npip install git+https://github.com/huggingface/nlp.git\r\n```",
"> `from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\n> Right now if you want to use `from_dict` you have to install the package from the master branch\r\n> \r\n> ```\r\n> pip install git+https://github.com/huggingface/nlp.git\r\n> ```\r\nOK, thank you.\r\n"
] | 2020-07-19T07:08:36 | 2020-07-21T02:21:17 | 2020-07-21T02:21:17 |
NONE
| null | null | null |
AttributeError: type object 'Dataset' has no attribute 'from_dict'
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/414/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/414/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/413
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/413/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/413/comments
|
https://api.github.com/repos/huggingface/datasets/issues/413/events
|
https://github.com/huggingface/datasets/issues/413
| 660,063,655 |
MDU6SXNzdWU2NjAwNjM2NTU=
| 413 |
Is there a way to download only NQ dev?
|
{
"login": "tholor",
"id": 1563902,
"node_id": "MDQ6VXNlcjE1NjM5MDI=",
"avatar_url": "https://avatars.githubusercontent.com/u/1563902?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/tholor",
"html_url": "https://github.com/tholor",
"followers_url": "https://api.github.com/users/tholor/followers",
"following_url": "https://api.github.com/users/tholor/following{/other_user}",
"gists_url": "https://api.github.com/users/tholor/gists{/gist_id}",
"starred_url": "https://api.github.com/users/tholor/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/tholor/subscriptions",
"organizations_url": "https://api.github.com/users/tholor/orgs",
"repos_url": "https://api.github.com/users/tholor/repos",
"events_url": "https://api.github.com/users/tholor/events{/privacy}",
"received_events_url": "https://api.github.com/users/tholor/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.",
"Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially also others. \r\nFor us, it will in this case make the difference of using the library or keeping the old downloads of the raw dev datasets. \r\nHowever, I don't know if that fits into your plans with the library and can also understand if you don't want to support this.",
"I don't think we could force this behavior generally since the dataset script authors are free to organize the file download as they want (sometimes the mapping between split and files can be very much nontrivial) but we can add an additional configuration for Natural Question indeed as @lhoestq indicate."
] | 2020-07-18T10:28:23 | 2022-02-11T09:50:21 | 2022-02-11T09:50:21 |
NONE
| null | null | null |
Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)?
As we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data.
I tried
```
dataset = nlp.load_dataset('natural_questions', split="validation", beam_runner="DirectRunner")
```
But this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits / slicing options only available after downloading?
Thanks!
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/413/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/413/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/412
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/412/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/412/comments
|
https://api.github.com/repos/huggingface/datasets/issues/412/events
|
https://github.com/huggingface/datasets/issues/412
| 660,047,139 |
MDU6SXNzdWU2NjAwNDcxMzk=
| 412 |
Unable to load XTREME dataset from disk
|
{
"login": "lewtun",
"id": 26859204,
"node_id": "MDQ6VXNlcjI2ODU5MjA0",
"avatar_url": "https://avatars.githubusercontent.com/u/26859204?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lewtun",
"html_url": "https://github.com/lewtun",
"followers_url": "https://api.github.com/users/lewtun/followers",
"following_url": "https://api.github.com/users/lewtun/following{/other_user}",
"gists_url": "https://api.github.com/users/lewtun/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lewtun/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lewtun/subscriptions",
"organizations_url": "https://api.github.com/users/lewtun/orgs",
"repos_url": "https://api.github.com/users/lewtun/repos",
"events_url": "https://api.github.com/users/lewtun/events{/privacy}",
"received_events_url": "https://api.github.com/users/lewtun/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`",
"I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !",
"Thanks for the rapid fix @lhoestq!"
] | 2020-07-18T09:55:00 | 2020-07-21T08:15:44 | 2020-07-21T08:15:44 |
MEMBER
| null | null | null |
Hi 🤗 team!
## Description of the problem
Following the [docs](https://huggingface.co/nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark.
I have manually downloaded the `AmazonPhotos.zip` file from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.
As far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:
```
# path where load_dataset is looking for fr.tar.gz
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/
# path where it actually exists
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/panx_dataset/
```
## Steps to reproduce the problem
1. Manually download the XTREME benchmark from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)
2. Run the following code snippet
```python
from nlp import load_dataset
# AmazonPhotos.zip is in the root of the folder
dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
```
3. Here is the stack trace
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-26786bb5fa93> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
464 split_dict = SplitDict(dataset_name=self.name)
465 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 466 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
467 # Checksums verification
468 if verify_infos:
/usr/local/lib/python3.6/dist-packages/nlp/datasets/xtreme/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746/xtreme.py in _split_generators(self, dl_manager)
725 panx_dl_dir = dl_manager.extract(panx_path)
726 lang = self.config.name.split(".")[1]
--> 727 lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz"))
728 return [
729 nlp.SplitGenerator(
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in extract(self, path_or_paths)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
170 return tuple(mapped)
171 # Singleton
--> 172 return function(data_struct)
173
174
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in <lambda>(path)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
203 elif urlparse(url_or_filename).scheme == "":
204 # File, but it doesn't exist.
--> 205 raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename))
206 else:
207 # Something unknown
FileNotFoundError: Local file /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/fr.tar.gz doesn't exist
```
## OS and hardware
```
- `nlp` version: 0.3.0
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/412/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/412/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/409
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/409/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/409/comments
|
https://api.github.com/repos/huggingface/datasets/issues/409/events
|
https://github.com/huggingface/datasets/issues/409
| 659,128,611 |
MDU6SXNzdWU2NTkxMjg2MTE=
| 409 |
train_test_split error: 'dict' object has no attribute 'deepcopy'
|
{
"login": "morganmcg1",
"id": 20516801,
"node_id": "MDQ6VXNlcjIwNTE2ODAx",
"avatar_url": "https://avatars.githubusercontent.com/u/20516801?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/morganmcg1",
"html_url": "https://github.com/morganmcg1",
"followers_url": "https://api.github.com/users/morganmcg1/followers",
"following_url": "https://api.github.com/users/morganmcg1/following{/other_user}",
"gists_url": "https://api.github.com/users/morganmcg1/gists{/gist_id}",
"starred_url": "https://api.github.com/users/morganmcg1/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/morganmcg1/subscriptions",
"organizations_url": "https://api.github.com/users/morganmcg1/orgs",
"repos_url": "https://api.github.com/users/morganmcg1/repos",
"events_url": "https://api.github.com/users/morganmcg1/events{/privacy}",
"received_events_url": "https://api.github.com/users/morganmcg1/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.\r\nCould you pull the latest changes from master @morganmcg1 ?",
"Thanks @lhoestq, works fine now!"
] | 2020-07-17T10:36:28 | 2020-07-21T14:34:52 | 2020-07-21T14:34:52 |
NONE
| null | null | null |
`train_test_split` is giving me an error when I try and call it:
`'dict' object has no attribute 'deepcopy'`
## To reproduce
```
dataset = load_dataset('glue', 'mrpc', split='train')
dataset = dataset.train_test_split(test_size=0.2)
```
## Full Stacktrace
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-feb740dbec9a> in <module>
1 dataset = load_dataset('glue', 'mrpc', split='train')
----> 2 dataset = dataset.train_test_split(test_size=0.2)
~/anaconda3/envs/fastai2_me/lib/python3.7/site-packages/nlp/arrow_dataset.py in train_test_split(self, test_size, train_size, shuffle, seed, generator, keep_in_memory, load_from_cache_file, train_cache_file_name, test_cache_file_name, writer_batch_size)
1032 "writer_batch_size": writer_batch_size,
1033 }
-> 1034 train_kwargs = cache_kwargs.deepcopy()
1035 train_kwargs["split"] = "train"
1036 test_kwargs = cache_kwargs.deepcopy()
AttributeError: 'dict' object has no attribute 'deepcopy'
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/409/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/409/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/407
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/407/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/407/comments
|
https://api.github.com/repos/huggingface/datasets/issues/407/events
|
https://github.com/huggingface/datasets/issues/407
| 658,672,736 |
MDU6SXNzdWU2NTg2NzI3MzY=
| 407 |
MissingBeamOptions for Wikipedia 20200501.en
|
{
"login": "mitchellgordon95",
"id": 7490438,
"node_id": "MDQ6VXNlcjc0OTA0Mzg=",
"avatar_url": "https://avatars.githubusercontent.com/u/7490438?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/mitchellgordon95",
"html_url": "https://github.com/mitchellgordon95",
"followers_url": "https://api.github.com/users/mitchellgordon95/followers",
"following_url": "https://api.github.com/users/mitchellgordon95/following{/other_user}",
"gists_url": "https://api.github.com/users/mitchellgordon95/gists{/gist_id}",
"starred_url": "https://api.github.com/users/mitchellgordon95/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/mitchellgordon95/subscriptions",
"organizations_url": "https://api.github.com/users/mitchellgordon95/orgs",
"repos_url": "https://api.github.com/users/mitchellgordon95/repos",
"events_url": "https://api.github.com/users/mitchellgordon95/events{/privacy}",
"received_events_url": "https://api.github.com/users/mitchellgordon95/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c ",
"Works for me! Thanks.",
"I found the same issue with almost any language other than English. (For English, it works). Will someone need to update the file on S3 again?",
"This is because only some languages are already preprocessed (en, de, fr, it) and stored on our google storage.\r\nWe plan to have a systematic way to preprocess more wikipedia languages in the future.\r\n\r\nFor the other languages you have to process them on your side using apache beam. That's why the lib asks for a Beam runner."
] | 2020-07-16T23:48:03 | 2021-01-12T11:41:16 | 2020-07-17T14:24:28 |
CONTRIBUTOR
| null | null | null |
There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):
```
nlp.load_dataset('wikipedia', "20200501.en", split='train')
```
And now, having pulled master, I get:
```
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to /home/hltcoe/mgordon/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...
Traceback (most recent call last):
File "scripts/download.py", line 11, in <module>
fire.Fire(download_pretrain)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 138, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 468, in _Fire
target=component.__name__)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 672, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "scripts/download.py", line 6, in download_pretrain
nlp.load_dataset('wikipedia', "20200501.en", split='train')
File "/exp/mgordon/nlp/src/nlp/load.py", line 534, in load_dataset
save_infos=save_infos,
File "/exp/mgordon/nlp/src/nlp/builder.py", line 460, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/exp/mgordon/nlp/src/nlp/builder.py", line 870, in _download_and_prepare
"\n\t`{}`".format(usage_example)
nlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S
park, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/407/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/407/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/406
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/406/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/406/comments
|
https://api.github.com/repos/huggingface/datasets/issues/406/events
|
https://github.com/huggingface/datasets/issues/406
| 658,581,764 |
MDU6SXNzdWU2NTg1ODE3NjQ=
| 406 |
Faster Shuffling?
|
{
"login": "mitchellgordon95",
"id": 7490438,
"node_id": "MDQ6VXNlcjc0OTA0Mzg=",
"avatar_url": "https://avatars.githubusercontent.com/u/7490438?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/mitchellgordon95",
"html_url": "https://github.com/mitchellgordon95",
"followers_url": "https://api.github.com/users/mitchellgordon95/followers",
"following_url": "https://api.github.com/users/mitchellgordon95/following{/other_user}",
"gists_url": "https://api.github.com/users/mitchellgordon95/gists{/gist_id}",
"starred_url": "https://api.github.com/users/mitchellgordon95/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/mitchellgordon95/subscriptions",
"organizations_url": "https://api.github.com/users/mitchellgordon95/orgs",
"repos_url": "https://api.github.com/users/mitchellgordon95/repos",
"events_url": "https://api.github.com/users/mitchellgordon95/events{/privacy}",
"received_events_url": "https://api.github.com/users/mitchellgordon95/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I think the slowness here probably come from the fact that we are copying from and to python.\r\n\r\n@lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?",
"> @lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?\r\n\r\nI just tried with `writer.write_table` with tables of 1000 elements and it's slower that the solution in #405 \r\n\r\nOn my side (select 10 000 examples):\r\n- Original implementation: 12s\r\n- Batched solution: 100ms\r\n- solution using arrow tables: 350ms\r\n\r\nI'll try with arrays and record batches to see if we can make it work.",
"I tried using `.take` from pyarrow recordbatches but it doesn't improve the speed that much:\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\ndset = nlp.Dataset.from_file(\"dummy_test_select.arrow\") # dummy dataset with 100000 examples like {\"a\": \"h\"*512}\r\nindices = np.random.randint(0, 100_000, 1000_000)\r\n```\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\",\r\n writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n table = pa.concat_tables(dset._data.slice(int(i), 1) for i in indices[i : min(len(indices), i + batch_size)])\r\n batch = table.to_pydict()\r\n writer.write_batch(batch)\r\nwriter.finalize()\r\n# 9.12s\r\n```\r\n\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\", \r\n writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n batch_indices = indices[i : min(len(indices), i + batch_size)]\r\n # First, extract only the indices that we need with a mask\r\n mask = [False] * len(dset)\r\n for k in batch_indices:\r\n mask[k] = True\r\n t_batch = dset._data.filter(pa.array(mask))\r\n # Second, build the list of indices for the filtered table, and taking care of duplicates\r\n rev_positions = {}\r\n duplicates = 0\r\n for i, j in enumerate(sorted(batch_indices)):\r\n if j in rev_positions:\r\n duplicates += 1\r\n else:\r\n rev_positions[j] = i - duplicates\r\n rev_map = [rev_positions[j] for j in batch_indices]\r\n # Third, use `.take` from the combined recordbatch\r\n t_combined = t_batch.combine_chunks() # load in memory\r\n recordbatch = t_combined.to_batches()[0]\r\n table = pa.Table.from_arrays(\r\n [recordbatch[c].take(pa.array(rev_map)) for c in range(len(dset._data.column_names))],\r\n schema=writer.schema\r\n )\r\n writer.write_table(table)\r\nwriter.finalize()\r\n# 3.2s\r\n```\r\n",
"Shuffling is now significantly faster thanks to #513 \r\nFeel free to play with it now :)\r\n\r\nClosing this one, but feel free to re-open if you have other questions",
"> Shuffling is now significantly faster thanks to #513 Feel free to play with it now :)\r\n> \r\n> Closing this one, but feel free to re-open if you have other questions\r\n\r\nI have a similar issue. My code is \r\n\r\n```\r\n for batch_num in range(num_batches):\r\n print(f'--> {batch_num=}\\n') if verbose else None\r\n # - Get batch\r\n shuffled_dataset = dataset.shuffle(buffer_size=buffer_size, seed=seed)\r\n raw_text_batch = shuffled_dataset.take(batch_size)\r\n tokenized_batch = map(raw_text_batch)\r\n if verbose:\r\n time_start = time.time()\r\n print(f'{raw_text_batch=}')\r\n print(f'{tokenized_batch=}')\r\n print(f'{next(iter(raw_text_batch))=}')\r\n print(f'{next(iter(tokenized_batch))=}')\r\n print(f'Time it took: {time.time() - time_start} seconds \\a\\n')\r\n ```\r\n\r\nwithout the suffle it takes 4.5 secs with it takes 87.1 secs. Is this difference expected? my dataset version is:\r\n```\r\n(beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ pip list | grep dataset\r\ndatasets 2.14.3\r\n```\r\n\r\n@lhoestq thoughts?\r\n\r\n",
"still slow even with update to `2.14.4` most recent as of this writing\r\n\r\n```\r\nTime it took: 4.301205635070801 seconds \r\n\r\n--> batch_num=0\r\n\r\nraw_text_batch=<datasets.iterable_dataset.IterableDataset object at 0x7f1fea7c2a40>\r\ntokenized_batch=<datasets.iterable_dataset.IterableDataset object at 0x7f1fea7c2f20>\r\nnext(iter(raw_text_batch))={'text': \"No matter which style you choose, you can be sure of one thing: our quality and craftsmanship are the best in the business. It's who we are and what we believe in. And it's evident every day on the factory floor where our dedicated teams take great pride in every stitch.\", 'timestamp': '2019-04-19T06:43:50Z', 'url': 'https://institchescustoms.com/katzkin.html'}\r\nnext(iter(tokenized_batch))={'input_ids': tensor([ 2949, 2300, 543, 3918, 345, 3853, 11, 345, 460, 307,\r\n 1654, 286, 530, 1517, 25, 674, 3081, 290, 5977, 49820,\r\n 389, 262, 1266, 287, 262, 1597, 13, 632, 338, 508,\r\n 356, 389, 290, 644, 356, 1975, 287, 13, 843, 340,\r\n 338, 10678, 790, 1110, 319, 262, 8860, 4314, 810, 674,\r\n 7256, 3466, 1011, 1049, 11293, 287, 790, 24695, 13, 50256,\r\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\r\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\r\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\r\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\r\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\r\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\r\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0])}\r\nTime it took: 102.99258613586426 seconds\r\n```",
"Shuffling leads to doing random access in many different locations on disk which is slower than reading contiguous data.\r\n\r\nThere is a super fast approximate shuffling algorithm implemented for iterable datasets though:\r\n\r\n```python\r\niterable_dataset = dataset.to_iterable_dataset(num_shards=1024)\r\nshuffled_dataset = iterable_dataset.shuffle(buffer_size=1000)\r\n```\r\n\r\n(the first batch might be a bit slow to get because the algorithm first fills a buffer before returning the first batch, see the [docs](https://huggingface.co/docs/datasets/v2.14.4/en/stream#shuffle) for more info)"
] | 2020-07-16T21:21:53 | 2023-08-16T09:52:39 | 2020-09-07T14:45:25 |
CONTRIBUTOR
| null | null | null |
Consider shuffling bookcorpus:
```
dataset = nlp.load_dataset('bookcorpus', split='train')
dataset.shuffle()
```
According to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.
But I can also just write the lines to a text file:
```
batch_size = 100000
with open('tmp.txt', 'w+') as out_f:
for i in tqdm(range(0, len(dataset), batch_size)):
batch = dataset[i:i+batch_size]['text']
print("\n".join(batch), file=out_f)
```
Which completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,
```
dataset = nlp.load_dataset('text', data_files='tmp2.txt')
```
Which completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping.
Is shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.)
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/406/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/406/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/395
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/395/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/395/comments
|
https://api.github.com/repos/huggingface/datasets/issues/395/events
|
https://github.com/huggingface/datasets/issues/395
| 657,454,983 |
MDU6SXNzdWU2NTc0NTQ5ODM=
| 395 |
Memory issue when doing select
|
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false |
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "lhoestq",
"id": 42851186,
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/lhoestq",
"html_url": "https://github.com/lhoestq",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"type": "User",
"site_admin": false
}
] | null |
[] | 2020-07-15T15:43:38 | 2020-07-16T08:07:31 | 2020-07-16T08:07:31 |
MEMBER
| null | null | null |
As noticed in #389, the following code loads the entire wikipedia in memory.
```python
import nlp
w = nlp.load_dataset("wikipedia", "20200501.en", split="train")
w.select([0])
```
This is caused by [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/arrow_dataset.py#L626) for some reason, that tries to serialize the function with all the wikipedia data with it.
It's not the case with `.map` or `.filter`.
However functions that are based on `.select` like `.shuffle`, `.shard`, `.train_test_split`, `.sort` are affected.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/395/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/395/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/388
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/388/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/388/comments
|
https://api.github.com/repos/huggingface/datasets/issues/388/events
|
https://github.com/huggingface/datasets/issues/388
| 656,707,497 |
MDU6SXNzdWU2NTY3MDc0OTc=
| 388 |
🐛 [Dataset] Cannot download wmt14, wmt15 and wmt17
|
{
"login": "SamuelCahyawijaya",
"id": 2826602,
"node_id": "MDQ6VXNlcjI4MjY2MDI=",
"avatar_url": "https://avatars.githubusercontent.com/u/2826602?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/SamuelCahyawijaya",
"html_url": "https://github.com/SamuelCahyawijaya",
"followers_url": "https://api.github.com/users/SamuelCahyawijaya/followers",
"following_url": "https://api.github.com/users/SamuelCahyawijaya/following{/other_user}",
"gists_url": "https://api.github.com/users/SamuelCahyawijaya/gists{/gist_id}",
"starred_url": "https://api.github.com/users/SamuelCahyawijaya/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/SamuelCahyawijaya/subscriptions",
"organizations_url": "https://api.github.com/users/SamuelCahyawijaya/orgs",
"repos_url": "https://api.github.com/users/SamuelCahyawijaya/repos",
"events_url": "https://api.github.com/users/SamuelCahyawijaya/events{/privacy}",
"received_events_url": "https://api.github.com/users/SamuelCahyawijaya/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067388877,
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug",
"name": "dataset bug",
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library"
}
] |
closed
| false |
{
"login": "patrickvonplaten",
"id": 23423619,
"node_id": "MDQ6VXNlcjIzNDIzNjE5",
"avatar_url": "https://avatars.githubusercontent.com/u/23423619?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/patrickvonplaten",
"html_url": "https://github.com/patrickvonplaten",
"followers_url": "https://api.github.com/users/patrickvonplaten/followers",
"following_url": "https://api.github.com/users/patrickvonplaten/following{/other_user}",
"gists_url": "https://api.github.com/users/patrickvonplaten/gists{/gist_id}",
"starred_url": "https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/patrickvonplaten/subscriptions",
"organizations_url": "https://api.github.com/users/patrickvonplaten/orgs",
"repos_url": "https://api.github.com/users/patrickvonplaten/repos",
"events_url": "https://api.github.com/users/patrickvonplaten/events{/privacy}",
"received_events_url": "https://api.github.com/users/patrickvonplaten/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"login": "patrickvonplaten",
"id": 23423619,
"node_id": "MDQ6VXNlcjIzNDIzNjE5",
"avatar_url": "https://avatars.githubusercontent.com/u/23423619?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/patrickvonplaten",
"html_url": "https://github.com/patrickvonplaten",
"followers_url": "https://api.github.com/users/patrickvonplaten/followers",
"following_url": "https://api.github.com/users/patrickvonplaten/following{/other_user}",
"gists_url": "https://api.github.com/users/patrickvonplaten/gists{/gist_id}",
"starred_url": "https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/patrickvonplaten/subscriptions",
"organizations_url": "https://api.github.com/users/patrickvonplaten/orgs",
"repos_url": "https://api.github.com/users/patrickvonplaten/repos",
"events_url": "https://api.github.com/users/patrickvonplaten/events{/privacy}",
"received_events_url": "https://api.github.com/users/patrickvonplaten/received_events",
"type": "User",
"site_admin": false
}
] | null |
[
"similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')\r\n`\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 658M/658M [1:00:42<00:00, 181kB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 918M/918M [1:39:38<00:00, 154kB/s]\r\nDownloading: 2%|▉ | 40.9M/2.37G [04:48<5:03:06, 128kB/s]\r\n`\r\nCould we just download a specific subdataset in 'wmt14', such as 'newstest14'? ",
"> The code runs but the download speed is extremely slow, the same behaviour is not observed on wmt16 and wmt18\r\n\r\nThe original source for the files may provide slow download speeds.\r\nWe can probably host these files ourselves.\r\n\r\n> When trying to download wmt17 zh-en, I got the following error:\r\n> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz\r\n\r\nLooks like the file`UNv1.0.en-zh.tar.gz` is missing, or the url changed. We need to fix that\r\n\r\n> Could we just download a specific subdataset in 'wmt14', such as 'newstest14'?\r\n\r\nRight now I don't think it's possible. Maybe @patrickvonplaten knows more about it\r\n",
"Yeah, the download speed is sadly always extremely slow :-/. \r\nI will try to check out the `wmt17 zh-en` bug :-) ",
"Maybe this can be used - https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 ",
"These issues seem to be fixed now."
] | 2020-07-14T15:36:41 | 2022-10-04T18:01:28 | 2022-10-04T18:01:28 |
NONE
| null | null | null |
1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:
```
nlp.load_dataset('wmt14','de-en')
nlp.load_dataset('wmt15','de-en')
nlp.load_dataset('wmt17','de-en')
nlp.load_dataset('wmt19','de-en')
```
The code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`
2. When trying to download `wmt17 zh-en`, I got the following error:
> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/388/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/388/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/387
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/387/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/387/comments
|
https://api.github.com/repos/huggingface/datasets/issues/387/events
|
https://github.com/huggingface/datasets/issues/387
| 656,361,357 |
MDU6SXNzdWU2NTYzNjEzNTc=
| 387 |
Conversion through to_pandas output numpy arrays for lists instead of python objects
|
{
"login": "thomwolf",
"id": 7353373,
"node_id": "MDQ6VXNlcjczNTMzNzM=",
"avatar_url": "https://avatars.githubusercontent.com/u/7353373?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/thomwolf",
"html_url": "https://github.com/thomwolf",
"followers_url": "https://api.github.com/users/thomwolf/followers",
"following_url": "https://api.github.com/users/thomwolf/following{/other_user}",
"gists_url": "https://api.github.com/users/thomwolf/gists{/gist_id}",
"starred_url": "https://api.github.com/users/thomwolf/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/thomwolf/subscriptions",
"organizations_url": "https://api.github.com/users/thomwolf/orgs",
"repos_url": "https://api.github.com/users/thomwolf/repos",
"events_url": "https://api.github.com/users/thomwolf/events{/privacy}",
"received_events_url": "https://api.github.com/users/thomwolf/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict/to_pylist.\r\n\r\n- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.\r\n- to_pydict/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.\r\n\r\nMaybe we can have to_pydict/to_pylist as the default and use to_numpy or to_pandas when the format (set by `set_format`) is 'numpy' or 'pandas'"
] | 2020-07-14T06:24:01 | 2020-07-17T11:37:00 | 2020-07-17T11:37:00 |
MEMBER
| null | null | null |
In a related question, the conversion through to_pandas output numpy arrays for the lists instead of python objects.
Here is an example:
```python
>>> dataset._data.slice(key, 1).to_pandas().to_dict("list")
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [array([ 101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292,
1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938,
4267, 12223, 21811, 1117, 2554, 119, 102])], 'token_type_ids': [array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0])], 'attention_mask': [array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1])]}
>>> type(dataset._data.slice(key, 1).to_pandas().to_dict("list")['input_ids'][0])
<class 'numpy.ndarray'>
>>> dataset._data.slice(key, 1).to_pydict()
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [[101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/387/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/387/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/382
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/382/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/382/comments
|
https://api.github.com/repos/huggingface/datasets/issues/382/events
|
https://github.com/huggingface/datasets/issues/382
| 655,290,482 |
MDU6SXNzdWU2NTUyOTA0ODI=
| 382 |
1080
|
{
"login": "saq194",
"id": 60942503,
"node_id": "MDQ6VXNlcjYwOTQyNTAz",
"avatar_url": "https://avatars.githubusercontent.com/u/60942503?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/saq194",
"html_url": "https://github.com/saq194",
"followers_url": "https://api.github.com/users/saq194/followers",
"following_url": "https://api.github.com/users/saq194/following{/other_user}",
"gists_url": "https://api.github.com/users/saq194/gists{/gist_id}",
"starred_url": "https://api.github.com/users/saq194/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/saq194/subscriptions",
"organizations_url": "https://api.github.com/users/saq194/orgs",
"repos_url": "https://api.github.com/users/saq194/repos",
"events_url": "https://api.github.com/users/saq194/events{/privacy}",
"received_events_url": "https://api.github.com/users/saq194/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-07-11T22:29:07 | 2020-07-11T22:49:38 | 2020-07-11T22:49:38 |
NONE
| null | null | null |
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/382/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/382/timeline
| null |
completed
|
|
https://api.github.com/repos/huggingface/datasets/issues/381
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/381/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/381/comments
|
https://api.github.com/repos/huggingface/datasets/issues/381/events
|
https://github.com/huggingface/datasets/issues/381
| 655,277,119 |
MDU6SXNzdWU2NTUyNzcxMTk=
| 381 |
NLp
|
{
"login": "Spartanthor",
"id": 68147610,
"node_id": "MDQ6VXNlcjY4MTQ3NjEw",
"avatar_url": "https://avatars.githubusercontent.com/u/68147610?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/Spartanthor",
"html_url": "https://github.com/Spartanthor",
"followers_url": "https://api.github.com/users/Spartanthor/followers",
"following_url": "https://api.github.com/users/Spartanthor/following{/other_user}",
"gists_url": "https://api.github.com/users/Spartanthor/gists{/gist_id}",
"starred_url": "https://api.github.com/users/Spartanthor/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/Spartanthor/subscriptions",
"organizations_url": "https://api.github.com/users/Spartanthor/orgs",
"repos_url": "https://api.github.com/users/Spartanthor/repos",
"events_url": "https://api.github.com/users/Spartanthor/events{/privacy}",
"received_events_url": "https://api.github.com/users/Spartanthor/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-07-11T20:50:14 | 2020-07-11T20:50:39 | 2020-07-11T20:50:39 |
NONE
| null | null | null |
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/381/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/381/timeline
| null |
completed
|
|
https://api.github.com/repos/huggingface/datasets/issues/378
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/378/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/378/comments
|
https://api.github.com/repos/huggingface/datasets/issues/378/events
|
https://github.com/huggingface/datasets/issues/378
| 655,226,316 |
MDU6SXNzdWU2NTUyMjYzMTY=
| 378 |
[dataset] Structure of MLQA seems unecessary nested
|
{
"login": "thomwolf",
"id": 7353373,
"node_id": "MDQ6VXNlcjczNTMzNzM=",
"avatar_url": "https://avatars.githubusercontent.com/u/7353373?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/thomwolf",
"html_url": "https://github.com/thomwolf",
"followers_url": "https://api.github.com/users/thomwolf/followers",
"following_url": "https://api.github.com/users/thomwolf/following{/other_user}",
"gists_url": "https://api.github.com/users/thomwolf/gists{/gist_id}",
"starred_url": "https://api.github.com/users/thomwolf/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/thomwolf/subscriptions",
"organizations_url": "https://api.github.com/users/thomwolf/orgs",
"repos_url": "https://api.github.com/users/thomwolf/repos",
"events_url": "https://api.github.com/users/thomwolf/events{/privacy}",
"received_events_url": "https://api.github.com/users/thomwolf/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Same for the RACE dataset: https://github.com/huggingface/nlp/blob/master/datasets/race/race.py\r\n\r\nShould we scan all the datasets to remove this pattern of un-necessary nesting?",
"You're right, I think we don't need to use the nested dictionary. \r\n"
] | 2020-07-11T15:16:08 | 2020-07-15T16:17:20 | 2020-07-15T16:17:20 |
MEMBER
| null | null | null |
The features of the MLQA dataset comprise several nested dictionaries with a single element inside (for `questions` and `ids`): https://github.com/huggingface/nlp/blob/master/datasets/mlqa/mlqa.py#L90-L97
Should we keep this @mariamabarham @patrickvonplaten? Was this added for compatibility with tfds?
```python
features=nlp.Features(
{
"context": nlp.Value("string"),
"questions": nlp.features.Sequence({"question": nlp.Value("string")}),
"answers": nlp.features.Sequence(
{"text": nlp.Value("string"), "answer_start": nlp.Value("int32"),}
),
"ids": nlp.features.Sequence({"idx": nlp.Value("string")})
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/378/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/378/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/377
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/377/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/377/comments
|
https://api.github.com/repos/huggingface/datasets/issues/377/events
|
https://github.com/huggingface/datasets/issues/377
| 655,215,790 |
MDU6SXNzdWU2NTUyMTU3OTA=
| 377 |
Iyy!!!
|
{
"login": "ajinomoh",
"id": 68154535,
"node_id": "MDQ6VXNlcjY4MTU0NTM1",
"avatar_url": "https://avatars.githubusercontent.com/u/68154535?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ajinomoh",
"html_url": "https://github.com/ajinomoh",
"followers_url": "https://api.github.com/users/ajinomoh/followers",
"following_url": "https://api.github.com/users/ajinomoh/following{/other_user}",
"gists_url": "https://api.github.com/users/ajinomoh/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ajinomoh/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ajinomoh/subscriptions",
"organizations_url": "https://api.github.com/users/ajinomoh/orgs",
"repos_url": "https://api.github.com/users/ajinomoh/repos",
"events_url": "https://api.github.com/users/ajinomoh/events{/privacy}",
"received_events_url": "https://api.github.com/users/ajinomoh/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2020-07-11T14:11:07 | 2020-07-11T14:30:51 | 2020-07-11T14:30:51 |
NONE
| null | null | null |
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/377/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/377/timeline
| null |
completed
|
|
https://api.github.com/repos/huggingface/datasets/issues/376
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/376/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/376/comments
|
https://api.github.com/repos/huggingface/datasets/issues/376/events
|
https://github.com/huggingface/datasets/issues/376
| 655,047,826 |
MDU6SXNzdWU2NTUwNDc4MjY=
| 376 |
to_pandas conversion doesn't always work
|
{
"login": "thomwolf",
"id": 7353373,
"node_id": "MDQ6VXNlcjczNTMzNzM=",
"avatar_url": "https://avatars.githubusercontent.com/u/7353373?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/thomwolf",
"html_url": "https://github.com/thomwolf",
"followers_url": "https://api.github.com/users/thomwolf/followers",
"following_url": "https://api.github.com/users/thomwolf/following{/other_user}",
"gists_url": "https://api.github.com/users/thomwolf/gists{/gist_id}",
"starred_url": "https://api.github.com/users/thomwolf/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/thomwolf/subscriptions",
"organizations_url": "https://api.github.com/users/thomwolf/orgs",
"repos_url": "https://api.github.com/users/thomwolf/repos",
"events_url": "https://api.github.com/users/thomwolf/events{/privacy}",
"received_events_url": "https://api.github.com/users/thomwolf/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"**Edit**: other topic previously in this message moved to a new issue: https://github.com/huggingface/nlp/issues/387",
"Could you try to update pyarrow to >=0.17.0 ? It should fix the `to_pandas` bug\r\n\r\nAlso I'm not sure that structures like list<struct> are fully supported in the lib (none of the datasets use that).\r\nIt can cause issues when using dataset transforms like `filter` for example"
] | 2020-07-10T21:33:31 | 2022-10-04T18:05:39 | 2022-10-04T18:05:39 |
MEMBER
| null | null | null |
For some complex nested types, the conversion from Arrow to python dict through pandas doesn't seem to be possible.
Here is an example using the official SQUAD v2 JSON file.
This example was found while investigating #373.
```python
>>> squad = load_dataset('json', data_files={nlp.Split.TRAIN: ["./train-v2.0.json"]}, download_mode=nlp.GenerateMode.FORCE_REDOWNLOAD, version="1.0.0", field='data')
>>> squad['train']
Dataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 442)
>>> squad['train'][0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/thomwolf/Documents/GitHub/datasets/src/nlp/arrow_dataset.py", line 589, in __getitem__
format_kwargs=self._format_kwargs,
File "/Users/thomwolf/Documents/GitHub/datasets/src/nlp/arrow_dataset.py", line 529, in _getitem
outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict("list"))
File "pyarrow/array.pxi", line 559, in pyarrow.lib._PandasConvertible.to_pandas
File "pyarrow/table.pxi", line 1367, in pyarrow.lib.Table._to_pandas
File "/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 766, in table_to_blockmanager
blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)
File "/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 1101, in _table_to_blocks
list(extension_columns.keys()))
File "pyarrow/table.pxi", line 881, in pyarrow.lib.table_to_blocks
File "pyarrow/error.pxi", line 105, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>
```
cc @lhoestq would we have a way to detect this from the schema maybe?
Here is the schema for this pretty complex JSON:
```python
>>> squad['train'].schema
title: string
paragraphs: list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>
child 0, item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>
child 0, qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>
child 0, item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>
child 0, question: string
child 1, id: string
child 2, answers: list<item: struct<text: string, answer_start: int64>>
child 0, item: struct<text: string, answer_start: int64>
child 0, text: string
child 1, answer_start: int64
child 3, is_impossible: bool
child 4, plausible_answers: list<item: struct<text: string, answer_start: int64>>
child 0, item: struct<text: string, answer_start: int64>
child 0, text: string
child 1, answer_start: int64
child 1, context: string
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/376/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/376/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/375
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/375/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/375/comments
|
https://api.github.com/repos/huggingface/datasets/issues/375/events
|
https://github.com/huggingface/datasets/issues/375
| 655,023,307 |
MDU6SXNzdWU2NTUwMjMzMDc=
| 375 |
TypeError when computing bertscore
|
{
"login": "willywsm1013",
"id": 13269577,
"node_id": "MDQ6VXNlcjEzMjY5NTc3",
"avatar_url": "https://avatars.githubusercontent.com/u/13269577?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/willywsm1013",
"html_url": "https://github.com/willywsm1013",
"followers_url": "https://api.github.com/users/willywsm1013/followers",
"following_url": "https://api.github.com/users/willywsm1013/following{/other_user}",
"gists_url": "https://api.github.com/users/willywsm1013/gists{/gist_id}",
"starred_url": "https://api.github.com/users/willywsm1013/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/willywsm1013/subscriptions",
"organizations_url": "https://api.github.com/users/willywsm1013/orgs",
"repos_url": "https://api.github.com/users/willywsm1013/repos",
"events_url": "https://api.github.com/users/willywsm1013/events{/privacy}",
"received_events_url": "https://api.github.com/users/willywsm1013/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I am not able to reproduce this issue on my side.\r\nCould you give us more details about the inputs you used ?\r\n\r\nI do get another error though:\r\n```\r\n~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/bert_score/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)\r\n 371 return sorted(list(set(l)), key=lambda x: len(x.split(\" \")))\r\n 372 \r\n--> 373 sentences = dedup_and_sort(refs + hyps)\r\n 374 embs = []\r\n 375 iter_range = range(0, len(sentences), batch_size)\r\n\r\nValueError: operands could not be broadcast together with shapes (0,) (2,)\r\n```\r\nThat's because it gets numpy arrays as input and not lists. See #387 ",
"The other issue was fixed by #403 \r\n\r\nDo you still get this issue @willywsm1013 ?\r\n"
] | 2020-07-10T20:37:44 | 2022-06-01T15:15:59 | 2022-06-01T15:15:59 |
NONE
| null | null | null |
Hi,
I installed nlp 0.3.0 via pip, and my python version is 3.7.
When I tried to compute bertscore with the code:
```
import nlp
bertscore = nlp.load_metric('bertscore')
# load hyps and refs
...
print (bertscore.compute(hyps, refs, lang='en'))
```
I got the following error.
```
Traceback (most recent call last):
File "bert_score_evaluate.py", line 16, in <module>
print (bertscore.compute(hyps, refs, lang='en'))
File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metric.py", line 200, in compute
output = self._compute(predictions=predictions, references=references, **metrics_kwargs)
File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metrics/bertscore/fb176889831bf0ce995ed197edc94b2e9a83f647a869bb8c9477dbb2d04d0f08/bertscore.py", line 105, in _compute
hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)
TypeError: get_hash() takes 3 positional arguments but 4 were given
```
It seems like there is something wrong with get_hash() function?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/375/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/375/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/373
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/373/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/373/comments
|
https://api.github.com/repos/huggingface/datasets/issues/373/events
|
https://github.com/huggingface/datasets/issues/373
| 654,845,133 |
MDU6SXNzdWU2NTQ4NDUxMzM=
| 373 |
Segmentation fault when loading local JSON dataset as of #372
|
{
"login": "vegarab",
"id": 24683907,
"node_id": "MDQ6VXNlcjI0NjgzOTA3",
"avatar_url": "https://avatars.githubusercontent.com/u/24683907?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/vegarab",
"html_url": "https://github.com/vegarab",
"followers_url": "https://api.github.com/users/vegarab/followers",
"following_url": "https://api.github.com/users/vegarab/following{/other_user}",
"gists_url": "https://api.github.com/users/vegarab/gists{/gist_id}",
"starred_url": "https://api.github.com/users/vegarab/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/vegarab/subscriptions",
"organizations_url": "https://api.github.com/users/vegarab/orgs",
"repos_url": "https://api.github.com/users/vegarab/repos",
"events_url": "https://api.github.com/users/vegarab/events{/privacy}",
"received_events_url": "https://api.github.com/users/vegarab/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I've seen this sort of thing before -- it might help to delete the directory -- I've also noticed that there is an error with the json Dataloader for any data I've tried to load. I've replaced it with this, which skips over the data feature population step:\r\n\r\n\r\n```python\r\nimport os\r\n\r\nimport pyarrow.json as paj\r\n\r\nimport nlp as hf_nlp\r\n\r\nfrom nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils\r\nfrom nlp.arrow_writer import ArrowWriter\r\n\r\n\r\nclass JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" We handle string, list and dicts in datafiles\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n\r\n```",
"Yes, deleting the directory solves the error whenever I try to rerun.\r\n\r\nBy replacing the json-loader, you mean the cached file in my `site-packages` directory? e.g. `/home/XXX/.cache/lib/python3.7/site-packages/nlp/datasets/json/(...)/json.py` \r\n\r\nWhen I was testing this out before the #372 PR was merged I had issues installing it properly locally. Since the `json.py` script was downloaded instead of actually using the one provided in the local install. Manually updating that file seemed to solve it, but it didn't seem like a proper solution. Especially when having to run this on a remote compute cluster with no access to that directory.",
"I see, diving in the JSON file for SQuAD it's a pretty complex structure.\r\n\r\nThe best solution for you, if you have a dataset really similar to SQuAD would be to copy and modify the SQuAD data processing script. We will probably add soon an option to be able to specify file path to use instead of the automatic URL encoded in the script but in the meantime you can:\r\n- copy the [squad script](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py) in a new script for your dataset\r\n- in the new script replace [these `urls_to_download `](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py#L99-L102) by `urls_to_download=self.config.data_files`\r\n- load the dataset with `dataset = load_dataset('path/to/your/new/script', data_files={nlp.Split.TRAIN: \"./datasets/train-v2.0.json\"})`\r\n\r\nThis way you can reuse all the processing logic of the SQuAD loading script.",
"This seems like a more sensible solution! Thanks, @thomwolf. It's been a little daunting to understand what these scripts actually do, due to the level of abstraction and central documentation.\r\n\r\nAm I correct in assuming that the `_generate_examples()` function is the actual procedure for how the data is loaded from file? Meaning that essentially with a file containing another format, that is the only function that requires re-implementation? I'm working with a lot of datasets that, due to licensing and privacy, cannot be published. As this library is so neatly integrated with the transformers library and gives easy access to public sets such as SQUAD and increased performance, it is very neat to be able to load my private sets as well. As of now, I have just been working on scripts for translating all my data into the SQUAD-format before using the json script, but I see that it might not be necessary after all. ",
"Yes `_generate_examples()` is the main entry point. If you change the shape of the returned dictionary you also need to update the `features` in the `_info`.\r\n\r\nI'm currently writing the doc so it should be easier soon to use the library and know how to add your datasets.\r\n",
"Could you try to update pyarrow to >=0.17.0 @vegarab ?\r\nI don't have any segmentation fault with my version of pyarrow (0.17.1)\r\n\r\nI tested with\r\n```python\r\nimport nlp\r\ns = nlp.load_dataset(\"json\", data_files=\"train-v2.0.json\", field=\"data\", split=\"train\")\r\ns[0]\r\n# {'title': 'Normans', 'paragraphs': [{'qas': [{'question': 'In what country is Normandy located?', 'id':...\r\n```",
"Also if you want to have your own dataset script, we now have a new documentation !\r\nSee here:\r\nhttps://huggingface.co/nlp/add_dataset.html",
"@lhoestq \r\nFor some reason, I am not able to reproduce the segmentation fault, on pyarrow==0.16.0. Using the exact same environment and file.\r\n\r\nAnyhow, I discovered that pyarrow>=0.17.0 is required to read in a JSON file where the pandas structs contain lists. Otherwise, pyarrow complains when attempting to cast the struct:\r\n```py\r\nimport nlp\r\n>>> s = nlp.load_dataset(\"json\", data_files=\"datasets/train-v2.0.json\", field=\"data\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> s[0]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 558, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 498, in _getitem\r\n outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n File \"pyarrow/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n File \"pyarrow/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 766, in table_to_blockmanager\r\n blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 1101, in _table_to_blocks\r\n list(extension_columns.keys()))\r\n File \"pyarrow/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n File \"pyarrow/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n>>> s\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 35)\r\n```\r\n\r\nUpgrading to >=0.17.0 provides the same dataset structure, but accessing the records is possible without the same exception. \r\n\r\n",
"Very happy to see some extended documentation! ",
"#376 seems to be reporting the same issue as mentioned above. ",
"This issue helped me a lot, thanks.\r\nHope this issue will be fixed soon."
] | 2020-07-10T15:04:25 | 2022-10-04T18:05:47 | 2022-10-04T18:05:47 |
CONTRIBUTOR
| null | null | null |
The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault.
```
dataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data')
```
causes
```
Using custom data configuration default
Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/XXX/.cache/huggingface/datasets/json/default/0.0.0...
0 tables [00:00, ? tables/s]Segmentation fault (core dumped)
```
where `./datasets/train-v2.0.json` is downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/.
This is consistent with other SQuAD-formatted JSON files.
When attempting to load the dataset again, I get the following:
```
Using custom data configuration default
Traceback (most recent call last):
File "dataloader.py", line 6, in <module>
'json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data')
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset
save_infos=save_infos,
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 382, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/XXX/.conda/envs/torch/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 368, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/XXX/.conda/envs/torch/lib/python3.7/os.py", line 223, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/XXX/.cache/huggingface/datasets/json/default/0.0.0.incomplete'
```
(Not sure if you wanted this in the previous issue #369 or not as it was closed.)
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/373/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/373/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/369
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/369/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/369/comments
|
https://api.github.com/repos/huggingface/datasets/issues/369/events
|
https://github.com/huggingface/datasets/issues/369
| 654,186,890 |
MDU6SXNzdWU2NTQxODY4OTA=
| 369 |
can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries
|
{
"login": "vegarab",
"id": 24683907,
"node_id": "MDQ6VXNlcjI0NjgzOTA3",
"avatar_url": "https://avatars.githubusercontent.com/u/24683907?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/vegarab",
"html_url": "https://github.com/vegarab",
"followers_url": "https://api.github.com/users/vegarab/followers",
"following_url": "https://api.github.com/users/vegarab/following{/other_user}",
"gists_url": "https://api.github.com/users/vegarab/gists{/gist_id}",
"starred_url": "https://api.github.com/users/vegarab/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/vegarab/subscriptions",
"organizations_url": "https://api.github.com/users/vegarab/orgs",
"repos_url": "https://api.github.com/users/vegarab/repos",
"events_url": "https://api.github.com/users/vegarab/events{/privacy}",
"received_events_url": "https://api.github.com/users/vegarab/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 2067388877,
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug",
"name": "dataset bug",
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library"
}
] |
closed
| false | null |
[] | null |
[
"I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/",
"I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.\r\nIs this fixed in latest version? \r\nI updated the latest version 4.0.1 but still getting this error. What could cause this error?"
] | 2020-07-09T16:16:53 | 2020-12-15T23:07:22 | 2020-07-10T14:52:06 |
CONTRIBUTOR
| null | null | null |
Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB):
```
dataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: ["./path/to/file.json"]})
```
causes
```
Traceback (most recent call last):
File "dataloader.py", line 9, in <module>
["./path/to/file.json"]})
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset
save_infos=save_infos,
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 483, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 719, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False):
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__
for obj in iterable:
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/datasets/json/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b/json.py", line 53, in _generate_tables
file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,
File "pyarrow/_json.pyx", line 191, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
I haven't been able to find any reports of this specific pyarrow error here or elsewhere.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/369/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/369/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/368
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/368/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/368/comments
|
https://api.github.com/repos/huggingface/datasets/issues/368/events
|
https://github.com/huggingface/datasets/issues/368
| 654,087,251 |
MDU6SXNzdWU2NTQwODcyNTE=
| 368 |
load_metric can't acquire lock anymore
|
{
"login": "ydshieh",
"id": 2521628,
"node_id": "MDQ6VXNlcjI1MjE2Mjg=",
"avatar_url": "https://avatars.githubusercontent.com/u/2521628?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ydshieh",
"html_url": "https://github.com/ydshieh",
"followers_url": "https://api.github.com/users/ydshieh/followers",
"following_url": "https://api.github.com/users/ydshieh/following{/other_user}",
"gists_url": "https://api.github.com/users/ydshieh/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ydshieh/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ydshieh/subscriptions",
"organizations_url": "https://api.github.com/users/ydshieh/orgs",
"repos_url": "https://api.github.com/users/ydshieh/repos",
"events_url": "https://api.github.com/users/ydshieh/events{/privacy}",
"received_events_url": "https://api.github.com/users/ydshieh/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I found that, in the same process (or the same interactive session), if I do\r\n\r\nimport nlp\r\n\r\nm1 = nlp.load_metric('glue', 'mrpc')\r\nm2 = nlp.load_metric('glue', 'sst2')\r\n\r\nI will get the same error `ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id'`."
] | 2020-07-09T14:04:09 | 2020-07-10T13:45:20 | 2020-07-10T13:45:20 |
NONE
| null | null | null |
I can't load metric (glue) anymore after an error in a previous run. I even removed the whole cache folder `/home/XXX/.cache/huggingface/`, and the issue persisted. What are the steps to fix this?
Traceback (most recent call last):
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 101, in __init__
self.filelock.acquire(timeout=1)
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/filelock.py", line 278, in acquire
raise Timeout(self._lock_file)
filelock.Timeout: The file lock '/home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock' could not be acquired.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "examples_huggingface_nlp.py", line 268, in <module>
main()
File "examples_huggingface_nlp.py", line 242, in main
dataset, metric = get_dataset_metric(glue_task)
File "examples_huggingface_nlp.py", line 77, in get_dataset_metric
metric = nlp.load_metric('glue', glue_config, experiment_id=1)
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/load.py", line 440, in load_metric
**metric_init_kwargs,
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 104, in __init__
"Cannot acquire lock, caching file might be used by another process, "
ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id' for this run.
I0709 15:54:41.008838 139854118430464 filelock.py:318] Lock 139852058030936 released on /home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/368/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/368/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/365
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/365/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/365/comments
|
https://api.github.com/repos/huggingface/datasets/issues/365/events
|
https://github.com/huggingface/datasets/issues/365
| 653,845,964 |
MDU6SXNzdWU2NTM4NDU5NjQ=
| 365 |
How to augment data ?
|
{
"login": "astariul",
"id": 43774355,
"node_id": "MDQ6VXNlcjQzNzc0MzU1",
"avatar_url": "https://avatars.githubusercontent.com/u/43774355?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/astariul",
"html_url": "https://github.com/astariul",
"followers_url": "https://api.github.com/users/astariul/followers",
"following_url": "https://api.github.com/users/astariul/following{/other_user}",
"gists_url": "https://api.github.com/users/astariul/gists{/gist_id}",
"starred_url": "https://api.github.com/users/astariul/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/astariul/subscriptions",
"organizations_url": "https://api.github.com/users/astariul/orgs",
"repos_url": "https://api.github.com/users/astariul/repos",
"events_url": "https://api.github.com/users/astariul/events{/privacy}",
"received_events_url": "https://api.github.com/users/astariul/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Using batched map is probably the easiest way at the moment.\r\nWhat kind of augmentation would you like to do ?",
"Some samples in the dataset are too long, I want to divide them in several samples.",
"Using batched map is the way to go then.\r\nWe'll make it clearer in the docs that map could be used for augmentation.\r\n\r\nLet me know if you think there should be another way to do it. Or feel free to close the issue otherwise.",
"It just feels awkward to use map to augment data. Also it means it's not possible to augment data in a non-batched way.\r\n\r\nBut to be honest I have no idea of a good API...",
"Or for non-batched samples, how about returning a tuple ?\r\n\r\n```python\r\ndef aug(sample):\r\n # Simply copy the existing data to have x2 amount of data\r\n return sample, sample\r\n\r\ndataset = dataset.map(aug)\r\n```\r\n\r\nIt feels really natural and easy, but :\r\n\r\n* it means the behavior with batched data is different\r\n* I don't know how doable it is backend-wise\r\n\r\n@lhoestq ",
"As we're working with arrow's columnar format we prefer to play with batches that are dictionaries instead of tuples.\r\nIf we have tuple it implies to re-format the data each time we want to write to arrow, which can lower the speed of map for example.\r\n\r\nIt's also a matter of coherence, as we don't want users to be confused whether they have to return dictionaries for some functions and tuples for others when they're doing batches."
] | 2020-07-09T07:52:37 | 2020-07-10T09:12:07 | 2020-07-10T08:22:15 |
NONE
| null | null | null |
Is there any clean way to augment data ?
For now my work-around is to use batched map, like this :
```python
def aug(samples):
# Simply copy the existing data to have x2 amount of data
for k, v in samples.items():
samples[k].extend(v)
return samples
dataset = dataset.map(aug, batched=True)
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/365/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/365/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/362
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/362/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/362/comments
|
https://api.github.com/repos/huggingface/datasets/issues/362/events
|
https://github.com/huggingface/datasets/issues/362
| 653,766,245 |
MDU6SXNzdWU2NTM3NjYyNDU=
| 362 |
[dateset subset missing] xtreme paws-x
|
{
"login": "jerryIsHere",
"id": 50871412,
"node_id": "MDQ6VXNlcjUwODcxNDEy",
"avatar_url": "https://avatars.githubusercontent.com/u/50871412?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jerryIsHere",
"html_url": "https://github.com/jerryIsHere",
"followers_url": "https://api.github.com/users/jerryIsHere/followers",
"following_url": "https://api.github.com/users/jerryIsHere/following{/other_user}",
"gists_url": "https://api.github.com/users/jerryIsHere/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jerryIsHere/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jerryIsHere/subscriptions",
"organizations_url": "https://api.github.com/users/jerryIsHere/orgs",
"repos_url": "https://api.github.com/users/jerryIsHere/repos",
"events_url": "https://api.github.com/users/jerryIsHere/events{/privacy}",
"received_events_url": "https://api.github.com/users/jerryIsHere/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"You're right, thanks for pointing it out. We will update it "
] | 2020-07-09T05:04:54 | 2020-07-09T12:38:42 | 2020-07-09T12:38:42 |
CONTRIBUTOR
| null | null | null |
I tried nlp.load_dataset('xtreme', 'PAWS-X.es') but get the value error
It turns out that the subset for Spanish is missing
https://github.com/google-research-datasets/paws/tree/master/pawsx
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/362/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/362/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/361
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/361/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/361/comments
|
https://api.github.com/repos/huggingface/datasets/issues/361/events
|
https://github.com/huggingface/datasets/issues/361
| 653,757,376 |
MDU6SXNzdWU2NTM3NTczNzY=
| 361 |
🐛 [Metrics] ROUGE is non-deterministic
|
{
"login": "astariul",
"id": 43774355,
"node_id": "MDQ6VXNlcjQzNzc0MzU1",
"avatar_url": "https://avatars.githubusercontent.com/u/43774355?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/astariul",
"html_url": "https://github.com/astariul",
"followers_url": "https://api.github.com/users/astariul/followers",
"following_url": "https://api.github.com/users/astariul/following{/other_user}",
"gists_url": "https://api.github.com/users/astariul/gists{/gist_id}",
"starred_url": "https://api.github.com/users/astariul/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/astariul/subscriptions",
"organizations_url": "https://api.github.com/users/astariul/orgs",
"repos_url": "https://api.github.com/users/astariul/repos",
"events_url": "https://api.github.com/users/astariul/events{/privacy}",
"received_events_url": "https://api.github.com/users/astariul/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi, can you give a full self-contained example to reproduce this behavior?",
"> Hi, can you give a full self-contained example to reproduce this behavior?\r\n\r\nThere is a notebook in the post ;)",
"> If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different.\r\n> \r\n> Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n> \r\n> Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n> \r\n> > ['0.3350', '0.1470', '0.2329']\r\n> > ['0.3358', '0.1451', '0.2332']\r\n> \r\n> Why ROUGE is not deterministic ?\r\n\r\nThis is because of rouge's `BootstrapAggregator` that uses sampling to get confidence intervals (low, mid, high).\r\nYou can get deterministic scores per sentence pair by using\r\n```python\r\nscore = rouge.compute(rouge_types=[\"rouge1\", \"rouge2\", \"rougeL\"], use_aggregator=False)\r\n```\r\nOr you can set numpy's random seed if you still want to use the aggregator.",
"Maybe we can set all the random seeds of numpy/torch etc. while running `metric.compute` ?",
"We should probably indeed!",
"Now if you re-run the notebook, the two printed results are the same @colanim\r\n```\r\n['0.3356', '0.1466', '0.2318']\r\n['0.3356', '0.1466', '0.2318']\r\n```\r\nHowever across sessions, the results may change (as numpy's random seed can be different). You can prevent that by setting your seed:\r\n```python\r\nrouge = nlp.load_metric('rouge', seed=42)\r\n```",
"> \r\n\r\nMinor nit: Note that \"aggregator\" is misspelled in this command. Should be `use_aggregator=False`. ",
"Thanks, I fixed the code snippet"
] | 2020-07-09T04:39:37 | 2022-09-09T15:20:55 | 2020-07-20T23:48:37 |
NONE
| null | null | null |
If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different.
Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.
Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :
> ['0.3350', '0.1470', '0.2329']
['0.3358', '0.1451', '0.2332']
---
Why ROUGE is not deterministic ?
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/361/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/361/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/360
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/360/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/360/comments
|
https://api.github.com/repos/huggingface/datasets/issues/360/events
|
https://github.com/huggingface/datasets/issues/360
| 653,687,176 |
MDU6SXNzdWU2NTM2ODcxNzY=
| 360 |
[Feature request] Add dataset.ragged_map() function for many-to-many transformations
|
{
"login": "jarednielsen",
"id": 4564897,
"node_id": "MDQ6VXNlcjQ1NjQ4OTc=",
"avatar_url": "https://avatars.githubusercontent.com/u/4564897?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jarednielsen",
"html_url": "https://github.com/jarednielsen",
"followers_url": "https://api.github.com/users/jarednielsen/followers",
"following_url": "https://api.github.com/users/jarednielsen/following{/other_user}",
"gists_url": "https://api.github.com/users/jarednielsen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jarednielsen/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jarednielsen/subscriptions",
"organizations_url": "https://api.github.com/users/jarednielsen/orgs",
"repos_url": "https://api.github.com/users/jarednielsen/repos",
"events_url": "https://api.github.com/users/jarednielsen/events{/privacy}",
"received_events_url": "https://api.github.com/users/jarednielsen/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Actually `map(batched=True)` can already change the size of the dataset.\r\nIt can accept examples of length `N` and returns a batch of length `M` (can be null or greater than `N`).\r\n\r\nI'll make that explicit in the doc that I'm currently writing.",
"You're two steps ahead of me :) In my testing, it also works if `M` < `N`.\r\n\r\nA batched map of different length seems to work if you directly overwrite all of the original keys, but fails if any of the original keys are preserved.\r\n\r\nFor example,\r\n```python\r\n# Create a dummy dataset\r\ndset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")[\"test\"]\r\ndset = dset.map(lambda ex: {\"length\": len(ex[\"text\"]), \"foo\": 1})\r\n\r\n# Do an allreduce on each batch, overwriting both keys\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])], \"foo\": [1]})\r\n# Dataset(schema: {'length': 'int64', 'foo': 'int64'}, num_rows: 5)\r\n\r\n# Now attempt an allreduce without touching the `foo` key\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])]})\r\n# This fails with the error message below\r\n```\r\n\r\n```bash\r\n File \"/path/to/nlp/src/nlp/arrow_dataset.py\", line 728, in map\r\n arrow_schema = pa.Table.from_pydict(test_output).schema\r\n File \"pyarrow/io.pxi\", line 1532, in pyarrow.lib.Codec.detect\r\n File \"pyarrow/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n File \"pyarrow/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n File \"pyarrow/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named foo expected length 1 but got length 2\r\n```\r\n\r\nAdding the `remove_columns=[\"length\", \"foo\"]` argument to `map()` solves the issue. Leaving the above error for future visitors. Perfect, thank you!"
] | 2020-07-09T01:04:43 | 2020-07-09T19:31:51 | 2020-07-09T19:31:51 |
CONTRIBUTOR
| null | null | null |
`dataset.map()` enables one-to-one transformations. Input one example and output one example. This is helpful for tokenizing and cleaning individual lines.
`dataset.filter()` enables one-to-(one-or-none) transformations. Input one example and output either zero/one example. This is helpful for removing portions from the dataset.
However, some dataset transformations are many-to-many. Consider constructing BERT training examples from a dataset of sentences, where you map `["a", "b", "c"] -> ["a[SEP]b", "a[SEP]c", "b[SEP]c", "c[SEP]b", ...]`
I propose a more general `ragged_map()` method that takes in a batch of examples of length `N` and return a batch of examples `M`. This is different from the `map(batched=True)` method, which takes examples of length `N` and returns a batch of length `N`, processing individual examples in parallel. I don't have a clear vision of how this would be implemented efficiently and lazily, but would love to hear the community's feedback on this.
My specific use case is creating an end-to-end ELECTRA data pipeline. I would like to take the raw WikiText data and generate training examples from this using the `ragged_map()` method, then export to TFRecords and train quickly. This would be a reproducible pipeline with no bash scripts. Currently I'm relying on scripts like https://github.com/google-research/electra/blob/master/build_pretraining_dataset.py, which are less general.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/360/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/360/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/359
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/359/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/359/comments
|
https://api.github.com/repos/huggingface/datasets/issues/359/events
|
https://github.com/huggingface/datasets/issues/359
| 653,656,279 |
MDU6SXNzdWU2NTM2NTYyNzk=
| 359 |
ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures
|
{
"login": "timothyjlaurent",
"id": 2000204,
"node_id": "MDQ6VXNlcjIwMDAyMDQ=",
"avatar_url": "https://avatars.githubusercontent.com/u/2000204?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/timothyjlaurent",
"html_url": "https://github.com/timothyjlaurent",
"followers_url": "https://api.github.com/users/timothyjlaurent/followers",
"following_url": "https://api.github.com/users/timothyjlaurent/following{/other_user}",
"gists_url": "https://api.github.com/users/timothyjlaurent/gists{/gist_id}",
"starred_url": "https://api.github.com/users/timothyjlaurent/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/timothyjlaurent/subscriptions",
"organizations_url": "https://api.github.com/users/timothyjlaurent/orgs",
"repos_url": "https://api.github.com/users/timothyjlaurent/repos",
"events_url": "https://api.github.com/users/timothyjlaurent/events{/privacy}",
"received_events_url": "https://api.github.com/users/timothyjlaurent/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"Hi, it depends on what it is in your `dataset_builder.py` file. Can you share it?\r\n\r\nIf you are just loading `json` files, you can also directly use the `json` script (which will find the schema/features from your JSON structure):\r\n\r\n```python\r\nfrom nlp import load_dataset\r\nds = load_dataset(\"json\", data_files=rel_datafiles)\r\n```",
"The behavior I'm seeing is from the `json` script. \r\nI hacked this together to overcome the error with the `JSON` dataloader\r\n\r\n```\r\nclass DatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" We handle string, list and dicts in datafiles\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n # this is where the error is coming from\r\n # def parse_schema(schema, schema_dict):\r\n # for field in schema:\r\n # if pa.types.is_struct(field.type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type, schema_dict[field.name])\r\n # elif pa.types.is_list(field.type) and pa.types.is_struct(field.type.value_type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type.value_type, schema_dict[field.name])\r\n # else:\r\n # schema_dict[field.name] = Value(str(field.type))\r\n # \r\n # parse_schema(writer.schema, features)\r\n # self.info.features = Features(features)\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n```\r\n\r\nSo I basically just don't populate the `self.info.features` though this doesn't seem to cause any problems in my downstream applications. \r\n\r\nThe other workaround I was doing was to just use pyarrow.json to build a table and then to create the Dataset with its constructor or from_table methods. `load_dataset` has nice split logic, so I'd prefer to use that.\r\n\r\n",
"Also noticed that if you for example in a loader script\r\n\r\n```\r\nfrom nlp import ArrowBasedBuilder\r\n\r\nclass MyBuilder(ArrowBasedBuilder):\r\n...\r\n\r\n```\r\nand use that in the subclass, it will be on the module's __dict__ and will be selected before the `MyBuilder` subclass, and it will raise `NotImplementedError` on its `_generate_examples` method... In the code it check for abstract classes but Builder and ArrowBasedBuilder aren't abstract classes, they're regular classes with `@abstract_methods`.",
"Indeed this is part of a more general limitation which is the fact that we should generate and update the `features` from the auto-inferred Arrow schema when they are not provided (also happen when a user change the schema using `map()`, the features should be auto-generated and guessed as much as possible to keep the `features` synced with the underlying Arrow table schema).\r\n\r\nWe will try to solve this soon."
] | 2020-07-08T23:24:05 | 2020-07-10T14:52:06 | 2020-07-10T14:52:06 |
NONE
| null | null | null |
I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-23-9aecfbee53bd> in <module>
55 from nlp import load_dataset
56
---> 57 ds = load_dataset("../text2struct/model/dataset_builder.py", data_files=rel_datafiles)
58
59
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
481 try:
482 # Prepare split will record examples associated to the split
--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)
484 except OSError:
485 raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _prepare_split(self, split_generator)
736 schema_dict[field.name] = Value(str(field.type))
737
--> 738 parse_schema(writer.schema, features)
739 self.info.features = Features(features)
740
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in parse_schema(schema, schema_dict)
734 parse_schema(field.type.value_type, schema_dict[field.name])
735 else:
--> 736 schema_dict[field.name] = Value(str(field.type))
737
738 parse_schema(writer.schema, features)
<string> in __init__(self, dtype, id, _type)
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in __post_init__(self)
55
56 def __post_init__(self):
---> 57 self.pa_type = string_to_arrow(self.dtype)
58
59 def __call__(self):
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in string_to_arrow(type_str)
32 if str(type_str + "_") not in pa.__dict__:
33 raise ValueError(
---> 34 f"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. "
35 f"Please make sure to use a correct data type, see: "
36 f"https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions"
ValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
```
If I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well.
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/359/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/359/timeline
| null |
completed
|
https://api.github.com/repos/huggingface/datasets/issues/355
|
https://api.github.com/repos/huggingface/datasets
|
https://api.github.com/repos/huggingface/datasets/issues/355/labels{/name}
|
https://api.github.com/repos/huggingface/datasets/issues/355/comments
|
https://api.github.com/repos/huggingface/datasets/issues/355/events
|
https://github.com/huggingface/datasets/issues/355
| 653,451,013 |
MDU6SXNzdWU2NTM0NTEwMTM=
| 355 |
can't load SNLI dataset
|
{
"login": "jxmorris12",
"id": 13238952,
"node_id": "MDQ6VXNlcjEzMjM4OTUy",
"avatar_url": "https://avatars.githubusercontent.com/u/13238952?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/jxmorris12",
"html_url": "https://github.com/jxmorris12",
"followers_url": "https://api.github.com/users/jxmorris12/followers",
"following_url": "https://api.github.com/users/jxmorris12/following{/other_user}",
"gists_url": "https://api.github.com/users/jxmorris12/gists{/gist_id}",
"starred_url": "https://api.github.com/users/jxmorris12/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jxmorris12/subscriptions",
"organizations_url": "https://api.github.com/users/jxmorris12/orgs",
"repos_url": "https://api.github.com/users/jxmorris12/repos",
"events_url": "https://api.github.com/users/jxmorris12/events{/privacy}",
"received_events_url": "https://api.github.com/users/jxmorris12/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[
"I just added the processed files of `snli` on our google storage, so that when you do `load_dataset` it can download the processed files from there :)\r\n\r\nWe are thinking about having available those processed files for more datasets in the future, because sometimes files aren't available (like for `snli`), or the download speed is too slow, or sometimes the files take time to be processed.",
"Closing this one. Feel free to re-open if you have other questions :)",
"Thank you!"
] | 2020-07-08T16:54:14 | 2020-07-18T05:15:57 | 2020-07-15T07:59:01 |
CONTRIBUTOR
| null | null | null |
`nlp` seems to load `snli` from some URL based on nlp.stanford.edu. This subdomain is frequently down -- including right now, when I'd like to load `snli` in a Colab notebook, but can't.
Is there a plan to move these datasets to huggingface servers for a more stable solution?
Btw, here's the stack trace:
```
File "/content/nlp/src/nlp/builder.py", line 432, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/content/nlp/src/nlp/builder.py", line 466, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/content/nlp/src/nlp/datasets/snli/e417f6f2e16254938d977a17ed32f3998f5b23e4fcab0f6eb1d28784f23ea60d/snli.py", line 76, in _split_generators
dl_dir = dl_manager.download_and_extract(_DATA_URL)
File "/content/nlp/src/nlp/utils/download_manager.py", line 217, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in download
lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
File "/content/nlp/src/nlp/utils/py_utils.py", line 190, in map_nested
return function(data_struct)
File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in <lambda>
lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
File "/content/nlp/src/nlp/utils/file_utils.py", line 198, in cached_path
local_files_only=download_config.local_files_only,
File "/content/nlp/src/nlp/utils/file_utils.py", line 356, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://nlp.stanford.edu/projects/snli/snli_1.0.zip
```
|
{
"url": "https://api.github.com/repos/huggingface/datasets/issues/355/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/huggingface/datasets/issues/355/timeline
| null |
completed
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.