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| 1,257,496,552 |
I_kwDODunzps5K89_o
| 4,435 |
Load a local cached dataset that has been modified
|
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[
"Hi! `datasets` caches every modification/loading, so you can either rerun the pipeline up to the `map` call or use `Dataset.from_file(modified_dataset)` to load the dataset directly from the cache file.",
"Awesome, hvala Mario! This works. "
] | 2022-06-02T01:51:49 | 2022-06-02T23:59:26 | 2022-06-02T23:59:18 |
NONE
| null | null | null |
## Describe the bug
I have loaded a dataset as follows:
```
d = load_dataset("emotion", split="validation")
```
Afterwards I make some modifications to the dataset via a `map` call:
```
d.map(some_update_func, cache_file_name=modified_dataset)
```
This generates a cached version of the dataset on my local system in the same directory as the original download of the data (/path/to/cache). Running an `ls` returns:
```
modified_dataset
dataset_info.json
emotion-test.arrow
emotion-train.arrow
emotion-validation.arrow
```
as expected. However, when I try to load up the modified cached dataset via a call to
```
modified = load_dataset("emotion", split="validation", data_files="/path/to/cache/modified_dataset")
```
it simply redownloads a new version of the dataset and dumps to a new cache rather than loading up the original modified dataset:
```
Using custom data configuration validation-cdbf51685638421b
Downloading and preparing dataset emotion/validation to ...
```
How am I supposed to load the original modified local cache copy of the dataset?
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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| 4,430 |
Add ability to load newer, cleaner version of Multi-News
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[
"Hi! Our versioning is based on Git revisions (the `revision` param in `load_dataset`), so you can just replace the old URL with the new one and open a PR :). I can also give you some pointers if needed.",
"@mariosasko Awesome thanks! I will do that. Looks like this new version of the data is not available as a zip but as three files (train/dev/test). How is this usually handled in HF Datasets, should `_URL` be a dict with keys `train`, `val`, `test` perhaps?",
"Yes! Let me help you with more detailed instructions.\r\n\r\nIn the first step, we need to update the URLs. One of the possible dictionary structures is as follows:\r\n```python\r\n_URLs = {\r\n \"train\": {\"src\": \"https://drive.google.com/uc?export=download&id=1wHAWDOwOoQWSj7HYpyJ3Aeud8WhhaJ7P\", \"tgt\": \"https://drive.google.com/uc?export=download&id=1QVgswwhVTkd3VLCzajK6eVkcrSWEK6kq\"}\r\n \"val\": ...\r\n \"test\": ...\r\n}\r\n```\r\n\r\n(You can use this page to generate direct download links: https://sites.google.com/site/gdocs2direct/)\r\n\r\nThen we move to the `split_generators` method:\r\n```python\r\ndef _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n files = dl_manager.download(_URLs)\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={\"src_file\": files[\"train\"][\"src\"], \"tgt_file\": files[\"train\"][\"tgt\"]},\r\n ),\r\n ... # same for val and test\r\n ]\r\n```\r\nFinally, we adjust the signature of `_generate_examples`:\r\n```python\r\ndef _generate_examples(self, src_file, tgt_file):\r\n \"\"\"Yields examples.\"\"\"\r\n with open(src_file, encoding=\"utf-8\") as src_f, open(\r\n tgt_file, encoding=\"utf-8\"\r\n ) as tgt_f:\r\n ... # the rest is the same\r\n```\r\n\r\nAnd that's it!\r\n\r\nPS: Let me know if you need help updating the dummy data and regenerating the metadata file.",
"Awesome! Thanks for the detailed help, that was straightforward with your instruction. However, I think I am being blocked by this issue: https://github.com/huggingface/datasets/issues/4428",
"Feel free to open a PR, and I can fix this manually.",
"Awsome, done in #4451!"
] | 2022-05-31T21:00:44 | 2022-06-07T17:14:44 | 2022-06-07T17:14:44 |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
The [Multi-News dataloader points to the original version of the Multi-News dataset](https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/datasets/multi_news/multi_news.py#L47), but this has [known errors in it](https://github.com/Alex-Fabbri/Multi-News/issues/11). There exists a [newer version which fixes some of these issues](https://drive.google.com/open?id=1jwBzXBVv8sfnFrlzPnSUBHEEAbpIUnFq).
Unfortunately I don't think you can just replace this old URL with the new one, otherwise this could lead to issues with reproducibility.
**Describe the solution you'd like**
Add a new version to the Multi-News dataloader that points to the updated dataset which has fixes for some known issues.
**Describe alternatives you've considered**
Replace the current URL to the original version to the dataset with the URL to the version with fixes.
**Additional context**
Would be happy to make a PR for this, could someone maybe point me to another dataloader that has multiple versions so I can see how this is handled in `datasets`?
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I_kwDODunzps5Kv_AS
| 4,428 |
Errors when building dummy data if you use nested _URLS
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[] | 2022-05-31T16:10:57 | 2022-06-07T09:24:09 | 2022-06-07T09:24:09 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
When making dummy data with the `datasets-cli dummy_data` tool,
an error will be raised if you use a nested _URLS in your dataset script.
Traceback (most recent call last):
File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 43, in <module>
main()
File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 311, in run
self._autogenerate_dummy_data(
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 337, in _autogenerate_dummy_data
dataset_builder._split_generators(dl_manager)
File "/home/name/.cache/huggingface/modules/datasets_modules/datasets/personal_dialog/559332bced5eeafa7f7efc2a7c10ce02cee2a8116bbab4611c35a50ba2715b77/personal_dialog.py", line 108, in _split_generators
data_dir = dl_manager.download_and_extract(urls)
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 56, in download_and_extract
dummy_output = self.mock_download_manager.download(url_or_urls)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 130, in download
return self.download_and_extract(data_url)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 122, in download_and_extract
return self.create_dummy_data_dict(dummy_file, data_url)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 165, in create_dummy_data_dict
if isinstance(first_value, str) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()):
TypeError: unhashable type: 'list'
## Steps to reproduce the bug
You can use my dataset script implemented here:
https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py
```python
datasets_cli dummy_data datasets/personal_dialog --auto_generate
```
You can change https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py#L54
to
```
"train": "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_random.jsonl.gz"
```
before runing the above script to avoid downloading a large training data.
## Expected results
The dummy data should be generated
## Actual results
An error is raised.
It seems that in https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165
We only check if the first item of dummy_data_dict.values() is str.
However, dummy_data_dict.values() may have the type of [str, list, list].
A simple fix would be changing https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165 to
```python
if all([isinstance(value, str) for value in dummy_data_dict.values()]) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()):
```
But I don't know if this kinds of change may bring any side effect since I am not sure about the detail logic here.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: Python 3.9.10
- PyArrow version: 7.0.0
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| 4,426 |
Add loading variable number of columns for different splits
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[
"Hi! Indeed the column is missing, but you shouldn't get an error? Have you made some modifications (locally) to the loading script? I've opened a PR to add the missing columns to the script. "
] | 2022-05-31T13:40:16 | 2022-06-03T16:25:25 | 2022-06-03T16:25:25 |
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
The original dataset `blended_skill_talk` consists of different sets of columns for the different splits: (test/valid) splits have additional data column `label_candidates` that the (train) doesn't have.
When loading such data, an exception occurs at table.py:cast_table_to_schema, because of mismatched columns.
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| 4,422 |
Cannot load timit_asr data set
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[
"Thanks for reporting, @bhaddow.\r\n\r\nI'm fixing it.",
"Thanks for the quick fix!",
"@bhaddow we have also made a fix so that you don't have to convert to uppercase the file extensions of the LDC data.\r\n\r\nWould you mind checking if it works OK now for you and reporting if there are any issues? Thanks. ",
"Hi @albertvillanova -It loads fine on a copy of the data from deepai - although I have to remove the copies of the .WAV files (with extension .WAV,wav). On a copy of the data that was obtained from the LDC, the glob still fails to find the files. The LDC copy looks like it was copied from CD, in 2004, so the structure may be different to a current download.",
"Ah, if I change the train/ and test/ directories to TRAIN/ and TEST/ then it works!",
"Thanks for your investigation and report, @bhaddow. I'm adding another fix for the TRAIN/train and TEST/test directory names."
] | 2022-05-30T22:00:22 | 2022-06-02T06:34:05 | 2022-05-31T13:42:31 |
NONE
| null | null | null |
## Describe the bug
I am trying to load the timit_asr data set. I have tried with a copy from the LDC, and a copy from deepai. In both cases they fail with a "duplicate key" error. With the LDC version I have to convert the file extensions all to upper-case before I can load it at all.
## Steps to reproduce the bug
```python
timit = datasets.load_dataset("timit_asr", data_dir = "/path/to/dataset")
# Sample code to reproduce the bug
```
## Expected results
The data set should load without error. It worked for me before the LDC url change.
## Actual results
```
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: SA1
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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I_kwDODunzps5Kq0in
| 4,420 |
Metric evaluation problems in multi-node, shared file system
|
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[
"If you call `metric.compute` in a distributed setup like yours, then `metric.compute` is called in each process. `metric.compute` first calls `metric.add_batch`, and it looks like your error appears at that stage.\r\n\r\nTo make sure that all the processes have started writing their predictions/references at the same time, each process waits for process 0 to lock `slurm-{world_size}-0.arrow.lock`. Process 0 locks this file when `metric.add_batch` is called, so here when `metric.compute` is called.\r\n\r\nTherefore your error can happen when process 0 takes too much time to call `metric.compute` compared to process 3 (>100 seconds by default). I haven't tried running your code but could it be the case ?\r\n\r\nI guess it could also happen if you run multiple times the same distributed job at the same time with the same `experiment_id` because they would collide.\r\n",
"We've finally been able to isolate the problem, it wasn't a timing problem, but rather a file locking one. \r\nThe locks produced by calling `flock` where not visible between nodes (so the master node couldn't check other node's locks nor the other way around). \r\n\r\nWe are now having issues with the pre-processing in our runner script, but are not related with the rendezvous process during the evaluation phase. We will let you know about it once we address it. \r\n\r\nOur solution to the rendezvous is as follows:\r\n- We solved the problem by calling `lockf` instead of `flock`.\r\n- We had to change slightly the `_check_all_processes_locks` method so that the main process (i.e. process 0) didn't check it's own lock (because `lockf` permits recursive locks and thus checking it only replaced the current lock with a new one). \r\n\r\nWe use a shared file system between nodes using GPFS in our cluster setup. Maybe the difference between the behavior we see with respect to your usage in multi-node executions comes from that fact. Which file system scheme do you use for the multi-node executions? \r\n\r\n`lockf` seems to work in more settings than `flock`, so maybe we could write a PR so you could test it in your environment. ",
"Cool, I'm glad you managed to make evaluation work :)\r\n\r\nI'm not completely aware of the differences between lockf and flock, but I've read somewhere that flock is preferable over lockf in multithreading and multiprocessing situations. Here we definitely are in such a situation so unless it is super important I don't think we will switch to lockf",
"> * We had to change slightly the `_check_all_processes_locks` method so that the main process (i.e. process 0) didn't check it's own lock (because `lockf` permits recursive locks and thus checking it only replaced the current lock with a new one).\r\n\r\nHi @panserbjorn , Can you share your `_check_all_processes_locks` function? thanks!",
"```\r\ndef _check_all_processes_locks(self):\r\n expected_lock_file_names = [\r\n os.path.join(self.data_dir, f\"{self.experiment_id}-{self.num_process}-{process_id}.arrow.lock\")\r\n for process_id in range(self.num_process)\r\n ]\r\n #for expected_lock_file_name in expected_lock_file_names: # OUR CHANGE process 0 shouldn't check its own lock\r\n for expected_lock_file_name in expected_lock_file_names[1:]:\r\n nofilelock = FileFreeLock(expected_lock_file_name)\r\n try:\r\n nofilelock.acquire(timeout=self.timeout)\r\n except Timeout:\r\n raise ValueError(\r\n f\"Expected to find locked file {expected_lock_file_name} from process {self.process_id} but it doesn't exist.\"\r\n )\r\n else:\r\n nofilelock.release()\r\n```\r\n\r\n### Changed files:\r\n- metric.py file in the datasets library \r\n- filelock.py file in the datasets/utils library. \r\n\r\n\r\nChanges we made:\r\n\r\n1. We changed the flock for lockf \r\n flock and lockf both perform a lock over a file (like the lock for writing). \r\n The difference is that flock only works in local file systems, but if you have a shared file system (like what we have in the clusters) the flock fails to “see” the lock of another node. The only disadvantage we had was that a single process couldn’t detect it’s own lock so we did the second change.\r\n2. We prevented the process 0 (which is the one that coordinates the rendezvous) from checking its own lock on its arrow because it didn't work with lockf (as stated in the previous change). \r\n3. We made a second rendezvous so that all the process had the results of the metrics (other than the loss) and not only the process 0.\r\n What happened was that only process 0 computed the metric and that didn’t present any problem if you are using the loss. However, if you are using another metric, the only process which had the information to choose the best checkpoint at evaluation time was the process 0. But since the evaluation was performed over all processes, every process except the process 0 chose a bad check point (bad meaning it wasn’t the best one) because they didn’t have the information of the metric of the best checkpoint. \r\n The consequence was that the evaluation was different from what would result if using only the best checkpoint, because each process chose a different checkpoint to run the evaluation and thus the numbers were often worse than the numbers that would be obtained if all processes choose the best checkpoint (correct one) to perform the evaluation of their samples. \r\n We performed a second rendezvous so that all processes had the same best_metric and best_model as process 0 after the evaluation cycle. \r\n",
"Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate"
] | 2022-05-30T13:24:05 | 2023-07-11T09:33:18 | 2023-07-11T09:33:17 |
NONE
| null | null | null |
## Describe the bug
Metric evaluation fails in multi-node within a shared file system, because the master process cannot find the lock files from other nodes. (This issue was originally mentioned in the transformers repo https://github.com/huggingface/transformers/issues/17412)
## Steps to reproduce the bug
1. clone [this huggingface model](https://huggingface.co/PereLluis13/wav2vec2-xls-r-300m-ca-lm) and replace the `run_speech_recognition_ctc.py` script with the version in the gist [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71#file-run_speech_recognition_ctc-py).
2. Setup the `venv` according to the requirements of the model file plus `datasets==2.0.0`, `transformers==4.18.0` and `torch==1.9.0`
3. Launch the runner in a distributed environment which has a shared file system for two nodes, preferably with SLURM. Example [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71)
Specifically for the datasets, for the distributed setup the `load_metric` is called as:
```
process_id=int(os.environ["RANK"])
num_process=int(os.environ["WORLD_SIZE"])
eval_metrics = {metric: load_metric(metric,
process_id=process_id,
num_process=num_process,
experiment_id="slurm")
for metric in data_args.eval_metrics}
```
## Expected results
The training should not fail, due to the failure of the `Metric.compute()` step.
## Actual results
For the test I am executing the world size is 4, with 2 GPUs in 2 nodes. However the process is not finding the necessary lock files
```
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 841, in <module>
main()
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 792, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1497, in train
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1624, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2291, in evaluate
metric_key_prefix=metric_key_prefix,
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2535, in evaluation_loop
metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in compute_metrics
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in <dictcomp>
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 419, in compute
self.add_batch(**inputs)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 465, in add_batch
self._init_writer()
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 552, in _init_writer
self._check_rendez_vous() # wait for master to be ready and to let everyone go
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 342, in _check_rendez_vous
) from None
ValueError: Expected to find locked file /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock from process 3 but it doesn't exist.
```
When I look at the cache directory, I can see all the lock files in principle:
```
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-rdv.lock
```
I see that there was another related issue here https://github.com/huggingface/datasets/issues/1942, but it seems to have resolved via https://github.com/huggingface/datasets/pull/1966. Let me know if there is problem with how I am calling the `load_metric` or whether I need to make changes to the `.compute()` steps.
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-147.8.1.el8_1.x86_64-x86_64-with-centos-8.1.1911-Core
- Python version: 3.7.4
- PyArrow version: 7.0.0
- Pandas version: 1.3.0
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| 4,419 |
Update `unittest` assertions over tuples from `assertEqual` to `assertTupleEqual`
|
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[
"Hi! If the only goal is to improve readability, it's better to use `assertTupleEqual` than `assertSequenceEqual` for Python tuples. Also, note that this function is called internally by `assertEqual`, but I guess we can accept a PR to be more verbose.",
"Hi @mariosasko, right! I'll update the issue title/desc with `assertTupleEqual` even though as you said it seems to be internally using `assertEqual` so I'm not sure whether it's worth it or not...\r\n\r\nhttps://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual",
"I thought we were supposed to move gradually from `unittest` to `pytest`..."
] | 2022-05-30T12:13:18 | 2022-09-30T16:01:37 | 2022-09-30T16:01:37 |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
So this is more a readability improvement rather than a proposal, wouldn't it be better to use `assertTupleEqual` over the tuples rather than `assertEqual`? As `unittest` added that function in `v3.1`, as detailed at https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual, so maybe it's worth updating.
Find an example of an `assertEqual` over a tuple in 🤗 `datasets` unit tests over an `ArrowDataset` at https://github.com/huggingface/datasets/blob/0bb47271910c8a0b628dba157988372307fca1d2/tests/test_arrow_dataset.py#L570
**Describe the solution you'd like**
Start slowly replacing all the `assertEqual` statements with `assertTupleEqual` if the assertion is done over a Python tuple, as we're doing with the Python lists using `assertListEqual` rather than `assertEqual`.
**Additional context**
If so, please let me know and I'll try to go over the tests and create a PR if applicable, otherwise, if you consider this should stay as `assertEqual` rather than `assertSequenceEqual` feel free to close this issue! Thanks 🤗
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how to convert a dict generator into a huggingface dataset.
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[
"@albertvillanova @lhoestq , could you please help me on this issue. ",
"Hi ! As mentioned on the [forum](https://discuss.huggingface.co/t/how-to-wrap-a-generator-with-hf-dataset/18464), the simplest for now would be to define a [dataset script](https://huggingface.co/docs/datasets/dataset_script) which can contain your generator. But we can also explore adding something like `ds = Dataset.from_iterable(seqio_dataset)`",
"@lhoestq , hey i did as you instructed, but sadly i cannot get pass through the download_manager, as i dont have anything to download. i was skipping the ` def _split_generators(self, dl_manager):` function. but i cannot get around it. I get a `NotImplementedError: `\r\n\r\nthe following is my code for the same: \r\n\r\n\r\n\r\n```\r\nimport datasets \r\nimport functools\r\nimport glob \r\nfrom datasets import load_from_disk\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_dataset\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\ndata_path = glob.glob(\"/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*\", recursive=False)\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column=\"text\", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\[email protected]_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n \"\"\"Assign the value from the dataset to target_key in key_map\"\"\"\r\n return {**key_map, target_key: x}\r\n\r\n\r\n_CITATION = \"Not ready yet\"\r\n_DESCRIPTION = \"a custom seqio based mixed samples on a given temperature value, that again returns a dataset in HF dataset format well samples on the Mixture temperature\"\r\n_HOMEPAGE = \"ldcil.org\"\r\n\r\nclass CustomSeqio(datasets.GeneratorBasedBuilder):\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n }\r\n ),\r\n homepage=\"https://ldcil.org\",\r\n citation=_CITATION,)\r\n\r\ndef generate_examples(self):\r\n seqio_train_list = []\r\n for lang in data_path:\r\n dataset_name = lang.split(\"/\")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=(\"train\", \"test\"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n \"targets\": None,\r\n }, target_key=\"targets\")],\r\n output_features={\"targets\": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n \r\n lang_name_list = []\r\n for lang in data_path:\r\n lang_name = lang.split(\"/\")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\n seqio_mixture = seqio.MixtureRegistry.add(\r\n \"seqio_mixture\",\r\n lang_name_list,\r\n default_rate=0.7)\r\n \r\n seqio_mixture_dataset = seqio.get_mixture_or_task(\"seqio_mixture\").get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\n for id, ex in enumerate(seqio_mixture_dataset):\r\n yield id, {\"text\": ex[\"targets\"].numpy().decode()}\r\n```\r\n\r\nand i load it by:\r\n\r\n`seqio_mixture = load_dataset(\"seqio_loader\")`",
"@lhoestq , just to make things clear ... \r\n\r\nthe following is my original code, thats not in the HF dataset loading script: \r\n\r\n```\r\nimport functools\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_from_disk\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\nimport glob \r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column=\"text\", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\n\r\[email protected]_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n \"\"\"Assign the value from the dataset to target_key in key_map\"\"\"\r\n return {**key_map, target_key: x}\r\n\r\ndata_path = glob.glob(\"/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*\", recursive=False)\r\n\r\nseqio_train_list = []\r\n\r\nfor lang in data_path:\r\n dataset_name = lang.split(\"/\")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=(\"train\", \"test\"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n \"targets\": None,\r\n }, target_key=\"targets\")],\r\n output_features={\"targets\": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n\r\nlang_name_list = []\r\nfor lang in data_path:\r\n lang_name = lang.split(\"/\")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\nseqio_mixture = seqio.MixtureRegistry.add(\r\n \"seqio_mixture\",\r\n lang_name_list,\r\n default_rate=0.7\r\n)\r\n\r\nseqio_mixture_dataset = seqio.get_mixture_or_task(\"seqio_mixture\").get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\nfor _, ex in zip(range(15), seqio_mixture_dataset):\r\n print(ex[\"targets\"].numpy().decode())\r\n```\r\n\r\nwhere the seqio_mixture_dataset is the generator that i wanted to be wrapped in HF dataset. \r\n\r\nalso additionally, could you please tell me how do i set the `default_rate=0.7` args where `seqio_mixture` is defined to be made as a custom option in the HF load_dataset() method,\r\n\r\nmaybe like this: \r\n`seqio_mixture_dataset = datasets.load_dataset(\"seqio_loader\",temperature=0.5)`",
"I like the idea of having `Dataset.from_iterable(iterable)` in the API. The only problem is that we also want to make this part cachable, which is tricky if `iterable` is a generator. \r\n\r\nSome resources on this issue:\r\n* https://github.com/uqfoundation/dill/issues/311\r\n* https://stackoverflow.com/questions/7180212/why-cant-generators-be-pickled\r\n* https://github.com/tonyroberts/generator_tools - python package for pickling generators; pickles bytecode, so it creates version-specific dumps",
"For the caching maybe we can have `Dataset.from_generator` as TF and pickle+hash the generator function (not the generator object itself) ?\r\n\r\nAnd then keep `Dataset.from_iterable` fo pickable objects like lists",
"@lhoestq, @mariosasko do you too have any examples where the dataset is a generator and needs to be wrapped into hf dataset ? ",
"@lhoestq, following to my previous question ... what possibly could be done in this [link1](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627404) [link2](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627593) case? do you have any ideas? ",
"@lhoestq +1 for the `Dataset.from_generator` idea.\r\n\r\nHaving thought about it, let's avoid adding `Dataset.from_iterable` to the API since dictionaries are technically iteralbles (\"iterable\" is a broad term in Python), and we already provide `Dataset.from_dict`. And for lists maybe we can add `Dataset.from_list` similar to `pa.Table.from_pylist`. WDYT?\r\n",
"Hi @StephennFernandes!\r\n\r\nTo fix the issues in the copied code, rename `generate_examples` to` _generate_examples` and add one level of indentation as this is a method of `GeneratorBasedBuilder` and define `_split_generators` as follows (again as a method of `GeneratorBasedBuilder):\r\n```python\r\n def _split_generators(self, dl_manager):\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={},\r\n ),\r\n ]\r\n```\r\n\r\nAnd if you are feeling extra adventurous, you can try to use ArrowWriter to directly create a cache file:\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets.arrow_writer import ArrowWriter\r\n\r\nwriter = ArrowWriter(path=\"path/to/cache_file.arrow\", writer_batch_size=1000)\r\n\r\nwith writer:\r\n for ex in generator:\r\n writer.write(ex) \r\n writer.finalize()\r\n\r\ndset = Dataset.from_file(\"path/to/cache_file.arrow\")\r\n```\r\n\r\n",
"I have a problem which I think is very similar: I would like to \"stream\" data to a HF Array (memory-mapped) Dataset, where the final size of the dataset is unknown, but could be much larger than what fits into memory.\r\nWhat I want to end up with is an Array Dataset which I can open using `Dataset.load_from_disk(dataset_path=\"somename\")` and use e.g. as the training set. \r\n\r\nFor this I would have thought there should be an API which allows me to open/create the dataset (and define the features etc), then write examples to the dataset, but I could not find a way to do this. \r\n\r\nI tried doing this and it looks like it works, but it feels very hacky and I am not sure if this might fail to update some of the fields in the json files which may turn out to be important:\r\n```\r\nfrom datasets import Dataset, Features, ClassLabel, Sequence, Value\r\nfrom datasets.arrow_writer import ArrowWriter \r\n# 1) define the features\r\nfeatures = Features(dict(\r\n id=Value(dtype=\"string\"),\r\n tokens=Sequence(feature=Value(dtype=\"string\")),\r\n ner_tags=Sequence(feature=ClassLabel(names=['O', 'B-corporation', 'I-corporation', 'B-creative-work', 'I-creative-work', 'B-group', 'I-group', 'B-location', 'I-location', 'B-person', 'I-person', 'B-product', 'I-product'])),\r\n))\r\n# 2) create empty dataset for examples with these features and store to disk\r\nempty = dict(\r\n id = [],\r\n tokens = [],\r\n ner_tags = [],\r\n)\r\nds = Dataset.from_dict(empty, features=features)\r\nds.save_to_disk(dataset_path=\"debug_ds1\")\r\n\r\n# 3) directly write all the examples to the arrow dataset \r\nwith ArrowWriter(path=\"debug_ds1/dataset.arrow\") as writer: \r\n writer.write(dict(id=0, tokens=[\"a\", \"b\"], ner_tags=[0, 0])) \r\n writer.write(dict(id=1, tokens=[\"x\", \"y\"], ner_tags=[1, 0])) \r\n writer.finalize() \r\n \r\nds2 = Dataset.load_from_disk(dataset_path=\"debug_ds1\")\r\nlen(ds2)\r\n```\r\nIs there a cleaner/proper way to do this?\r\n\r\nI like the sound of `Dataset.from_iterable` or `Dataset.from_generator` (should not from iterable be able to handle from generator too as all generators are iterables?) but how would I define the features for me examples there? ",
"Hi @johann-petrak! You can pass the features directly to ArrowWriter's initializer like so `ArrowWriter(..., features=features)`.\r\n\r\nAnd the reason why I prefer `Dataset.from_generator` over `Dataset.from_iterable` is mentioned in one of my previous comments.",
"@mariosasko so at the moment we still have to create a fake `Dataset` first and then use `ArrowWriter` to write an actual dataset? I'm using the latest version of `datasets` on pypi but my final file is always empty. Is there anything wrong with the code below?\r\n\r\n```python\r\n total = 0\r\n with ArrowWriter(path=str(final_data_path), features=features) as writer:\r\n for batch in loader:\r\n for traj in batch:\r\n for generator in question_generators:\r\n for xi in generator(traj):\r\n # print(f\"Question: {xi.question}, answer: {xi.answer}\")\r\n total += 1\r\n writer.write(\r\n {\r\n \"id\": f\"qa_{total}\",\r\n \"question\": xi.question,\r\n \"answer\": xi.answer,\r\n }\r\n )\r\n writer.finalize()\r\n print(f\"Total #questions = {total}\") # this prints 402\r\n```",
"This works for me if I then (actually I also close the writer: `writer.close()`) open the Arrow file as a dataset using `ds=Dataset.from_file(final_data_path)` then `ds.save_to_disk(somedir)`. The Dataset created that way contains the expected examples.",
"Oh thanks. That did the trick I believe. Shouldn't ArrowWriter have a context manager that does these operations?",
"You can just use `Dataset.from_file` to get your dataset, no need to do an extra `save_to_disk` somewhere else ;)",
"I was thinking that `save_to_disk` is necessary when one wants to re-use that dataset as a proper HF dataset later, no?\r\nAt least what I wanted to achieve is create a dataset that can be opened like any other local or remote dataset. ",
"`save_to_disk`/`load_from_disk` is indeed more general, e.g. it supports datasets that consist in several files, and saves some extra info in a dataset_info.json file (description, citation, split sizes, etc.)\r\n\r\nIf you have one single file it's fine to simply do `.from_file()`"
] | 2022-05-29T16:28:27 | 2022-09-16T14:44:19 | 2022-09-16T14:44:19 |
NONE
| null | null | null |
### Link
_No response_
### Description
Hey there, I have used seqio to get a well distributed mixture of samples from multiple dataset. However the resultant output from seqio is a python generator dict, which I cannot produce back into huggingface dataset.
The generator contains all the samples needed for training the model but I cannot convert it into a huggingface dataset.
The code looks like this:
```
for ex in seqio_data:
print(ex[“text”])
```
I need to convert the seqio_data (generator) into huggingface dataset.
the complete seqio code goes here:
```
import functools
import seqio
import tensorflow as tf
import t5.data
from datasets import load_dataset
from t5.data import postprocessors
from t5.data import preprocessors
from t5.evaluation import metrics
from seqio import FunctionDataSource, utils
TaskRegistry = seqio.TaskRegistry
def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_params=None):
dataset = load_dataset(**dataset_params)
if shuffle:
if seed:
dataset = dataset.shuffle(seed=seed)
else:
dataset = dataset.shuffle()
while True:
for item in dataset[str(split)]:
yield item[column]
def dataset_fn(split, shuffle_files, seed=None, dataset_params=None):
return tf.data.Dataset.from_generator(
functools.partial(gen_dataset, split, shuffle_files, seed, dataset_params=dataset_params),
output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_name)
)
@utils.map_over_dataset
def target_to_key(x, key_map, target_key):
"""Assign the value from the dataset to target_key in key_map"""
return {**key_map, target_key: x}
dataset_name = 'oscar-corpus/OSCAR-2109'
subset= 'mr'
dataset_params = {"path": dataset_name, "language":subset, "use_auth_token":True}
dataset_shapes = None
TaskRegistry.add(
"oscar_marathi_corpus",
source=seqio.FunctionDataSource(
dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
splits=("train", "validation"),
caching_permitted=False,
num_input_examples=dataset_shapes,
),
preprocessors=[
functools.partial(
target_to_key, key_map={
"targets": None,
}, target_key="targets")],
output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},
metric_fns=[]
)
dataset = seqio.get_mixture_or_task("oscar_marathi_corpus").get_dataset(
sequence_length=None,
split="train",
shuffle=True,
num_epochs=1,
shard_info=seqio.ShardInfo(index=0, num_shards=10),
use_cached=False,
seed=42
)
for _, ex in zip(range(5), dataset):
print(ex['targets'].numpy().decode())
```
### Owner
_No response_
|
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[
"Thanks for reporting @dgcnz.\r\n\r\nI have checked that the dataset works fine in streaming mode.\r\n\r\nAdditionally, other datasets containing timestamps are properly rendered by the viewer: https://huggingface.co/datasets/blbooks\r\n\r\nI have tried to force the refresh of the preview, but the endpoint is not responsive: Connection timed out\r\n\r\nCC: @severo ",
"I've just resent the refresh of the preview to the new endpoint, without success.\r\n\r\nCC: @severo ",
"Fixed!\r\n\r\nhttps://huggingface.co/datasets/ett/viewer/h1/test\r\n\r\n<img width=\"982\" alt=\"Capture d’écran 2022-06-15 à 09 30 22\" src=\"https://user-images.githubusercontent.com/1676121/173769035-a075d753-ecfc-4a43-b54b-973105d464d3.png\">\r\n"
] | 2022-05-27T02:12:35 | 2022-06-15T07:30:46 | 2022-06-15T07:30:46 |
NONE
| null | null | null |
### Link
https://huggingface.co/datasets/ett
### Description
Timestamp is not JSON serializable.
```
Status code: 500
Exception: Status500Error
Message: Type is not JSON serializable: Timestamp
```
### Owner
No
|
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| 1,248,671,778 |
I_kwDODunzps5KbTgi
| 4,407 |
Dataset Viewer issue for conll2012_ontonotesv5
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[
"Thanks for reporting, @jiangwy99.\r\n\r\nI guess this could be addressed only once we fix our issue with irresponsive backend endpoint.\r\n\r\nCC: @severo ",
"I've just sent the forcing of the refresh of the preview to the new endpoint.",
"Fixed, thanks for the patience. The issue was the amount of RAM allowed to extract the first rows of the dataset was not sufficient."
] | 2022-05-25T20:18:33 | 2022-06-07T18:39:16 | 2022-06-07T18:39:16 |
NONE
| null | null | null |
### Link
https://huggingface.co/datasets/conll2012_ontonotesv5
### Description
Dataset viewer outage.
### Owner
No
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[TypeError: Couldn't cast array of type] Cannot process dataset in v2.2.2
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[
"And if the problem is that the way I am to construct the {Entity Type: list of spans} makes entity types without any spans hard to handle, is there a better way to meet the demand? Although I have verified that to make entity types without any spans to behave like `entity_chunk[label] = [[\"\"]]` can perform normally, I still wonder if there is a more elegant way?"
] | 2022-05-25T18:56:43 | 2022-06-07T14:27:20 | 2022-06-07T14:27:20 |
NONE
| null | null | null |
## Describe the bug
I am trying to process the [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5) dataset in `datasets` v2.2.2 and am running into a type error when casting the features.
## Steps to reproduce the bug
```python
import os
from typing import (
List,
Dict,
)
from collections import (
defaultdict,
)
from dataclasses import (
dataclass,
)
from datasets import (
load_dataset,
)
@dataclass
class ConllConverter:
path: str
name: str
cache_dir: str
def __post_init__(
self,
):
self.dataset = load_dataset(
path=self.path,
name=self.name,
cache_dir=self.cache_dir,
)
def convert(
self,
):
class_label = self.dataset["train"].features["sentences"][0]["named_entities"].feature
# label_set = list(set([
# label.split("-")[1] if label != "O" else label for label in class_label.names
# ]))
def prepare_chunk(token, entity):
assert len(token) == len(entity)
# Sequence length
length = len(token)
# Variable used
entity_chunk = defaultdict(list)
idx = flag = 0
# While loop
while idx < length:
if entity[idx] == "O":
flag += 1
idx += 1
else:
iob_tp, lab_tp = entity[idx].split("-")
assert iob_tp == "B"
idx += 1
while idx < length and entity[idx].startswith("I-"):
idx += 1
entity_chunk[lab_tp].append(token[flag: idx])
flag = idx
entity_chunk = dict(entity_chunk)
# for label in label_set:
# if label != "O" and label not in entity_chunk.keys():
# entity_chunk[label] = None
return entity_chunk
def prepare_features(
batch: Dict[str, List],
) -> Dict[str, List]:
sentence = [
sent for doc_sent in batch["sentences"] for sent in doc_sent
]
feature = {
"sentence": list(),
}
for sent in sentence:
token = sent["words"]
entity = class_label.int2str(sent["named_entities"])
entity_chunk = prepare_chunk(token, entity)
sent_feat = {
"token": token,
"entity": entity,
"entity_chunk": entity_chunk,
}
feature["sentence"].append(sent_feat)
return feature
column_names = self.dataset.column_names["train"]
dataset = self.dataset.map(
function=prepare_features,
with_indices=False,
batched=True,
batch_size=3,
remove_columns=column_names,
num_proc=1,
)
dataset.save_to_disk(
dataset_dict_path=os.path.join("data", self.path, self.name)
)
if __name__ == "__main__":
converter = ConllConverter(
path="conll2012_ontonotesv5",
name="english_v4",
cache_dir="cache",
)
converter.convert()
```
## Expected results
I want to use the dataset to perform NER task and to change the label list into a {Entity Type: list of spans} format.
## Actual results
<details>
<summary>Traceback</summary>
```python
Traceback (most recent call last): | 0/81 [00:00<?, ?ba/s]
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 532, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 499, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2751, in _map_single
writer.write_batch(batch)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1844, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>>
to
{'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)}
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 110, in <module>
converter.convert()
File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 91, in convert
dataset = self.dataset.map(
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 770, in map
{
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp>
k: dataset.map(
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2459, in map
transformed_shards[index] = async_result.get()
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 771, in get
raise self._value
TypeError: Couldn't cast array of type
struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>>
to
{'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)}
```
</details>
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: Ubuntu 18.04
- Python version: 3.9.7
- PyArrow version: 7.0.0
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| 1,248,572,899 |
I_kwDODunzps5Ka7Xj
| 4,404 |
Dataset should have a `.name` field
|
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[
"Hi! You can already use `dset.builder_name` and `dset.config_name` for that purpose. And when it comes to versioning, it's better to use `dset._fingerprint` than the `version` attribute as the former represents a deterministic hash that encodes all the mutable ops executed on a dataset, and the latter stays the same unless it's manually updated after each op.",
"@mariosasko Can we make ._fingerprint not private? seems a critical component for tracking how a model was generated to ensure reproducibility."
] | 2022-05-25T18:56:08 | 2022-09-13T15:09:30 | 2022-06-16T10:47:53 |
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
If building pipelines that can evaluate on more than one dataset, it would be nice to be able to log results of things like `Evaluating on {dataset.name}` or `results for {dataset.name} are: {results}`
Without some way of concisely identifying a dataset from the dataset object, tools which might run on more than one dataset must be passed the dataset object _and_ the name/id of the dataset being used.
**Describe the solution you'd like**
The DatasetInfo class should have a `name` field which is the name of a dataset. then for a given dataset if it evolves in time the `version` can be updated but its different versions of the same dataset with a unique `name`. The name could then all be accessed by `dataset.name`
**Describe alternatives you've considered**
For my own purposes I am considering making `NamedDataset[Dataset]` where the subclass just has a .name field.
**Additional context**
My guess is that most usecases are not working with more than one dataset in a given pipeline so a name is not really needed. This has surprised me though as one of the advantages of a standard dataset interface is to be able to build pipelines which can be passed in a dataset and separate responsibilities of the dataset loading from the train or eval pipeline.
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"NonMatchingChecksumError" when importing 'spider' dataset
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[
"Thanks for reporting, @OmarAlaaeldein.\r\n\r\nDatasets hosted at Google Drive give problems quite often due to a change in their service:\r\n- #3786 \r\n\r\nRelated to:\r\n- #3906\r\n\r\nI'm having a look.",
"We have made a Pull Request to replace the Google Drive URL. This fix will be accessible in our next `datasets` library release.\r\n\r\nIn the meantime, once the PR merged into master, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```"
] | 2022-05-25T07:45:07 | 2022-05-26T06:40:12 | 2022-05-26T06:40:12 |
NONE
| null | null | null |
## Describe the bug
When importing 'spider' dataset [https://huggingface.co/datasets/spider] an error occurs
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('spider')
```
## Expected results
Dataset object
## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']
## Environment info
- `datasets` version: 2.2.2
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.11
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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| 1,247,404,237 |
I_kwDODunzps5KWeDN
| 4,400 |
load dataset wikitext-2-raw-v1 failed. Could not reach wikitext-2-raw-v1.py.
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[
"I tried in this way.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(path=\"wikitext\", name=\"wikitext-103-v1\", split=\"train\")\r\n```"
] | 2022-05-25T03:10:44 | 2022-10-24T06:10:27 | 2022-05-25T03:26:36 |
NONE
| null | null | null |
## Describe the bug
Could not reach wikitext-2-raw-v1.py
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikitext-2-raw-v1")
```
## Expected results
Download `wikitext-2-raw-v1` dataset successfully.
## Actual results
```
File "load_datasets.py", line 13, in <module>
load_dataset("wikitext-2-raw-v1")
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1715, in load_dataset
**config_kwargs,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1536, in load_dataset_builder
data_files=data_files,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1282, in dataset_module_factory
raise e1 from None
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1224, in dataset_module_factory
dynamic_modules_path=dynamic_modules_path,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 559, in get_module
local_path = self.download_loading_script(revision)
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 539, in download_loading_script
return cached_path(file_path, download_config=download_config)
File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 246, in cached_path
download_desc=download_config.download_desc,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 582, in get_from_cache
raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.2.2/datasets/wikitext-2-raw-v1/wikitext-2-raw-v1.py (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Read timed out. (read timeout=100)",),))
```
I tried to download wikitext-2-raw-v1.py by chrome and got:
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: CentOS 7
- Python version: 3.6
- PyArrow version: 3.0.0
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I_kwDODunzps5KUuvL
| 4,399 |
LocalDatasetModuleFactoryWithoutScript extracts invalid builder name
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[
"Ok, so\r\n```\r\nos.path.basename(\"/home/user/\")\r\n```\r\ngives `''` while \r\n```\r\nos.path.basename(\"/home/user\")\r\n```\r\ngives `user`. \r\nThe code should check if the last char is a slash.\r\n",
"The fix is:\r\n```\r\n\"name\": os.path.basename(self.path[:-1] if self.path[-1] == \"/\" else self.path)\r\n```",
"I came through the same issue , just removing the last slash in the dataset path fixed it for me, may be this repo moderators could accept this as an accepted answer atleast if this could not be integrated\r\n\r\n> The fix is:\r\n> \r\n> ```\r\n> \"name\": os.path.basename(self.path[:-1] if self.path[-1] == \"/\" else self.path)\r\n> ```\r\n\r\n@apohllo consider making a pull request on this \r\n\r\nThanks for the amazing contributions from huggingface people !!\r\n",
"@apohllo Would you be interested in submitting a PR with the fix?",
"@mariosasko here we go:\r\n\r\nhttps://github.com/huggingface/datasets/pull/4967\r\n\r\nTBH I haven't tested it yet, but should work, since this is a basic change."
] | 2022-05-24T18:03:01 | 2022-09-12T15:30:43 | 2022-09-12T15:30:43 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
Trying to load a local dataset raises an error indicating that the config builder has to have a name.
No error should be reported, since the call is completly valid.
## Steps to reproduce the bug
```python
load_dataset("./data/some-dataset/", name="some-name")
```
## Expected results
The dataset should be loaded.
## Actual results
```
Traceback (most recent call last):
File "train_lquad.py", line 19, in <module>
load(tokenize_target_function, tokenize_target_function, {}, tokenizer)
File "train_lquad.py", line 14, in load
dataset = load_dataset("./data/lquad/", name="lquad")
File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/load.py", line 1708, in load_dataset
builder_instance = load_dataset_builder(
File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/load.py", line 1560, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/builder.py", line 269, in __init__
self.config, self.config_id = self._create_builder_config(
File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/builder.py", line 403, in _create_builder_config
raise ValueError(f"BuilderConfig must have a name, got {builder_config.name}")
ValueError: BuilderConfig must have a name, got
```
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.8.6
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
The error is probably in line 795 in load.py:
```
builder_kwargs = {
"hash": hash,
"data_files": data_files,
"name": os.path.basename(self.path),
"base_path": self.path,
**builder_kwargs,
}
```
`os.path.basename` for a directory returns an empty string, rather than the name of the directory.
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| 4,398 |
Calling `cast_column`/`remove_columns` and a sequence of `map` operations ends up making `faiss` fail with `ValueError`
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"It works if we either remove the `ds = ds.cast_column(\"id\", Value(\"int32\"))` line from the code above, or if instead calling `ds.remove_columns()` we remove the columns inside each mapping as `ds.map(..., remove_columns=[...])` instead of right after the mapping.\r\n\r\nBoth of those solutions seem to fix the issue, so the root cause of it may be around that. Sorry I cannot provide you more insights, in case I get to fix it I'll submit a PR, in the meanwhile the code that I'm using as a workaround is the following:\r\n\r\n```python\r\nfrom transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\nimport torch\r\n\r\ntorch.set_grad_enabled(False)\r\nctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\nctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n\r\nfrom datasets import load_dataset, Value\r\n\r\nds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\nds = ds.cast_column(\"id\", Value(\"int32\"))\r\nds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])}, remove_columns=[\"title\", \"summary\"])\r\n\r\ndef generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n\r\nds = ds.map(generate_embeddings, remove_columns=[\"inputs\"])\r\nds.add_faiss_index(column=\"embeddings\")\r\n```",
"FYI the main reason I want to use `dataset.remove_columns` rather than the function inside `dataset.map` is because according to the 🤗 Datasets documentation, it's faster.\r\n\r\n\"🤗 Datasets also has a [Dataset.remove_columns()](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.Dataset.remove_columns) method that is functionally identical, but faster, because it doesn’t copy the data of the remaining columns.\"\r\n\r\nMore information at https://huggingface.co/docs/datasets/process#map",
"Here I'm presenting all the scenarios so that you can further investigate the issue:\r\n\r\n- ✅ `cast_column` -> `map` with `remove_columns` -> `map` with `remove_columns` -> `add_faiss_index`\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])}, remove_columns=[\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings, remove_columns=[\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ❌ `cast_column` -> `map` -> `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])})\r\n ds = ds.remove_columns([\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns([\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ❌ `cast_column` -> `map` with `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])}, remove_columns=[\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns([\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ✅ `cast_column` -> `map` -> `remove_columns` -> `map` with `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])})\r\n ds = ds.remove_columns([\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings, remove_columns=[\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ✅ `map` -> `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])})\r\n ds = ds.remove_columns([\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns([\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```",
"So on, I've created #4411 so as to fix the bug with `remove_columns` under certain conditions before `add_faiss_index`, which means that the scenarios not working above are already working fine."
] | 2022-05-24T14:41:34 | 2022-06-14T16:01:56 | 2022-06-14T16:01:56 |
CONTRIBUTOR
| null | null | null |
First of all, sorry in advance for the unclear title, but this bug is weird to explain (at least for me), so I tried my best to summarize all the information in this issue.
## Describe the bug
Calling a certain combination of operations over a 🤗 `Dataset` and then trying to calculate the `faiss` index with `.add_faiss_index` ends up throwing an exception while trying to set the format back of a previously removed column. But this just happens over certain conditions... I'll present some scenarios below!
## Steps to reproduce the bug
Assuming the following dataset named `sample.csv` with some IMDb data:
```csv
id,title,summary
1877830,"The Batman","When a sadistic serial killer begins murdering key political figures in Gotham, Batman is forced to investigate the city's hidden corruption and question his family's involvement."
9419884,"Doctor Strange in the Multiverse of Madness","Doctor Strange teams up with a mysterious teenage girl from his dreams who can travel across multiverses, to battle multiple threats, including other-universe versions of himself, which threaten to wipe out millions across the multiverse. They seek help from Wanda the Scarlet Witch, Wong and others."
11138512,"The Northman","From visionary director Robert Eggers comes The Northman, an action-filled epic that follows a young Viking prince on his quest to avenge his father's murder."
1745960,"Top Gun: Maverick","After more than thirty years of service as one of the Navy's top aviators, Pete Mitchell is where he belongs, pushing the envelope as a courageous test pilot and dodging the advancement in rank that would ground him."
```
We'll be able to reproduce the bug using the following piece of code:
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset, Value
ds = load_dataset("csv", data_files=["sample.csv"], split="train")
ds = ds.cast_column("id", Value("int32")) # from `int64` to `int32`
ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])})
ds = ds.remove_columns(["title", "summary"])
def generate_embeddings(x):
return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}
ds = ds.map(generate_embeddings)
ds = ds.remove_columns("inputs")
ds.add_faiss_index(column="embeddings") # It fails here!
```
The code above is an adaptation of https://huggingface.co/docs/datasets/faiss_es, for the sake of presenting the bug with a simple example.
## Expected results
Ideally, the `faiss` index should be calculated over the 🤗 `Dataset` and no exception should be triggered.
## Actual results
But what happens instead is that a `ValueError: Columns ['inputs'] not in the dataset. Current columns in the dataset: ['id', 'embeddings']`, which makes no sense as that column has been previously dropped.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-1074-azure-x86_64-with-glibc2.31
- Python version: 3.9.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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| 4,394 |
trainer became extremely slow after reload dataset by `load_from_disk`
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[
"I tried to make the dataset much more smaller (100000 rows) , then the speed became `33.88it/s` from`8.62s/it`. It's nearly 200 times... Do you have any idea? Thank you!",
"Similar issue: https://github.com/huggingface/transformers/issues/8818\r\n\r\nI changed `RandomSampler` to `SequentialSampler` in the `trainer.py`, but the speed didn't become faster.",
"I changed\r\n```\r\ntokenized_datasets = load_from_disk(\r\n \"/pathto/dataset\"\r\n )\r\n```\r\nto\r\n```\r\ntokenized_datasets = load_from_disk(\r\n \"/pathto/dataset\", keep_in_memory=True\r\n )\r\n```\r\nand obtained normal speed. It's seems that the problem is on the os's speed limit.",
"Hi ! Currently `save_to_disk` saves one big Arrow file, which causes some slow downs. This has been discussed in #3735 and we'll implement sharding pretty soon to solve this\r\n\r\nFor now you can try splitting and saving your dataset in several Arrow files. Then you can load them one by one and use `concatenate_datasets` to have your big dataset again and hopefully with a better speed",
"Any update on fixing this? The issue still seems to be present."
] | 2022-05-23T14:04:37 | 2023-11-23T07:40:30 | null |
NONE
| null | null | null |
## Describe the bug
Due to memory problem, I need to save my tokenized datasets locally by CPU and reload it by multi GPU for running training script. However, after I reload it by `load_from_disk` and start training, the speed is extremely slow. It says I need about 1500 hours with 8 A100 cards. Before this, I can run the whole script in one day with a single A100 card.
Since I am try to pre-train a BERT, **my dataset is very large(29058165 rows)**
## Steps to reproduce the bug
```python
tokenized_datasets.save_to_disk(
"/pathto/dataset"
)
tokenized_datasets = load_from_disk(
"/pathto/dataset"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
eval_dataset=tokenized_datasets["validation"]
if training_args.do_eval
else None,
tokenizer=tokenizer,
data_collator=data_collator,
)
train_result = trainer.train(resume_from_checkpoint=checkpoint)
```
## Expected results
Without the save and reload process, I only need about one day to run the whole script with one A100 card.
## Actual results
```
[INFO|trainer.py:1290] 2022-05-23 22:49:46,266 >> ***** Running training *****
[INFO|trainer.py:1291] 2022-05-23 22:49:46,266 >> Num examples = 29058165
[INFO|trainer.py:1292] 2022-05-23 22:49:46,266 >> Num Epochs = 5
[INFO|trainer.py:1293] 2022-05-23 22:49:46,266 >> Instantaneous batch size per device = 16
[INFO|trainer.py:1294] 2022-05-23 22:49:46,266 >> Total train batch size (w. parallel, distributed & accumulation) = 256
[INFO|trainer.py:1295] 2022-05-23 22:49:46,266 >> Gradient Accumulation steps = 2
[INFO|trainer.py:1296] 2022-05-23 22:49:46,266 >> Total optimization steps = 567540
0%| | 1/567540 [00:09<1544:49:04, 9.80s/it]
0%| | 2/567540 [00:17<1320:00:17, 8.37s/it]
0%| | 3/567540 [00:26<1393:10:17, 8.84s/it]
0%| | 4/567540 [00:34<1344:56:33, 8.53s/it]
0%| | 5/567540 [00:43<1359:36:12, 8.62s/it]
```
## Environment info
```
torch 1.11.0+cu113
torchaudio 0.11.0+cu113
torchvision 0.12.0+cu113
transformers 4.18.0
datasets 2.2.2
```
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device/google/accessory/adk2012 - Git at Google
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[] | 2022-05-22T04:57:19 | 2022-05-23T06:36:27 | 2022-05-23T06:36:27 |
NONE
| null | null | null |
"git clone https://android.googlesource.com/device/google/accessory/adk2012"
https://android.googlesource.com/device/google/accessory/adk2012/#:~:text=git%20clone%20https%3A//android.googlesource.com/device/google/accessory/adk2012
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Bug for wiki_auto_asset_turk from GEM
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[
"Thanks for reporting, @StevenTang1998.\r\n\r\nI'm looking into it. ",
"Hi @StevenTang1998,\r\n\r\nWe have fixed the issue:\r\n- #4389\r\n\r\nThe fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by installing `datasets` from our GitHub repo:\r\n```\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```",
"Thanks for your reply!!\r\nAnd the totto dataset has the same problem. The url should be change to [https://storage.googleapis.com/totto-public/totto_data.zip](https://storage.googleapis.com/totto-public/totto_data.zip).",
"Hi again @StevenTang1998,\r\n\r\nI don't see any problem when loading `totto` dataset:\r\n```python\r\nIn [4]: import datasets\r\n ...: ds = datasets.load_dataset(\"totto\")\r\nDownloading builder script: 5.58kB [00:00, 5.33MB/s] \r\nDownloading metadata: 2.78kB [00:00, 2.96MB/s] \r\nUsing custom data configuration default\r\nDownloading and preparing dataset totto/default (download: 179.03 MiB, generated: 706.59 MiB, post-processed: Unknown size, total: 885.62 MiB) to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2...\r\nDownloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 188M/188M [00:32<00:00, 5.77MB/s]\r\nDataset totto downloaded and prepared to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 147.95it/s]\r\n\r\nIn [5]: ds\r\nOut[5]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],\r\n num_rows: 120761\r\n })\r\n validation: Dataset({\r\n features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],\r\n num_rows: 7700\r\n })\r\n test: Dataset({\r\n features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],\r\n num_rows: 7700\r\n })\r\n})\r\n```",
"Sorry, I didn't express it clearly. It's the totto dataset from gem.\r\ndatasets.load_dataset('gem', 'totto')\r\n",
"@StevenTang1998 fixed in:\r\n- #4396",
"Thanks!!"
] | 2022-05-21T12:31:30 | 2022-05-24T05:55:52 | 2022-05-23T10:29:55 |
NONE
| null | null | null |
## Describe the bug
The script of wiki_auto_asset_turk for GEM may be out of date.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('gem', 'wiki_auto_asset_turk')
```
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1731, in load_dataset
builder_instance.download_and_prepare(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 640, in download_and_prepare
self._download_and_prepare(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 1158, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 707, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/tangtianyi/.cache/huggingface/modules/datasets_modules/datasets/gem/982a54473b12c6a6e40d4356e025fb7172a5bb2065e655e2c1af51f2b3cf4ca1/gem.py", line 538, in _split_generators
dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 416, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 294, in download
downloaded_path_or_paths = map_nested(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 351, in map_nested
mapped = [
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 352, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 288, in _single_map_nested
return function(data_struct)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 320, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 234, in cached_path
output_path = get_from_cache(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 579, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/asset/raw/master/dataset/asset.test.orig
```
|
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L
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[] | 2022-05-21T03:47:58 | 2022-05-21T19:20:13 | 2022-05-21T19:20:13 |
NONE
| null | null | null |
## Describe the L
L
## Expected L
A clear and concise lmll
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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First time trying
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[] | 2022-05-21T02:15:18 | 2022-05-21T19:20:44 | 2022-05-21T19:20:44 |
NONE
| null | null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
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I_kwDODunzps5KHftP
| 4,381 |
Bug in caching 2 datasets both with the same builder class name
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[
"Hi @NouamaneTazi, thanks for reporting.\r\n\r\nPlease note that both datasets are cached in the same directory because their loading builder classes have the same name: `class MTOP(datasets.GeneratorBasedBuilder)`.\r\n\r\nYou should name their builder classes differently, e.g.:\r\n- `MtopDomain`\r\n- `MtopIntent`",
"Hi @NouamaneTazi, please note that after our fix:\r\n- #4388\r\n\r\nwe do not consider the class name anymore, but the name of the file where the loading builder class is implemented. "
] | 2022-05-20T18:18:03 | 2022-06-02T08:18:37 | 2022-05-25T05:16:15 |
MEMBER
| null | null | null |
## Describe the bug
The two datasets `mteb/mtop_intent` and `mteb/mtop_domain `use both the same cache folder `.cache/huggingface/datasets/mteb___mtop`. So if you first load `mteb/mtop_intent` then datasets will not load `mteb/mtop_domain`.
If you delete this cache folder and flip the order how you load the two datasets , you will get the opposite datasets loaded (difference is here in terms of the label and label_text).
## Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("mteb/mtop_intent", "en")
print(dataset['train'][0])
dataset = datasets.load_dataset("mteb/mtop_domain", "en")
print(dataset['train'][0])
```
## Expected results
```
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_intent/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 920.14it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_domain/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1307.59it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 0, 'label_text': 'messaging'}
```
## Actual results
```
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 920.14it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1307.59it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.1
- Platform: macOS-12.1-arm64-arm-64bit
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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[
"Fixed by:\r\n- #4380 ",
"Just an additional insight, the latest dill (either 0.3.5 or 0.3.5.1) also broke the hashing/fingerprinting of any mapping function.\r\n\r\nFor example:\r\n```\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"rotten_tomatoes\")\r\nd.map(lambda x: x)\r\n```\r\n\r\nReturns the standard non-dillable error:\r\n```\r\nParameter 'function'=<function <lambda> at 0x7fe7d18c9560> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly....\r\n```",
"@albertvillanova ExamplesTests.test_run_speech_recognition_seq2seq is in which file?",
"Thanks a lot @gugarosa for the insight: we will incorporate it in our CI as regression testing for future dill releases.",
"Hi @anivegesana, that test is in `transformers` library:\r\n- https://github.com/huggingface/transformers/blob/main/examples/pytorch/test_pytorch_examples.py#L449\r\n- https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py ",
"@albertvillanova\n\nI did a deep dive into @gugarosa's problem and found the issue and it might be related to the one @sgugger discovered. In dill 0.3.5(.1), I created a new `save_function` that fixes a bug in dill that prevented the pickling of recursive inner functions. It was a more complete solution to the problem that `dill._dill.stack` tried to solve in the internal API of dill. Since `dill._dill.stack` was no longer needed, I removed it. Since datasets copies the `save_function` directly from the dill API, it stops working with the new dill version since `dill._dill.stack` is no longer present and the `save_function` has been updated with new code.\r\n\r\nhttps://github.com/huggingface/datasets/blob/95193ae61e92aa537d0c65d37a1fd9d2393aae89/src/datasets/utils/py_utils.py#L607-L678\r\n\r\n~If the dill version is below 0.3.5, you should keep this function. If it is after, you would need to update your copy of `save_function` to use the code I introduced, or manually add a `stack` variable to `dill._dill` if it doesn't exist. Fortunately, in any version of Python 3.7+, dictionaries are always in insertion order and dill no longer supports Python 3.6 or older. So, any globals dictionary saved by dill 0.3.5+ will be deterministic given that the version of dill is held constant and this save_function is unnecessary for newer versions of dill.~\r\n\r\nAh. I see what is happening. I guess a different copy of the function code is needed that sorts the global variables by name.\r\n\r\n```py\r\nif dill.__version__.split('.') < ['0', '3', '5']:\r\n # current save_function code inside here\r\nelse:\r\n # new save_function code inside here with the following line inserted after creating the globals\r\n globs = {k: globs[k] for k in sorted(globs.keys())} \r\n```\r\n\r\nWill look into the test case @sgugger pointed out after that and verify if this is causing the problem.\r\n\r\nI am actually looking into rewriting the global variables code in uqfoundation/dill#466 and will keep this in mind and will try to create an easy way to modify the global variables in dill 0.3.6 (for example, sort them by key like datasets does).",
"Thanks a lot for your investigation @anivegesana.\r\n\r\nYes, we copied-pasted the old `save_function` function from `dill`, just adding a line to make deterministic the order of global variables `globs`. \r\n\r\nHowever, this function has changed a lot from version 0.3.5, after your PR (thank you for the fix in recursiveness, indeed):\r\n- uqfoundation/dill#443\r\n\r\nWe have to address this change.\r\n\r\nIf finally your PR to sort global variables is merged into dill 0.3.6, that will make our life easier, as the tweak will no longer be necessary. ;)\r\n\r\nI have included a regression test so that we are sure future releases of dill do not break `datasets`:\r\n- #4385 ",
"I should note that because Python 3.6 and older are now deprecated and Python 3.7 has insertion order dictionaries, the globals in dill will have a deterministic order, just not sorted. I would still keep it sorted like you have it to help with stability (for example, if someone reorders variables in a file, then sorting the globals would not invalidate the cache.)\n\nIt seems that the order is not quite deterministic in IPython. Huggingface datasets seems to do well in Jupyter regardless, so it is not a good idea to remove the sorting. uqfoundation/dill#19"
] | 2022-05-20T13:48:36 | 2022-05-21T15:53:26 | 2022-05-20T17:06:27 |
MEMBER
| null | null | null |
## Describe the bug
As reported by @sgugger, latest dill release is breaking things with Datasets.
```
______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________
self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None
def get(self, timeout=None):
self.wait(timeout)
if not self.ready():
raise TimeoutError
if self._success:
return self._value
else:
> raise self._value
E TypeError: '>' not supported between instances of 'NoneType' and 'float'
```
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| 4,376 |
irc_disentagle viewer error
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[
"DUPLICATED comment from https://github.com/huggingface/datasets/issues/3807:\r\n\r\nmy code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"irc_disentangle\", download_mode=\"force_redownload\")\r\n```\r\nhowever, it produces the same error\r\n```\r\n[38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0:\r\n [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master']\r\n```\r\nI attempted to use the `ignore_verifications' as such:\r\n\r\n```\r\nds = datasets.load_dataset('irc_disentangle', download_mode=\"force_redownload\", ignore_verifications=True)\r\n\r\nDownloading builder script: 12.0kB [00:00, 5.92MB/s] \r\nDownloading metadata: 7.58kB [00:00, 3.48MB/s] \r\nNo config specified, defaulting to: irc_disentangle/ubuntu\r\nDownloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5...\r\nDownloading data: 118MB [00:09, 12.1MB/s] \r\n \r\nDataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data.\r\n100%|██████████| 3/3 [00:00<00:00, 675.38it/s]\r\n```\r\nbut, this returns an empty set?\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\nnot sure what else to try at this point?\r\nThanks in advanced🤗",
"Thanks for reporting, @labouz. I'm addressing it. ",
"The issue with checksum and empty dataset has been fixed by:\r\n- #4377\r\n\r\nTo load the dataset, you should force the re-generation of the dataset from the downloaded file by passing `download_mode=\"reuse_cache_if_exists\"` to `load_dataset`.\r\n\r\nIn relation with the issue with the dataset viewer, first the dataset should be refactored to support streaming.",
"parfait!\r\nit works now, thank you 🙏 ",
"Hi there, \r\nI see this issue is closed, but I am wondering if there is any chance the source files have been moved since this fix? I am stumbling into the same NonMatchingChecksumError noted by lebouz's second post once 118MB of data has been downloaded, and have tried the solutions noted in the various fix checksum posts linked here and in other posts regarding passing in \"reuse_cache_if_exists\" to download_mode. Any suggestions? Thank you!\r\n\r\n"
] | 2022-05-19T19:15:16 | 2023-01-12T16:56:13 | 2022-06-02T08:20:00 |
NONE
| null | null | null |
the dataviewer shows this message for "ubuntu" - "train", "test", and "validation" splits:
```
Server error
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
```
it appears to give the same message for the "channel_two" data as well.
I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807
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I_kwDODunzps5KBUm3
| 4,374 |
extremely slow processing when using a custom dataset
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[
"Hi !\r\n\r\nMy guess is that some examples in your dataset are bigger than your RAM, and therefore loading them in RAM to pass them to `remove_non_indic_sentences` takes forever because it might use SWAP memory.\r\n\r\nMaybe several examples in your dataset are grouped together, can you check `len(lang_dataset[\"train\"])` and `lang_dataset[\"train\"].data.nbytes` of both datasets please ? It can also be helpful to check the distribution of lengths of each examples in your dataset.",
"Closing due to inactivity"
] | 2022-05-19T14:18:05 | 2023-07-25T15:07:17 | 2023-07-25T15:07:16 |
NONE
| null | null | null |
## processing a custom dataset loaded as .txt file is extremely slow, compared to a dataset of similar volume from the hub
I have a large .txt file of 22 GB which i load into HF dataset
`lang_dataset = datasets.load_dataset("text", data_files="hi.txt")`
further i use a pre-processing function to clean the dataset
`lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)`
the following processing takes astronomical time to process, while hoging all the ram.
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)`
the hours predicted to preprocess are as follows:
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
note: both the datasets are almost actually same, just provided by different sources with has +/- some samples, only one is hosted on the HF hub and the other is downloaded in a text format.
## Steps to reproduce the bug
```
import datasets
import psutil
import sys
import glob
from fastcore.utils import listify
import re
import gc
def remove_non_indic_sentences(example):
tmp_ls = []
eng_regex = r'[. a-zA-Z0-9ÖÄÅöäå _.,!"\'\/$]*'
for e in listify(example['text']):
matches = re.findall(eng_regex, e)
for match in (str(match).strip() for match in matches if match not in [""," ", " ", ",", " ,", ", ", " , "]):
if len(list(match.split(" "))) > 2:
e = re.sub(match," ",e,count=1)
tmp_ls.append(e)
gc.collect()
example['clean_text'] = tmp_ls
return example
lang_dataset = datasets.load_dataset("text", data_files="hi.txt")
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
## same thing work much faster when loading similar dataset from hub
lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", split="train", use_auth_token=True)
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
```
## Actual results
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)
**the hours predicted to preprocess are as follows:**
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
**i even tried the following:**
- sharding the large 22gb text files into smaller files and loading
- saving the file to disk and then loading
- using lesser num_proc
- using smaller batch size
- processing without batches ie : without `batched=True`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2.dev0
- Platform: Ubuntu 20.04 LTS
- Python version: 3.9.7
- PyArrow version:8.0.0
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TypeError: __init__() missing 1 required positional argument: 'scheme'
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"Duplicate of:\r\n- #3956\r\n\r\nI think you should report that issue to `elasticsearch` library: https://github.com/elastic/elasticsearch-py"
] | 2022-05-18T07:17:29 | 2022-05-18T16:36:22 | 2022-05-18T16:36:21 |
NONE
| null | null | null |
"name" : "node-1",
"cluster_name" : "elasticsearch",
"cluster_uuid" : "",
"version" : {
"number" : "7.5.0",
"build_flavor" : "default",
"build_type" : "tar",
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"build_date" : "2019-11-26T01:06:52.518245Z",
"build_snapshot" : false,
"lucene_version" : "8.3.0",
"minimum_wire_compatibility_version" : "6.8.0",
"minimum_index_compatibility_version" : "6.0.0-beta1"
when I run the order:
nohup python3 custom_service.pyc > service.log 2>&1&
the log:
nohup: 忽略输入
Traceback (most recent call last):
File "/home/xfz/p3_custom_test/custom_service.py", line 55, in <module>
File "/home/xfz/p3_custom_test/custom_service.py", line 48, in doInitialize
File "custom_impl.py", line 286, in custom_setup
File "custom_impl.py", line 127, in create_es_index
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/__init__.py", line 345, in __init__
ssl_show_warn=ssl_show_warn,
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 105, in client_node_configs
node_configs = hosts_to_node_configs(hosts)
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 154, in hosts_to_node_configs
node_configs.append(host_mapping_to_node_config(host))
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 221, in host_mapping_to_node_config
return NodeConfig(**options) # type: ignore
TypeError: __init__() missing 1 required positional argument: 'scheme'
[1]+ 退出 1 nohup python3 custom_service.pyc > service.log 2>&1
custom_service_pyc can't running
|
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The dataset preview is not available for this split.
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[
"Hi! A dataset has to be streamable to work with the viewer. I did a quick test, and yours is, so this might be a bug in the viewer. cc @severo \r\n",
"Looking at it. The message is now:\r\n\r\n```\r\nMessage: cannot cache function '__shear_dense': no locator available for file '/src/services/worker/.venv/lib/python3.9/site-packages/librosa/util/utils.py'\r\n```\r\n\r\nso possibly it's related to the libraries versions?\r\n",
"Maybe this SO thread can help: https://stackoverflow.com/questions/59290386/runtimeerror-at-cannot-cache-function-shear-dense-no-locator-available-fo",
"Same error for https://huggingface.co/datasets/LIUM/tedlium/viewer/release1/test. cc @sanchit-gandhi . I'm on it",
"Fixed in the datasets viewer, by setting the `NUMBA_CACHE_DIR` env var to a writable directory.",
"https://huggingface.co/datasets/Roh/ryanspeech/viewer/male/train\r\n\r\n<img width=\"1538\" alt=\"Capture d’écran 2022-06-08 à 11 30 08\" src=\"https://user-images.githubusercontent.com/1676121/172583285-4cd49a0f-5715-423b-95dd-5f6ace3b2416.png\">\r\n",
"https://huggingface.co/datasets/LIUM/tedlium/viewer/\r\n\r\n<img width=\"1538\" alt=\"Capture d’écran 2022-06-08 à 14 31 52\" src=\"https://user-images.githubusercontent.com/1676121/172616897-fbcb7df7-0308-4d09-a17d-48826bc91374.png\">\r\n"
] | 2022-05-17T16:34:43 | 2022-06-08T12:32:10 | 2022-06-08T09:26:56 |
NONE
| null | null | null |
I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it?
```
Status code: 400
Exception: AttributeError
Message: 'NoneType' object has no attribute 'split'
```
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`udhr` doesn't load, dataset checksum mismatch
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[] | 2022-05-17T13:47:09 | 2022-06-08T19:11:21 | 2022-06-08T19:11:21 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
Loading `udhr` fails due to a checksum mismatch for some source files. Looks like both of the source files on unicode.org have changed:
size + checksum in datasets repo:
```
(hfdev) leon@blade:~/datasets/datasets/udhr$ jq .default.download_checksums < dataset_infos.json
{
"https://unicode.org/udhr/assemblies/udhr_xml.zip": {
"num_bytes": 2273633,
"checksum": "0565fa62c2ff155b84123198bcc967edd8c5eb9679eadc01e6fb44a5cf730fee"
},
"https://unicode.org/udhr/assemblies/udhr_txt.zip": {
"num_bytes": 2107471,
"checksum": "087b474a070dd4096ae3028f9ee0b30dcdcb030cc85a1ca02e143be46327e5e5"
}
}
```
size + checksum regenerated from current source files:
```
(hfdev) leon@blade:~/datasets/datasets/udhr$ rm dataset_infos.json
(hfdev) leon@blade:~/datasets/datasets/udhr$ datasets-cli test --save_infos udhr.py
Using custom data configuration default
Testing builder 'default' (1/1)
Downloading and preparing dataset udhn/default (download: 4.18 MiB, generated: 6.15 MiB, post-processed: Unknown size, total: 10.33 MiB) to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66...
Dataset udhn downloaded and prepared to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 686.69it/s]
Dataset Infos file saved at dataset_infos.json
Test successful.
(hfdev) leon@blade:~/datasets/datasets/udhr$ jq .default.download_checksums < dataset_infos.json
{
"https://unicode.org/udhr/assemblies/udhr_xml.zip": {
"num_bytes": 2389690,
"checksum": "a3350912790196c6e1b26bfd1c8a50e8575f5cf185922ecd9bd15713d7d21438"
},
"https://unicode.org/udhr/assemblies/udhr_txt.zip": {
"num_bytes": 2215441,
"checksum": "cb87ecb25b56f34e4fd6f22b323000524fd9c06ae2a29f122b048789cf17e9fe"
}
}
(hfdev) leon@blade:~/datasets/datasets/udhr$
```
--- is unicode.org a sustainable hosting solution for this dataset?
## Steps to reproduce the bug
```python
from datasets import load_dataset
udhr = load_dataset("udhr")
```
## Expected results
That a Dataset object containing the UDHR data will be returned.
## Actual results
```
>>> d = load_dataset('udhr')
Using custom data configuration default
Downloading and preparing dataset udhn/default (download: 4.18 MiB, generated: 6.15 MiB, post-processed: Unknown size, total: 10.33 MiB) to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/leon/.local/lib/python3.9/site-packages/datasets/load.py", line 1731, in load_dataset
builder_instance.download_and_prepare(
File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 613, in download_and_prepare
self._download_and_prepare(
File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 1117, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 684, in _download_and_prepare
verify_checksums(
File "/home/leon/.local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://unicode.org/udhr/assemblies/udhr_xml.zip', 'https://unicode.org/udhr/assemblies/udhr_txt.zip']
>>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.1 commit/4110fb6034f79c5fb470cf1043ff52180e9c63b7
- Platform: Linux Ubuntu 20.04
- Python version: 3.9.12
- PyArrow version: 8.0.0
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Missing dataset tags and sections in some dataset cards
|
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[
"@lhoestq I can take this issue. Please can you point out to me where I can find the other positional arguments?",
"Hi @RohitRathore1 :)\r\n\r\nYou can find all the YAML tags in the tagging app here: https://hf.co/spaces/huggingface/datasets-tagging). They're all passed as arguments to a DatasetMetadata object used to validate the tags."
] | 2022-05-16T13:18:16 | 2022-05-30T15:36:52 | null |
NONE
| null | null | null |
Summary of CircleCI errors for different dataset metadata:
- **BoolQ**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **Conllpp**: expected some content in section `Citation Information` but it is empty.
- **GLUE**: 'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags
- **ConLL2003**: field 'task_ids': ['part-of-speech-tagging'] are not registered tags for 'task_ids'
- **Hate_speech18:** Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty
- **Jjigsaw_toxicity_pred**: `Citation Information` but it is empty.
- **LIAR** : `Data Instances`,`Data Fields`, `Data Splits`, `Citation Information` are empty.
- **MSRA NER** : Dataset Summary`, `Data Instances`, `Data Fields`, `Data Splits`, `Citation Information` are empty.
- **sem_eval_2010_task_8**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **sms_spam**: `Data Instances` and`Data Splits` are empty.
- **Quora** : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
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Problems with WMT dataset
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"Hi! Yes, the docs are outdated. Expect this to be fixed soon. \r\n\r\nIn the meantime, you can try to fix the issue yourself.\r\n\r\nThese are the configs/language pairs supported by `wmt15` from which you can choose:\r\n* `cs-en` (Czech - English)\r\n* `de-en` (German - English)\r\n* `fi-en` (Finnish- English)\r\n* `fr-en` (French - English)\r\n* `ru-en` (Russian - English)\r\n\r\nAnd the current implementation always uses all the subsets available for a language, so to define custom subsets, you'll have to clone the repo from the Hub and replace the line https://huggingface.co/datasets/wmt15/blob/main/wmt_utils.py#L688 with:\r\n`for split, ss_names in (self._subsets if self.config.subsets is None else self.config.subsets).items()`\r\n\r\nThen, you can load the dataset as follows:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset(\"path/to/local/wmt15_folder\", \"<one of 5 available configs>\", subsets=...)",
"@mariosasko thanks a lot for the suggested fix! ",
"Hi @mariosasko \r\n\r\nAre the docs updated? If not, I would like to get on it. I am new around here, would we helpful, if you can guide.\r\n\r\nThanks",
"Hi @khushmeeet! The docs haven't been updated, so feel free to work on this issue. This is a tricky issue, so I'll give the steps you can follow to fix this:\r\n\r\nFirst, this code:\r\nhttps://github.com/huggingface/datasets/blob/7cff5b9726a223509dbd6224de3f5f452c8d924f/src/datasets/load.py#L113-L118\r\n\r\nneeds to be replaced with (makes the dataset builder search more robust and allows us to remove the ABC stuff from `wmt_utils.py`):\r\n```python\r\n for name, obj in module.__dict__.items():\r\n if inspect.isclass(obj) and issubclass(obj, main_cls_type):\r\n if inspect.isabstract(obj):\r\n continue\r\n module_main_cls = obj\r\n obj_module = inspect.getmodule(obj)\r\n if obj_module is not None and module == obj_module:\r\n break\r\n```\r\n\r\nThen, all the `wmt_utils.py` scripts need to be updated as follows (these are the diffs with the requiered changes):\r\n````diff\r\n import os\r\n import re\r\n import xml.etree.cElementTree as ElementTree\r\n-from abc import ABC, abstractmethod\r\n\r\n import datasets\r\n````\r\n\r\n````diff\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\n _DESCRIPTION = \"\"\"\\\r\n-Translate dataset based on the data from statmt.org.\r\n+Translation dataset based on the data from statmt.org.\r\n\r\n-Versions exists for the different years using a combination of multiple data\r\n-sources. The base `wmt_translate` allows you to create your own config to choose\r\n-your own data/language pair by creating a custom `datasets.translate.wmt.WmtConfig`.\r\n+Versions exist for different years using a combination of data\r\n+sources. The base `wmt` allows you to create a custom dataset by choosing\r\n+your own data/language pair. This can be done as follows:\r\n\r\n ```\r\n-config = datasets.wmt.WmtConfig(\r\n- version=\"0.0.1\",\r\n+from datasets import inspect_dataset, load_dataset_builder\r\n+\r\n+inspect_dataset(\"<insert the dataset name\", \"path/to/scripts\")\r\n+builder = load_dataset_builder(\r\n+ \"path/to/scripts/wmt_utils.py\",\r\n language_pair=(\"fr\", \"de\"),\r\n subsets={\r\n datasets.Split.TRAIN: [\"commoncrawl_frde\"],\r\n datasets.Split.VALIDATION: [\"euelections_dev2019\"],\r\n },\r\n )\r\n-builder = datasets.builder(\"wmt_translate\", config=config)\r\n-```\r\n\r\n+# Standard version\r\n+builder.download_and_prepare()\r\n+ds = builder.as_dataset()\r\n+\r\n+# Streamable version\r\n+ds = builder.as_streaming_dataset()\r\n+```\r\n \"\"\"\r\n````\r\n\r\n````diff\r\n+class Wmt(datasets.GeneratorBasedBuilder):\r\n \"\"\"WMT translation dataset.\"\"\"\r\n+\r\n+ BUILDER_CONFIG_CLASS = WmtConfig\r\n\r\n def __init__(self, *args, **kwargs):\r\n- if type(self) == Wmt and \"config\" not in kwargs: # pylint: disable=unidiomatic-typecheck\r\n- raise ValueError(\r\n- \"The raw `wmt_translate` can only be instantiated with the config \"\r\n- \"kwargs. You may want to use one of the `wmtYY_translate` \"\r\n- \"implementation instead to get the WMT dataset for a specific year.\"\r\n- )\r\n super(Wmt, self).__init__(*args, **kwargs)\r\n\r\n @property\r\n- @abstractmethod\r\n def _subsets(self):\r\n \"\"\"Subsets that make up each split of the dataset.\"\"\"\r\n````\r\n```diff\r\n \"\"\"Subsets that make up each split of the dataset for the language pair.\"\"\"\r\n source, target = self.config.language_pair\r\n filtered_subsets = {}\r\n- for split, ss_names in self._subsets.items():\r\n+ subsets = self._subsets if self.config.subsets is None else self.config.subsets\r\n+ for split, ss_names in subsets.items():\r\n filtered_subsets[split] = []\r\n for ss_name in ss_names:\r\n dataset = DATASET_MAP[ss_name]\r\n```\r\n\r\n`wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t` have this script, so all of them need to be updated. Also, the dataset summaries from the READMEs of these datasets need to be updated to match the new `_DESCRIPTION` string. And that's it! Let me know if you need additional help.",
"Hi @mariosasko ,\r\n\r\nI have made the changes as suggested by you and have opened a PR #4537.\r\n\r\nThanks",
"Resolved via #4554 "
] | 2022-05-15T20:58:26 | 2022-07-11T14:54:02 | 2022-07-11T14:54:01 |
NONE
| null | null | null |
## Describe the bug
I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore.
## Steps to reproduce the bug
```shell
>>> import datasets
>>> a = datasets.translate.wmt.WmtConfig()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'datasets' has no attribute 'translate'
>>> a = datasets.wmt.WmtConfig()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'datasets' has no attribute 'wmt'
```
## Expected results
To load WMT15 with given data-sources.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
|
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| 1,236,086,170 |
I_kwDODunzps5JrS2a
| 4,352 |
When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way
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[
"Hi ! Thanks for reporting :) `datasets` usually returns a `pa.lib.ArrowInvalid` error if the feature types don't match.\r\n\r\nIt would be awesome if we had a way to reproduce the `OverflowError` in this case, to better understand what happened and be able to provide the best error message"
] | 2022-05-14T17:55:15 | 2022-05-16T15:09:17 | null |
NONE
| null | null | null |
## Describe the bug
Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not match the types that came back. Because of this, i ended up in tracebacks deep inside arrow_dataset.py and arrow_writer.py with exceptions that [did not make clear what the problem was](https://github.com/huggingface/datasets/issues/4349). In short i ended up with overflows and the OS killing processes when Arrow was attempting to write. It wasn't until I dug into `def write_batch` and the loop that loops over cols that I figured out what was going on.
It seems like `.map()` could set a boolean that it's checked that for at least 1 instance from the dataset, the returned data's types match the types provided by the `features` param and error out with a clear exception if they don't. This would make the cause of the issue much more understandable and save people time. This could be construed as a feature but it feels more like a bug to me.
## Steps to reproduce the bug
I don't have explicit code to repro the bug, but ill show an example
Code prior to the fix:
```python
def preprocess(examples):
# returns an encoded data dict with keys that match the features, but the types do not match
...
def get_encoded_data(data):
dataset = Dataset.from_pandas(data)
unique_labels = data['audit_type'].unique().tolist()
features = Features({
'image': Array3D(dtype="uint8", shape=(3, 224, 224))),
'input_ids': Sequence(feature=Value(dtype='int64'))),
'attention_mask': Sequence(Value(dtype='int64'))),
'token_type_ids': Sequence(Value(dtype='int64'))),
'bbox': Array2D(dtype="int64", shape=(512, 4))),
'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
})
encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names)
```
The Features set that fixed it:
```python
features = Features({
'image': Sequence(Array3D(dtype="uint8", shape=(3, 224, 224))),
'input_ids': Sequence(Sequence(feature=Value(dtype='int64'))),
'attention_mask': Sequence(Sequence(Value(dtype='int64'))),
'token_type_ids': Sequence(Sequence(Value(dtype='int64'))),
'bbox': Sequence(Array2D(dtype="int64", shape=(512, 4))),
'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
})
```
The difference between my original code (which was based on documentation) and the working code is the addition of the `Sequence(...)` to 4/5 features as I am working with paginated data and the doc examples are not.
## Expected results
Dataset.map() attempts to validate the data types for each Feature on the first iteration and errors out if they are not validated.
## Actual results
Specify the actual results or traceback.
Based on the value of `writer_batch_size`, execution errors out when Arrow attempts to write because the types do not match, though its error messages dont make this obvious
Example errors:
```
OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB.
(offset overflow while concatenating arrays)
```
```
zsh: killed python doc_classification.py
UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
datasets version: 2.1.0
Platform: macOS-12.2.1-arm64-arm-64bit
Python version: 3.9.12
PyArrow version: 6.0.1
Pandas version: 1.4.2
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I_kwDODunzps5JqxqB
| 4,351 |
Add optional progress bar for .save_to_disk(..) and .load_from_disk(..) when working with remote filesystems
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[
"Hi! I like this idea. For consistency with `load_dataset`, we can use `fsspec`'s `TqdmCallback` in `.load_from_disk` to monitor the number of bytes downloaded, and in `.save_to_disk`, we can track the number of saved shards for consistency with `push_to_hub` (after we implement https://github.com/huggingface/datasets/issues/4196)."
] | 2022-05-14T11:30:42 | 2022-12-14T18:22:59 | 2022-12-14T18:22:59 |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
When working with large datasets stored on remote filesystems(such as s3), the process of uploading a dataset could take really long time. For instance: I was uploading a re-processed version of wmt17 en-ru to my s3 bucket and it took like 35 minutes(and that's given that I have a fiber optic connection). The only output during that process was a progress bar for flattening indices and then ~35 minutes of complete silence.
**Describe the solution you'd like**
I want to be able to enable a progress bar when calling .save_to_disk(..) and .load_from_disk(..), it would track either amount of bytes sent/received or amount of records written/loaded, and will give some ETA. Basically just tqdm.
**Describe alternatives you've considered**
- Save dataset to tmp folder at the disk and then upload it using custom wrapper over botocore, which will work with progress bar, like [this](https://alexwlchan.net/2021/04/s3-progress-bars/).
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Dataset.map()'s fails at any value of parameter writer_batch_size
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[
"Note that this same issue occurs even if i preprocess with the more default way of tokenizing that uses LayoutLMv2Processor's internal OCR:\r\n\r\n```python\r\n feature_extractor = LayoutLMv2FeatureExtractor()\r\n tokenizer = LayoutLMv2Tokenizer.from_pretrained(\"microsoft/layoutlmv2-base-uncased\")\r\n processor = LayoutLMv2Processor(feature_extractor, tokenizer)\r\n encoded_inputs = processor(images, padding=\"max_length\", truncation=True)\r\n encoded_inputs[\"image\"] = np.array(encoded_inputs[\"image\"])\r\n encoded_inputs[\"label\"] = examples['label_id']\r\n```",
"Wanted to make sure anyone that finds this also finds my other report: https://github.com/huggingface/datasets/issues/4352",
"Did you close it because you found that it was due to the incorrect Feature types ?",
"Yeah-- my analysis of the issue was wrong in this one so I just closed it while linking to the new issue",
"I met with the same problem when doing some experiments about layoutlm. I tried to set the writer_batch_size to 1, and the error still exists. Is there any solutions to this problem?",
"The problem lies in how your Features are defined. It's erroring out when it actually goes to write them to disk"
] | 2022-05-13T16:55:12 | 2022-06-02T12:51:11 | 2022-05-14T15:08:08 |
NONE
| null | null | null |
## Describe the bug
If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance.
Context:
I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug.
I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages.
Code I am using is provided below
## Steps to reproduce the bug
I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents.
```python
def get_encoded_data(data):
dataset = Dataset.from_pandas(data)
unique_labels = data['label'].unique()
features = Features({
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'input_ids': Sequence(feature=Value(dtype='int64')),
'attention_mask': Sequence(Value(dtype='int64')),
'token_type_ids': Sequence(Value(dtype='int64')),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
})
encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1)
encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME)
encoded_dataset.set_format(type="torch")
return encoded_dataset
```
```python
PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False)
def preprocess_data(examples):
directory = os.path.join(FILES_PATH, examples['file_location'])
images_dir = os.path.join(directory, PDF_IMAGE_DIR)
textract_response_path = os.path.join(directory, 'textract.json')
doc_meta_path = os.path.join(directory, 'doc_meta.json')
textract_document = get_textract_document(textract_response_path, doc_meta_path)
images, words, bboxes = get_doc_training_data(images_dir, textract_document)
encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True)
# https://github.com/NielsRogge/Transformers-Tutorials/issues/36
encoded_inputs["image"] = np.array(encoded_inputs["image"])
encoded_inputs["label"] = examples['label_id']
return encoded_inputs
```
## Expected results
My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly.
## Actual results
If writer_batch_size is set to a value less than the number of rows, I get either:
```
OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB.
(offset overflow while concatenating arrays)
```
or simply
```
zsh: killed python doc_classification.py
UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
```
If it is greater than the number of rows, i get the `zsh: killed` error above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.12
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
|
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| 4,348 |
`inspect` functions can't fetch dataset script from the Hub
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[
"Hi, thanks for reporting! `git bisect` points to #2986 as the PR that introduced the bug. Since then, there have been some additional changes to the loading logic, and in the current state, `force_local_path` (set via `local_path`) forbids pulling a script from the internet instead of downloading it: https://github.com/huggingface/datasets/blob/cfae0545b2ba05452e16136cacc7d370b4b186a1/src/datasets/inspect.py#L89-L91\r\n\r\ncc @lhoestq: `force_local_path` is only used in `inspect_dataset` and `inspect_metric`. Is it OK if we revert the behavior to match the old one?",
"Good catch ! Yea I think it's fine :)"
] | 2022-05-13T16:08:26 | 2022-06-09T10:26:06 | 2022-06-09T10:26:06 |
MEMBER
| null | null | null |
The `inspect_dataset` and `inspect_metric` functions are unable to retrieve a dataset or metric script from the Hub and store it locally at the specified `local_path`:
```py
>>> from datasets import inspect_dataset
>>> inspect_dataset('rotten_tomatoes', local_path='path/to/my/local/folder')
FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory.
```
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I_kwDODunzps5JnaC2
| 4,346 |
GH Action to build documentation never ends
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[] | 2022-05-13T10:44:44 | 2022-05-13T11:22:00 | 2022-05-13T11:22:00 |
MEMBER
| null | null | null |
## Describe the bug
See: https://github.com/huggingface/datasets/runs/6418035586?check_suite_focus=true
I finally forced the cancel of the workflow.
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Metrics documentation is not accessible in the datasets doc UI
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"Hey @fxmarty :) Yes we are working on showing the docs of all the metrics on the Hugging face website. If you want to follow the advancements you can check the [evaluate](https://github.com/huggingface/evaluate) repository cc @lvwerra @sashavor "
] | 2022-05-13T07:46:30 | 2022-06-03T08:50:25 | 2022-06-03T08:50:25 |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
Search for a metric name like "seqeval" yields no results on https://huggingface.co/docs/datasets/master/en/index . One needs to go look in `datasets/metrics/README.md` to find the doc. Even in the `README.md`, it can be hard to understand what the metric expects as an input, for example for `squad` there is a [key `id`](https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L42) documented only in the function doc but not in the `README.md`, and one needs to go look into the code to understand what the metric expects.
**Describe the solution you'd like**
Have the documentation for metrics appear as well in the doc UI, e.g. this https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L21-L63
I know there are plans to migrate metrics to the evaluate library, but just pointing this out.
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Failing CI on Windows for sari and wiki_split metrics
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[] | 2022-05-13T04:55:17 | 2022-05-13T05:47:41 | 2022-05-13T05:47:41 |
MEMBER
| null | null | null |
## Describe the bug
Our CI is failing from yesterday on Windows for metrics: sari and wiki_split
```
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_sari - ...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_wiki_split
```
See: https://app.circleci.com/pipelines/github/huggingface/datasets/11928/workflows/79daa5e7-65c9-4e85-829b-00d2bfbd076a/jobs/71594
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`wikipedia` pre-processed datasets
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"Hi @vpj, thanks for reporting.\r\n\r\nI'm sorry, but I can't reproduce your bug: I load \"20220301.simple\"in 9 seconds:\r\n```shell\r\ntime python -c \"from datasets import load_dataset; load_dataset('wikipedia', '20220301.simple')\"\r\n\r\nDownloading and preparing dataset wikipedia/20220301.simple (download: 228.58 MiB, generated: 224.18 MiB, post-processed: Unknown size, total: 452.76 MiB) to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.66k/1.66k [00:00<00:00, 1.02MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 235M/235M [00:02<00:00, 82.8MB/s]\r\nDataset wikipedia downloaded and prepared to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 290.75it/s]\r\n\r\nreal\t0m9.693s\r\nuser\t0m6.002s\r\nsys\t0m3.260s\r\n```\r\n\r\nCould you please check your environment info, as requested when opening this issue?\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```\r\nMaybe you are using an old version of `datasets`...",
"Downloading and processing `wikipedia simple` dataset completed in under 11sec on M1 Mac. Could you please check `dataset` version as mentioned by @albertvillanova? Also check system specs, if system is under load processing could take some time I guess."
] | 2022-05-12T11:25:42 | 2022-08-31T08:26:57 | 2022-08-31T08:26:57 |
NONE
| null | null | null |
## Describe the bug
[Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", "20220301.en")
```
## Expected results
To load the dataset
## Actual results
Takes a very long time to load (after downloading)
After `Downloading data files: 100%`. It takes hours and gets killed.
Tried `wikipedia.simple` and it got processed after ~30mins.
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Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance
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[
"Not sure if it's related... I was going to raise an issue for https://huggingface.co/datasets/domenicrosati/TruthfulQA which also has the same issue... https://huggingface.co/datasets/domenicrosati/TruthfulQA/viewer/domenicrosati--TruthfulQA/train \r\n\r\n",
"Yes, it's related. The backend behind the dataset viewer is currently under too much load, and these datasets are still in the jobs queue. We're actively working on this issue, and we expect to fix the issue permanently soon. Thanks for your patience 🙏 ",
"Thanks @severo and no worries! - a suggestion for a UI usability thing maybe is to indicate that the dataset processing is in the job queue (rather than no data?)",
"Thanks, these are working great now (including @domenicrosati 's, afaics!)"
] | 2022-05-12T10:59:08 | 2022-05-13T10:57:15 | 2022-05-13T10:57:02 |
CONTRIBUTOR
| null | null | null |
### Link
https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train
### Description
The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time.
* https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train
* https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train
While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped!
### Owner
Yes
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| 4,324 |
Support >1 PWC dataset per dataset card
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"Hi @leondz, I agree it would be nice. We'll see what we can do ;)"
] | 2022-05-12T10:29:07 | 2022-05-13T11:25:29 | null |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
Some datasets cover more than one dataset on PapersWithCode. For example, the OffensEval 2020 challenge involved five languages, and there's one dataset to cover all five datasets, [`strombergnlp/offenseval_2020`](https://huggingface.co/datasets/strombergnlp/offenseval_2020). However, the yaml `paperswithcode_id:` dataset card entry only supports one value; when multiple are added, the PWC link disappears from the dataset page.
Because the link from a PapersWithCode dataset to a Hugging Face Hub entry can't be entered manually and seems to be scraped, this means end users don't have a way of getting a dataset reader link to appear on all the PWC datasets supported by one HF Hub Dataset reader.
It's not super unusual to have papers introduce multiple parallel variants of a dataset and would be handy to reflect this, so e.g. dataset maintainers can DRY, and so dataset users can keep what they're doing simple.
**Describe the solution you'd like**
I'd like `paperswithcode_id:` to support lists and be able to connect with multiple PWC datasets.
**Describe alternatives you've considered**
De-normalising the datasets on HF Hub to create multiple readers for each variation on a task, i.e. instead of a single `offenseval_2020`, having `offenseval_2020_ar`, `offenseval_2020_da`, `offenseval_2020_gr`, ...
**Additional context**
Hope that's enough
**Priority**
Low
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Audio can not find value["bytes"]
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[
"\r\n\r\nthat is reason my bytes`s empty\r\nbut i have some confused why path prior is higher than bytes?\r\n\r\nif you can make bytes in _generate_examples , you don`t have to make bytes to path?\r\nbecause we have path and bytes already",
"> but i have some confused why path prior is higher than bytes?\r\n\r\nIf the audio file is already available locally, we don't need to store the bytes again.\r\n\r\nIf you don't specify a \"path\" to a local file, then the bytes are stored. You can set \"path\" to None for example.\r\n\r\n> if you can make bytes in _generate_examples , you don`t have to make bytes to path?\r\n> because we have path and bytes already\r\n\r\nIt's useful to pass both \"path\" and \"bytes\" in `_generate_examples`:\r\n- when the dataset has been downloaded, then the \"path\" to the audio files are stored and we can ignore \"bytes\" in order to save disk space.\r\n- when the dataset is loaded in streaming mode, the audio files are not available on your disk and therefore we use the \"bytes\" ",
"@lhoestq \r\nFirst of all, thx for reply\r\n\r\nbut, if i put in \"bytes\" and \"path\"\r\nex) {\"bytes\":\"blah blah~\", \"path\":\"blah blah~\"}\r\n\r\nthat source working that my bytes to empty first,\r\nand then, re-calculate my bytes!\r\n\r\n\r\nif you have some pcm file, pcm is can read bytes.\r\nso, i put in bytes and paths.\r\nbut bytes is been None why encode_example func make None\r\nand then, on decode_example func, we no have bytes. so, calculate bytes to path.\r\npcm is not support librosa or soundfile, error occured!\r\n\r\nthe most important thing is not announced anywhere this situation can be reproduced\r\n\r\nis that truly right process flow?",
"I don't think we support PCM files, feel free to convert your data to WAV for now.\r\n\r\nIt would be awesome to support PCM files though, let me know if you'd like to contribute this feature, I'd be happy to help",
"@lhoestq oh, how can i contribute?",
"You can clone the repository (see the guide on [how to contribute](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request)) and see how we can make the `Image.encode_example` method work with PCM data.\r\n\r\nThere might be other ways to approach this problem, but here is what I think is a reasonable one:\r\n\r\nI think `Image.encode_example` should be able to take PCM bytes as input and the sampling rate, and return the WAV bytes (built by combining the PCM bytes and the sampling rate info), so that `Image.decode_example` can read it.\r\n\r\nTo check if the input bytes are PCM data, you can just check if the extension of the `path` is \".pcm\".\r\n",
"maybe i can start to contribute on this sunday!\r\n@lhoestq ",
"@lhoestq plz check my pr #4409 \r\n\r\nam i wrong somting?",
"Thanks, I reviewed your PR :)"
] | 2022-05-12T08:31:58 | 2022-07-07T13:16:08 | 2022-07-07T13:16:08 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
I wrote down _generate_examples like:
but where is the bytes?
## Expected results
value["bytes"] is not None, so i can make datasets with bytes, not path
## bytes looks like:
blah blah~~
\xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03
blah blah~~
that function not return None
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:2.2.1
- Platform:ubuntu 18.04
- Python version:3.6.9
- PyArrow version:6.0.1
|
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| 1,233,208,864 |
I_kwDODunzps5JgUYg
| 4,320 |
Multi-news dataset loader attempts to strip wrong character from beginning of summaries
|
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[] | null |
[
"Hi ! Thanks for reporting :)\r\n\r\nThis dataset was simply converted from [tensorflow datasets](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/summarization/multi_news.py)\r\n\r\nI think we can just remove the `.strip(\"- \")` and keep this character",
"Cool! I made a PR."
] | 2022-05-11T21:36:41 | 2022-05-16T13:52:10 | 2022-05-16T13:52:10 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
The `multi_news.py` data loader has [a line which attempts to strip `"- "` from the beginning of summaries](https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/datasets/multi_news/multi_news.py#L97). The actual character in the multi-news dataset, however, is `"– "`, which is different, e.g. `"– " != "- "`.
I would have just opened a PR to fix the mistake, but I am wondering what the motivation for stripping this character is? AFAICT most approaches just leave it in, e.g. the current SOTA on this dataset, [PRIMERA](https://huggingface.co/allenai/PRIMERA-multinews) (you can see its in the generated summaries of the model in their [example notebook](https://github.com/allenai/PRIMER/blob/main/Evaluation_Example.ipynb)).
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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I_kwDODunzps5JZHMH
| 4,310 |
Loading dataset with streaming: '_io.BufferedReader' object has no attribute 'loc'
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[] | 2022-05-10T15:12:53 | 2022-05-11T16:46:31 | 2022-05-11T16:46:31 |
NONE
| null | null | null |
## Describe the bug
Loading a datasets with `load_dataset` and `streaming=True` returns `AttributeError: '_io.BufferedReader' object has no attribute 'loc'`. Notice that loading with `streaming=False` works fine.
In the following steps we load parquet files but the same happens with pickle files. The problem seems to come from `fsspec` lib, I put in the environment info also `s3fs` and `fsspec` versions since I'm loading from an s3 bucket.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# path is the path to parquet files
data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
dataset = load_dataset("parquet", data_files=data_files, streaming=True)
```
## Expected results
A dataset object `datasets.dataset_dict.DatasetDict`
## Actual results
```
AttributeError Traceback (most recent call last)
<command-562086> in <module>
11
12 data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
---> 13 dataset = load_dataset("parquet", data_files=data_files, streaming=True)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1679 if streaming:
1680 extend_dataset_builder_for_streaming(builder_instance, use_auth_token=use_auth_token)
-> 1681 return builder_instance.as_streaming_dataset(
1682 split=split,
1683 use_auth_token=use_auth_token,
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/builder.py in as_streaming_dataset(self, split, base_path, use_auth_token)
904 )
905 self._check_manual_download(dl_manager)
--> 906 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
907 # By default, return all splits
908 if split is None:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py in _split_generators(self, dl_manager)
30 if not self.config.data_files:
31 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
---> 32 data_files = dl_manager.download_and_extract(self.config.data_files)
33 if isinstance(data_files, (str, list, tuple)):
34 files = data_files
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in download_and_extract(self, url_or_urls)
798
799 def download_and_extract(self, url_or_urls):
--> 800 return self.extract(self.download(url_or_urls))
801
802 def iter_archive(self, urlpath_or_buf: Union[str, io.BufferedReader]) -> Iterable[Tuple]:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in extract(self, path_or_paths)
776
777 def extract(self, path_or_paths):
--> 778 urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)
779 return urlpaths
780
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
312 num_proc = 1
313 if num_proc <= 1 or len(iterable) <= num_proc:
--> 314 mapped = [
315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
313 if num_proc <= 1 or len(iterable) <= num_proc:
314 mapped = [
--> 315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
317 ]
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
249 # Singleton first to spare some computation
250 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 251 return function(data_struct)
252
253 # Reduce logging to keep things readable in multiprocessing with tqdm
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _extract(self, urlpath)
781 def _extract(self, urlpath: str) -> str:
782 urlpath = str(urlpath)
--> 783 protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
784 if protocol is None:
785 # no extraction
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol(urlpath, use_auth_token)
371 urlpath, kwargs = urlpath, {}
372 with fsspec.open(urlpath, **kwargs) as f:
--> 373 return _get_extraction_protocol_with_magic_number(f)
374
375
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol_with_magic_number(f)
335 def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
336 """read the magic number from a file-like object and return the compression protocol"""
--> 337 prev_loc = f.loc
338 magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
339 f.seek(prev_loc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/fsspec/implementations/local.py in __getattr__(self, item)
337
338 def __getattr__(self, item):
--> 339 return getattr(self.f, item)
340
341 def __enter__(self):
AttributeError: '_io.BufferedReader' object has no attribute 'loc'
```
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-1071-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
- `fsspec` version: 2021.08.1
- `s3fs` version: 2021.08.1
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| 1,231,137,204 |
I_kwDODunzps5JYam0
| 4,306 |
`load_dataset` does not work with certain filename.
|
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[
"Never mind. It is because of the caching of datasets..."
] | 2022-05-10T13:14:04 | 2022-05-10T18:58:36 | 2022-05-10T18:58:09 |
NONE
| null | null | null |
## Describe the bug
This is a weird bug that took me some time to find out.
I have a JSON dataset that I want to load with `load_dataset` like this:
```
data_files = dict(train="train.json.zip", val="val.json.zip")
dataset = load_dataset("json", data_files=data_files, field="data")
```
## Expected results
No error.
## Actual results
The val file is loaded as expected, but the train file throws JSON decoding error:
```
╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮
│ <ipython-input-74-97947e92c100>:5 in <module> │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/load.py:1687 in │
│ load_dataset │
│ │
│ 1684 │ try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES │
│ 1685 │ │
│ 1686 │ # Download and prepare data │
│ ❱ 1687 │ builder_instance.download_and_prepare( │
│ 1688 │ │ download_config=download_config, │
│ 1689 │ │ download_mode=download_mode, │
│ 1690 │ │ ignore_verifications=ignore_verifications, │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:605 in │
│ download_and_prepare │
│ │
│ 602 │ │ │ │ │ │ except ConnectionError: │
│ 603 │ │ │ │ │ │ │ logger.warning("HF google storage unreachable. Downloa │
│ 604 │ │ │ │ │ if not downloaded_from_gcs: │
│ ❱ 605 │ │ │ │ │ │ self._download_and_prepare( │
│ 606 │ │ │ │ │ │ │ dl_manager=dl_manager, verify_infos=verify_infos, **do │
│ 607 │ │ │ │ │ │ ) │
│ 608 │ │ │ │ │ # Sync info │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:694 in │
│ _download_and_prepare │
│ │
│ 691 │ │ │ │
│ 692 │ │ │ try: │
│ 693 │ │ │ │ # Prepare split will record examples associated to the split │
│ ❱ 694 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │
│ 695 │ │ │ except OSError as e: │
│ 696 │ │ │ │ raise OSError( │
│ 697 │ │ │ │ │ "Cannot find data file. " │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:1151 in │
│ _prepare_split │
│ │
│ 1148 │ │ │
│ 1149 │ │ generator = self._generate_tables(**split_generator.gen_kwargs) │
│ 1150 │ │ with ArrowWriter(features=self.info.features, path=fpath) as writer: │
│ ❱ 1151 │ │ │ for key, table in logging.tqdm( │
│ 1152 │ │ │ │ generator, unit=" tables", leave=False, disable=True # not loggin │
│ 1153 │ │ │ ): │
│ 1154 │ │ │ │ writer.write_table(table) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/notebook.py:257 in │
│ __iter__ │
│ │
│ 254 │ │
│ 255 │ def __iter__(self): │
│ 256 │ │ try: │
│ ❱ 257 │ │ │ for obj in super(tqdm_notebook, self).__iter__(): │
│ 258 │ │ │ │ # return super(tqdm...) will not catch exception │
│ 259 │ │ │ │ yield obj │
│ 260 │ │ # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/std.py:1183 in │
│ __iter__ │
│ │
│ 1180 │ │ # If the bar is disabled, then just walk the iterable │
│ 1181 │ │ # (note: keep this check outside the loop for performance) │
│ 1182 │ │ if self.disable: │
│ ❱ 1183 │ │ │ for obj in iterable: │
│ 1184 │ │ │ │ yield obj │
│ 1185 │ │ │ return │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/packaged_modules/j │
│ son/json.py:90 in _generate_tables │
│ │
│ 87 │ │ │ # If the file is one json object and if we need to look at the list of │
│ 88 │ │ │ if self.config.field is not None: │
│ 89 │ │ │ │ with open(file, encoding="utf-8") as f: │
│ ❱ 90 │ │ │ │ │ dataset = json.load(f) │
│ 91 │ │ │ │ │
│ 92 │ │ │ │ # We keep only the field we are interested in │
│ 93 │ │ │ │ dataset = dataset[self.config.field] │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:293 in load │
│ │
│ 290 │ To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` │
│ 291 │ kwarg; otherwise ``JSONDecoder`` is used. │
│ 292 │ """ │
│ ❱ 293 │ return loads(fp.read(), │
│ 294 │ │ cls=cls, object_hook=object_hook, │
│ 295 │ │ parse_float=parse_float, parse_int=parse_int, │
│ 296 │ │ parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:357 in loads │
│ │
│ 354 │ if (cls is None and object_hook is None and │
│ 355 │ │ │ parse_int is None and parse_float is None and │
│ 356 │ │ │ parse_constant is None and object_pairs_hook is None and not kw): │
│ ❱ 357 │ │ return _default_decoder.decode(s) │
│ 358 │ if cls is None: │
│ 359 │ │ cls = JSONDecoder │
│ 360 │ if object_hook is not None: │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:337 in decode │
│ │
│ 334 │ │ containing a JSON document). │
│ 335 │ │ │
│ 336 │ │ """ │
│ ❱ 337 │ │ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) │
│ 338 │ │ end = _w(s, end).end() │
│ 339 │ │ if end != len(s): │
│ 340 │ │ │ raise JSONDecodeError("Extra data", s, end) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:353 in raw_decode │
│ │
│ 350 │ │ │
│ 351 │ │ """ │
│ 352 │ │ try: │
│ ❱ 353 │ │ │ obj, end = self.scan_once(s, idx) │
│ 354 │ │ except StopIteration as err: │
│ 355 │ │ │ raise JSONDecodeError("Expecting value", s, err.value) from None │
│ 356 │ │ return obj, end │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
JSONDecodeError: Unterminated string starting at: line 85 column 20 (char 60051)
```
However, when I rename the `train.json.zip` to other names (like `training.json.zip`, or even to `train.json`), everything works fine; when I unzip the file to `train.json`, it works as well.
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-4.4.0-131-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
```
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| 4,304 |
Language code search does direct matches
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[
"Thanks for reporting ! I forwarded the issue to the front-end team :)\r\n\r\nWill keep you posted !\r\n\r\nI also changed the tagging app to suggest two letters code for now."
] | 2022-05-10T11:59:16 | 2022-05-10T12:38:42 | null |
CONTRIBUTOR
| null | null | null |
## Describe the bug
Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search.
## Steps to reproduce the bug
1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL))
2. Look for datasets using the full code
3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq))
Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`.
One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :)
## Expected results
Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`).
## Actual results
The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches.
## Environment info
(web app)
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Normalise license names
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[
"we'll add the same server-side metadata validation system as for hf.co/models soon-ish\r\n\r\n(you can check on hf.co/models that licenses are \"clean\")",
"Fixed by #4367."
] | 2022-05-09T13:51:32 | 2022-05-20T09:51:50 | 2022-05-20T09:51:50 |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata.
**Describe the solution you'd like**
I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) .
**Describe alternatives you've considered**
None
**Additional context**
None
**Priority**
Low
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Datasets YAML tagging space is down
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[
"@lhoestq @albertvillanova `update-task-list` branch does not exist anymore, should point to `main` now i guess",
"Thanks for reporting, fixing it now",
"It's up again :)"
] | 2022-05-09T13:45:05 | 2022-05-09T14:44:25 | 2022-05-09T14:44:25 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
The neat hf spaces app for generating YAML tags for dataset `README.md`s is down
## Steps to reproduce the bug
1. Visit https://huggingface.co/spaces/huggingface/datasets-tagging
## Expected results
There'll be a HF spaces web app for generating dataset metadata YAML
## Actual results
There's an error message; here's the step where it breaks:
```
Step 18/29 : RUN pip install -r requirements.txt
---> Running in e88bfe7e7e0c
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/huggingface/datasets.git@update-task-list (from -r requirements.txt (line 4))
Cloning https://github.com/huggingface/datasets.git (to revision update-task-list) to /tmp/pip-req-build-bm8t0r0k
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets.git /tmp/pip-req-build-bm8t0r0k
WARNING: Did not find branch or tag 'update-task-list', assuming revision or ref.
Running command git checkout -q update-task-list
error: pathspec 'update-task-list' did not match any file(s) known to git
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
```
## Environment info
- Platform: Linux / Brave
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Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
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[
"Hi @leondz, thanks for reporting.\r\n\r\nIndeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions.\r\n\r\nIn particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`.\r\n\r\nLet me know if you need some help: I could push a commit to your repo with the fix.",
"Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)"
] | 2022-05-06T12:03:27 | 2022-05-09T08:25:58 | 2022-05-09T08:25:58 |
CONTRIBUTOR
| null | null | null |
### Link
https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train
### Description
The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss?
### Owner
Yes
|
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"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
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[
"So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above.\r\n\r\nAs it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered.\r\n\r\nSo on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU.\r\n\r\nI'll add the code in a PR linked to this issue in case you want to merge it!",
"Adding here the complete error traceback!\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/alvarobartt/lol.py\", line 12, in <module>\r\n ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3656, in add_faiss_index\r\n super().add_faiss_index(\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 478, in add_faiss_index\r\n faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 281, in add_vectors\r\n self.faiss_index = self._faiss_index_to_device(index, self.device)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 327, in _faiss_index_to_device\r\n faiss_res = faiss.StandardGpuResources()\r\nNameError: name 'faiss' is not defined\r\n```",
"Closed as https://github.com/huggingface/datasets/pull/4288 already merged! :hugs:"
] | 2022-05-05T15:09:45 | 2022-05-10T13:53:19 | 2022-05-10T13:53:19 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception.
All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset
ds = load_dataset('crime_and_punish', split='train[:100]')
ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()})
ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`
```
## Expected results
A new column named `embeddings` in the dataset that we're adding the index to.
## Actual results
An exception is triggered with the following message `NameError: name 'faiss' is not defined`.
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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| 4,284 |
Issues in processing very large datasets
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[
"Hi ! `datasets` doesn't load the dataset in memory. Instead it uses memory mapping to load your dataset from your disk (it is stored as arrow files). Do you know at what point you have RAM issues exactly ?\r\n\r\nHow big are your graph_data_train dictionaries btw ?",
"Closing this issue due to inactivity."
] | 2022-05-05T05:01:09 | 2023-07-25T15:12:38 | 2023-07-25T15:12:38 |
NONE
| null | null | null |
## Describe the bug
I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory.
Here are my modifications to `run_summarization.py` code.
```
# loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph
graph_data_train = get_graph_data('train')
graph_data_validation = get_graph_data('val')
...
...
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function_train,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
```
And here is the modified preprocessed function:
```
def preprocess_function_train(examples):
inputs, targets, sub_graphs, ids = [], [], [], []
for i in range(len(examples[text_column])):
if examples[text_column][i] is not None and examples[summary_column][i] is not None:
# if examples['doc_id'][i] in graph_data.keys():
inputs.append(examples[text_column][i])
targets.append(examples[summary_column][i])
sub_graphs.append(graph_data_train[examples['id'][i]])
ids.append(examples['id'][i])
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,
sub_graphs=sub_graphs, ids=ids)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
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OpenBookQA has missing and inconsistent field names
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[
"Thanks for reporting, @vblagoje.\r\n\r\nIndeed, I noticed some of these issues while reviewing this PR:\r\n- #4259 \r\n\r\nThis is in my TODO list. ",
"Ok, awesome @albertvillanova How about #4275 ?",
"On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency.\r\n\r\nFor example, other datasets also flatten \"question.stem\" into \"question\":\r\n- ai2_arc:\r\n ```python\r\n question = data[\"question\"][\"stem\"]\r\n choices = data[\"question\"][\"choices\"]\r\n text_choices = [choice[\"text\"] for choice in choices]\r\n label_choices = [choice[\"label\"] for choice in choices]\r\n yield id_, {\r\n \"id\": id_,\r\n \"answerKey\": answerkey,\r\n \"question\": question,\r\n \"choices\": {\"text\": text_choices, \"label\": label_choices},\r\n }\r\n ```\r\n- commonsense_qa:\r\n ```python\r\n question = data[\"question\"]\r\n stem = question[\"stem\"]\r\n yield id_, {\r\n \"answerKey\": answerkey,\r\n \"question\": stem,\r\n \"choices\": {\"label\": labels, \"text\": texts},\r\n }\r\n ```\r\n- cos_e:\r\n ```python\r\n \"question\": cqa[\"question\"][\"stem\"],\r\n ```\r\n- qasc\r\n- quartz\r\n- wiqa\r\n\r\nExceptions:\r\n- exams\r\n\r\nI think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.",
"@albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq ",
"I'm opening a PR that adds the missing fields.\r\n\r\nLet's agree on the feature structure: @lhoestq @mariosasko @polinaeterna ",
"IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).",
"I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new. ",
"I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets.\r\n\r\nThere is always the tension between:\r\n- preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset),\r\n- and on the other hand performing some kind of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain \"standard\" structure).\r\n - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model\r\n\r\nRecently, in the BigScience BioMedical hackathon, they adopted a different approach:\r\n- they implement a \"source\" config, respecting the original structure as much as possible\r\n- they implement additional config for each task, with a \"standard\" nested structure per task, which is most useful for users.",
"@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best. ",
"Datasets are not tracked in this repository anymore. I think we must move this thread to the [discussions tab of the dataset](https://huggingface.co/datasets/openbookqa/discussions)",
"Indeed @osbm thanks. I'm closing this issue if it's fine for you all then"
] | 2022-05-04T05:51:52 | 2022-10-11T17:11:53 | 2022-10-05T13:50:03 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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| 4,275 |
CommonSenseQA has missing and inconsistent field names
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[
"Thanks for reporting, @vblagoje.\r\n\r\nI'm opening a PR to address this. "
] | 2022-05-04T05:38:59 | 2022-05-04T11:41:18 | null |
CONTRIBUTOR
| null | null | null |
## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.
More precisely, we need to:
1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original
## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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A typo in docs of datasets.disable_progress_bar
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[
"Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)"
] | 2022-05-03T17:44:56 | 2022-05-04T06:58:35 | 2022-05-04T06:58:35 |
NONE
| null | null | null |
## Describe the bug
in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable".
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| 4,268 |
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
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[
"It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for [\"word\"](https://en.wiktionary.org/wiki/word),\r\n\r\nPronunciation\r\n([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/\r\n([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/",
"Hi @i-am-neo, thanks for reporting.\r\n\r\nNormally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public.\r\n\r\nAlso note that last commit \"Add metadata\" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well.\r\n\r\n@i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.",
"Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!",
"All datasets are private now. \r\n\r\nRe:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)",
"Thanks a lot, @cakiki.\r\n\r\n@i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets. ",
"Thanks for letting me know, @albertvillanova @cakiki.\r\nAny chance of having a subset alpha version in the meantime? \r\nI only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes.\r\n\r\nWould like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. \r\n\r\n(Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!",
"Hey @i-am-neo,\r\n\r\nCool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)",
"@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/)\r\nYou're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file",
"thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!",
"thanks @patrickvonplaten. will do - getting my observations together."
] | 2022-05-02T20:34:25 | 2022-05-06T15:53:30 | 2022-05-03T11:23:48 |
NONE
| null | null | null |
## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
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[
"Hi @xflashxx, thanks for reporting.\r\n\r\nPlease note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis\r\n\r\nWe are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.",
"i'd suggest just pinging the authors here in the issue if possible?",
"Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.",
"Hi @xflashxx and @albertvillanova,\r\n\r\nI have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to \"new\" items. The length of `ids_validation` and `ids_testing` is zero.\r\n\r\nRegarding impact on scores:\r\n1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. \r\n\r\n2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository.\r\n\r\nHope this helps!",
"Thanks @shahbazsyed for your fast fix.\r\n\r\nAs a side note:\r\n- Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de\r\n- Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings."
] | 2022-04-30T17:43:37 | 2022-05-03T06:04:26 | 2022-05-03T06:04:26 |
NONE
| null | null | null |
## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0
|
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| 1,218,460,444 |
I_kwDODunzps5IoDsc
| 4,248 |
conll2003 dataset loads original data.
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[
"Thanks for reporting @sue99.\r\n\r\nUnfortunately. I'm not able to reproduce your problem:\r\n```python\r\nIn [1]: import datasets\r\n ...: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"conll2003\")\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 14042\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3251\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3454\r\n })\r\n})\r\n\r\nIn [3]: dataset[\"train\"][0]\r\nOut[3]: \r\n{'id': '0',\r\n 'tokens': ['EU',\r\n 'rejects',\r\n 'German',\r\n 'call',\r\n 'to',\r\n 'boycott',\r\n 'British',\r\n 'lamb',\r\n '.'],\r\n 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}\r\n```\r\n\r\nJust guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub. "
] | 2022-04-28T09:33:31 | 2022-07-18T07:15:48 | 2022-07-18T07:15:48 |
NONE
| null | null | null |
## Describe the bug
I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
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I_kwDODunzps5Inhny
| 4,247 |
The data preview of XGLUE
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[
"\r\n",
"Thanks for reporting @czq1999.\r\n\r\nNote that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.\r\n\r\nThat is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming. ",
"Fixed, thanks @albertvillanova !\r\n\r\nhttps://huggingface.co/datasets/xglue\r\n\r\n<img width=\"824\" alt=\"Capture d’écran 2022-04-29 à 10 23 14\" src=\"https://user-images.githubusercontent.com/1676121/165909391-9f98d98a-665a-4e57-822d-8baa2dc9b7c9.png\">\r\n"
] | 2022-04-28T07:30:50 | 2022-04-29T08:23:28 | 2022-04-28T16:08:03 |
NONE
| null | null | null |
It seems that something wrong with the data previvew of XGLUE
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I_kwDODunzps5IkGlG
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NonMatchingChecksumError when attempting to download GLUE
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[
"Hi :)\r\n\r\nI think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with:\r\n\r\n```py\r\npip install -U datasets\r\n```\r\n\r\nThen you can download:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"glue\", \"rte\")\r\n```",
"This appears to work. Thank you!\n\nOn Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote:\n\n> Hi :)\n>\n> I think your issue may be related to the older nlp library. I was able to\n> download glue with the latest version of datasets. Can you try updating\n> with:\n>\n> pip install -U datasets\n>\n> Then you can download:\n>\n> from datasets import load_datasetds = load_dataset(\"glue\", \"rte\")\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n"
] | 2022-04-27T14:14:21 | 2022-04-28T07:45:27 | 2022-04-28T07:45:27 |
NONE
| null | null | null |
## Describe the bug
I am trying to download the GLUE dataset from the NLP module but get an error (see below).
## Steps to reproduce the bug
```python
import nlp
nlp.__version__ # '0.2.0'
nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
```
## Expected results
I expect the dataset to download without an error.
## Actual results
```
INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports.
INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue
INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py
INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json
INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb
INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805
Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0...
Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s]
INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-7-669a8343dcc1> in <module>
----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
~/anaconda3/envs/py36_bert_ee_torch1_11/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)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
~/anaconda3/envs/py36_bert_ee_torch1_11/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)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
458 # Checksums verification
459 if verify_infos:
--> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
461 for split_generator in split_generators:
462 if str(split_generator.split_info.name).lower() == "all":
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa
- Python version: 3.6.13
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
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| 4,238 |
Dataset caching policy
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[
"Hi @loretoparisi, thanks for reporting.\r\n\r\nThere is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode=\"force_redownload\")`.\r\n\r\nPlease, let me know if this fixes your problem.\r\n\r\nI can confirm you that your dataset loads without any problem for me:\r\n```python\r\nIn [2]: ds = load_dataset(\"loretoparisi/tatoeba-sentences\", data_files={\"train\": \"train.csv\", \"test\": \"test.csv\"}, delimiter=\"\\t\", column_names=['label', 'text'])\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['label', 'text'],\r\n num_rows: 8256449\r\n })\r\n test: Dataset({\r\n features: ['label', 'text'],\r\n num_rows: 2061204\r\n })\r\n})\r\n``` ",
"@albertvillanova thank you, it seems it still does not work using:\r\n\r\n```python\r\nsentences = load_dataset(\r\n \"loretoparisi/tatoeba-sentences\",\r\n data_files=data_files,\r\n delimiter='\\t', \r\n column_names=['label', 'text'],\r\n download_mode=\"force_redownload\"\r\n)\r\n```\r\n[This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook!\r\n\r\nThe problem is that the download file's revision for `test.csv` is not correctly parsed\r\n\r\n\r\n\r\nIf you download that file `test.csv` from the repo, the line `\\\\N` is not there anymore (it was there at the first file upload).\r\n\r\nMy impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\\\N`) since are binary files:\r\n\r\n```\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow\r\n!head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json\r\n```\r\n",
"SOLVED! The problem was the with the file itself, using caching parameter helped indeed.\r\nThanks for helping!"
] | 2022-04-27T10:42:11 | 2022-04-27T16:29:25 | 2022-04-27T16:28:50 |
NONE
| null | null | null |
## Describe the bug
I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error
```
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally:
```python
from datasets import load_dataset_builder
dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences")
print(dataset_builder.cache_dir)
print(dataset_builder.info.features)
print(dataset_builder.info.splits)
```
```
Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd
/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519
None
None
```
and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`.
Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it?
Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last.
Thank you.
## Steps to reproduce the bug
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset(
"loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
)
# You can make this part faster with num_proc=<some int>
sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
sentences = sentences.shuffle()
```
## Expected results
Properly tokenize dataset file `test.csv` without issues.
## Actual results
Specify the actual results or traceback.
```
Downloading data files: 100%
2/2 [00:16<00:00, 7.34s/it]
Downloading data: 100%
391M/391M [00:12<00:00, 36.6MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 40.0MB/s]
Extracting data files: 100%
2/2 [00:00<00:00, 47.66it/s]
Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.
100%
2/2 [00:00<00:00, 25.94it/s]
11%
942339/8256449 [01:55<13:11, 9245.85ex/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>()
12 )
13 # You can make this part faster with num_proc=<some int>
---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
15 sentences = sentences.shuffle()
10 frames
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
- ```
```
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.11.0+cu113 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
- ```
|
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| 4,237 |
Common Voice 8 doesn't show datasets viewer
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"Thanks for reporting. I understand it's an error in the dataset script. To reproduce:\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> split_names = ds.get_dataset_split_names(\"mozilla-foundation/common_voice_8_0\", use_auth_token=\"**********\")\r\nDownloading builder script: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10.9k/10.9k [00:00<00:00, 10.9MB/s]\r\nDownloading extra modules: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 3.36MB/s]\r\nDownloading extra modules: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 53.1k/53.1k [00:00<00:00, 650kB/s]\r\nNo config specified, defaulting to: common_voice/en\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 280, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py\", line 153, in _split_generators\r\n self._log_download(self.config.name, bundle_version, hf_auth_token)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py\", line 139, in _log_download\r\n email = HfApi().whoami(auth_token)[\"email\"]\r\nKeyError: 'email'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 323, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 285, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```",
"Thanks for reporting @patrickvonplaten and thanks for the investigation @severo.\r\n\r\nUnfortunately I'm not able to reproduce the error.\r\n\r\nI think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139\r\n```python\r\nfrom huggingface_hub import HfApi, HfFolder\r\n\r\nif isinstance(auth_token, bool):\r\n email = HfApi().whoami(auth_token)\r\nemail = HfApi().whoami(auth_token)[\"email\"]\r\n```\r\n\r\nCould you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?",
"OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email!\r\n\r\n```python\r\n>>> from huggingface_hub import HfApi, HfFolder\r\n>>> auth_token = \"hf_app_******\"\r\n>>> t = HfApi().whoami(auth_token)\r\n>>> t\r\n{'type': 'app', 'name': 'dataset-preview-backend'}\r\n>>> t[\"email\"]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nKeyError: 'email'\r\n```\r\n\r\nNote also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key.\r\n\r\n@SBrandeis @julien-c: do you think the app token should have an email associated, like the users?",
"We can workaround this with\r\n```python\r\nemail = HfApi().whoami(auth_token).get(\"email\", \"[email protected]\")\r\n```\r\nin the common voice scripts",
"Hmmm, does this mean that any person who downloads the common voice dataset will be logged as \"[email protected]\"? If so, it would defeat the purpose of sending the user's email to the commonvoice API, right?",
"I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy.\r\n\r\nAdditionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nCC: @patrickvonplaten @lhoestq @SBrandeis @julien-c ",
"Hmm I don't agree here. \r\n\r\nAnybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it's trivial to tweak the code to not send the \"correct\" email but to just whatever and it would work.\r\n\r\nNote that someone only has visibility on the code after having \"signed\" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. \r\n\r\nI'm fine with both @lhoestq's solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?",
"> Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nYes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism.\r\n\r\nCurrently the script as it is already requires the user being logged in:\r\n```python\r\nHfApi().whoami(auth_token)\r\n```\r\nthrows an exception if None/invalid auth_token is passed.\r\n\r\nOn the other hand, we should agree on the way to allow the viewer to stream the data.",
"The preview is back now, thanks !"
] | 2022-04-27T10:05:20 | 2022-05-10T12:17:05 | 2022-05-10T12:17:04 |
CONTRIBUTOR
| null | null | null |
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
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| 4,235 |
How to load VERY LARGE dataset?
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"The `Trainer` support `IterableDataset`, not just datasets."
] | 2022-04-27T07:50:13 | 2023-07-25T15:07:57 | 2023-07-25T15:07:57 |
NONE
| null | null | null |
### System Info
```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency.
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```
### Who can help?
Trainer: @sgugger
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```
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Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?
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[
"Thanks for reporting @beyondguo.\r\n\r\nIndeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip\r\nAnd that URL only contains the English version.",
"The German data requires payment\r\n\r\nThe [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states \"The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00).\"\r\n\r\nInflation since 2003 has also affected LDC's prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly.\r\n\r\nThe ACL hold copyright of this data; I'll mail them and anyone I can find at ECI to see if they'll open this up now. After all, it worked with Microsoft 3DMM, why not here too, after 28 years? :)\r\n",
"Closing this issue as we are not allowed to share publicly the German subset."
] | 2022-04-27T00:53:52 | 2023-07-25T15:10:15 | 2023-07-25T15:10:15 |
NONE
| null | null | null |
But on huggingface datasets:
Where is the German data?
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Dictionary Feature
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"Hi @jordiae,\r\n\r\nInstead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n```python\r\n\"list_of_dict_feature\": [\r\n {\r\n \"key1_in_dict\": datasets.Value(\"string\"),\r\n \"key2_in_dict\": datasets.Value(\"int32\"),\r\n ...\r\n }\r\n],\r\n```\r\n\r\nFeel free to re-open this issue if that does not work for your use case.",
"> Hi @jordiae,\r\n> \r\n> Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n> \r\n> ```python\r\n> \"list_of_dict_feature\": [\r\n> {\r\n> \"key1_in_dict\": datasets.Value(\"string\"),\r\n> \"key2_in_dict\": datasets.Value(\"int32\"),\r\n> ...\r\n> }\r\n> ],\r\n> ```\r\n> \r\n> Feel free to re-open this issue if that does not work for your use case.\r\n\r\nThank you"
] | 2022-04-26T12:50:18 | 2022-04-29T14:52:19 | 2022-04-28T17:04:58 |
NONE
| null | null | null |
Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something?
Thank you in advance.
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Big_Patent dataset broken
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[
"Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive.\r\n\r\nSee related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com\r\n\r\nTo quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551):\r\n\r\n> PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.\r\n\r\n",
"We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.",
"The data owners have agreed on hosting their data on the Hub."
] | 2022-04-25T15:31:45 | 2022-05-26T06:29:43 | 2022-05-02T18:21:15 |
NONE
| null | null | null |
## Dataset viewer issue for '*big_patent*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)*
*Unable to view because it says FileNotFound, also cannot download it through the python API*
Am I the one who added this dataset ? No
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DatasetDict containing Datasets with different features when pushed to hub gets remapped features
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[
"Hi @pietrolesci, thanks for reporting.\r\n\r\nPlease note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**.\r\n\r\nTo handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**.\r\n\r\nHowever, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.",
"Hi @albertvillanova,\r\n\r\nThanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation.\r\n\r\nAgain, thanks for your time @albertvillanova!\r\n\r\nBest,\r\nPietro",
"Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. \r\n\r\n@albertvillanova @lhoestq wdyt?",
"I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features.\r\n\r\nNote that later you will be able to push datasets with different features as different dataset **configurations** (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). We will work on this soon",
"Hi @lhoestq,\r\n\r\nReturning to this thread to ask whether the possibility to create `DatasetDict` with different configurations will be supported in the future.\r\n\r\nBest,\r\nPietro",
"DatasetDict is likely to always require the datasets to have the same columns and types, while different configurations may have different columns and types.\r\n\r\nWhy would you like to see that ?\r\nIf it's related to push_to_hub, we plan to allow pushing several configs, but not using DatasetDict",
"Hi @lhoestq and @pietrolesci,\r\n\r\nI have been curious about this question as well. I don't have experience working with different configurations, but I can give a bit more detail on the work flow that I have been using with `Dataset_dict`.\r\n\r\nAs @pietrolesci mentions, I have been using `push_to_hub` to quickly iterate on dataset curation for different ML experiments - locally I create a set of dataset splits e.g. `train/val/test/inference`, then convert them to `HF_Datasets` and finally a to `Dataset_Dict` to `push_to_hub`. Where I have run into issues is when I want to include different metadata for different splits. For example, I have situations where I only have meta-data for one of the splits (e.g. test) or situations where I am working with `inference` data that does not have labels. Currently I use a rather hacky work around by adding \"dummy\" columns for missing columns to avoid the error:\r\n\r\n```\r\nValueError: All datasets in `DatasetDict` should have the same features\r\n```\r\n\r\nI am curious why `DatasetDict` will likely not support this functionality? I don't know much about working with different configurations, but allowing for different columns between datasets / splits would be a very helpful use-case for me. Are there any docs for using different configuration OR a more info about incorporating it with `push_to_hub`.\r\n\r\nBest wishes,\r\nJonathan\r\n\r\n",
"+1",
"> I am curious why DatasetDict will likely not support this functionality?\r\n\r\nThere's a possibility we may merge the Dataset and DatasetDict classes. The DatasetDict purpose was to define a way to get the train/test splits of a dataset.\r\n\r\nsee the discussions at https://github.com/huggingface/datasets/issues/5189\r\n\r\n> Are there any docs for using different configuration OR a more info about incorporating it with push_to_hub.\r\n\r\nThere's a PR open to allow to upload a dataset with a certain configuration name. Then later you can reload this specific configuration using `load_dataset(ds_name, config_name)`\r\n\r\nsee the PR at https://github.com/huggingface/datasets/pull/5213",
"Hi, regarding the following information:\r\n\r\n> Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**.\r\n> \r\n> To handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**.\r\n\r\nAltough this is often implied (such as how else would `DatasetDict` be able to process multiple splits in the same way?), I would expect it to be written somewhere in the docs plainly and maybe even in bold. Also I would expect to see it in multiple places such as:\r\n\r\n- in docstring of `DatasetDict`\r\n- in nlp/image/audio guides on how to create a dataset\r\n- [in conceptual guide on how to create a loading script](https://huggingface.co/docs/datasets/main/en/about_dataset_load)\r\n\r\n\r\nI think this addition would benefit the docs, especially when you guide a newbie (such as me) through the process of creating a dataset. As I said, you somehow suspect that this is in fact the case, but without reading it in the docs you cannot be sure."
] | 2022-04-25T11:22:54 | 2023-04-06T19:25:50 | 2022-05-20T15:15:30 |
NONE
| null | null | null |
Hi there,
I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same.
Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli).
In short:
I have 3 feature mapping
```python
Tri_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
Ent_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]),
}
)
Con_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]),
}
)
```
Then I create different datasets
```python
dataset_splits = {}
for split in df["split"].unique():
print(split)
df_split = df.loc[df["split"] == split].copy()
if split in Tri_dataset:
df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
ds = Dataset.from_pandas(df_split, features=Tri_features)
elif split in Ent_bin_dataset:
df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1})
ds = Dataset.from_pandas(df_split, features=Ent_features)
elif split in Con_bin_dataset:
df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1})
ds = Dataset.from_pandas(df_split, features=Con_features)
else:
print("ERROR:", split)
dataset_splits[split] = ds
datasets = DatasetDict(dataset_splits)
```
I then push to hub
```python
datasets.push_to_hub("pietrolesci/robust_nli", token="<token>")
```
Finally, I load it from the hub
```python
datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli")
```
And I get that
```python
datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features
```
since
```python
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"])
```
gets remapped to
```python
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"])
```
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"Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows:\r\n```python\r\nfrom datasets import load_dataset,Features,Value,ClassLabel\r\nclass_names = [\"cmn\",\"deu\",\"rus\",\"fra\",\"eng\",\"jpn\",\"spa\",\"ita\",\"kor\",\"vie\",\"nld\",\"epo\",\"por\",\"tur\",\"heb\",\"hun\",\"ell\",\"ind\",\"ara\",\"arz\",\"fin\",\"bul\",\"yue\",\"swe\",\"ukr\",\"bel\",\"que\",\"ces\",\"swh\",\"nno\",\"wuu\",\"nob\",\"zsm\",\"est\",\"kat\",\"pol\",\"lat\",\"urd\",\"sqi\",\"isl\",\"fry\",\"afr\",\"ron\",\"fao\",\"san\",\"bre\",\"tat\",\"yid\",\"uig\",\"uzb\",\"srp\",\"qya\",\"dan\",\"pes\",\"slk\",\"eus\",\"cycl\",\"acm\",\"tgl\",\"lvs\",\"kaz\",\"hye\",\"hin\",\"lit\",\"ben\",\"cat\",\"bos\",\"hrv\",\"tha\",\"orv\",\"cha\",\"mon\",\"lzh\",\"scn\",\"gle\",\"mkd\",\"slv\",\"frm\",\"glg\",\"vol\",\"ain\",\"jbo\",\"tok\",\"ina\",\"nds\",\"mal\",\"tlh\",\"roh\",\"ltz\",\"oss\",\"ido\",\"gla\",\"mlt\",\"sco\",\"ast\",\"jav\",\"oci\",\"ile\",\"ota\",\"xal\",\"tel\",\"sjn\",\"nov\",\"khm\",\"tpi\",\"ang\",\"aze\",\"tgk\",\"tuk\",\"chv\",\"hsb\",\"dsb\",\"bod\",\"sme\",\"cym\",\"mri\",\"ksh\",\"kmr\",\"ewe\",\"kab\",\"ber\",\"tpw\",\"udm\",\"lld\",\"pms\",\"lad\",\"grn\",\"mlg\",\"xho\",\"pnb\",\"grc\",\"hat\",\"lao\",\"npi\",\"cor\",\"nah\",\"avk\",\"mar\",\"guj\",\"pan\",\"kir\",\"myv\",\"prg\",\"sux\",\"crs\",\"ckt\",\"bak\",\"zlm\",\"hil\",\"cbk\",\"chr\",\"nav\",\"lkt\",\"enm\",\"arq\",\"lin\",\"abk\",\"pcd\",\"rom\",\"gsw\",\"tam\",\"zul\",\"awa\",\"wln\",\"amh\",\"bar\",\"hbo\",\"mhr\",\"bho\",\"mrj\",\"ckb\",\"osx\",\"pfl\",\"mgm\",\"sna\",\"mah\",\"hau\",\"kan\",\"nog\",\"sin\",\"glv\",\"dng\",\"kal\",\"liv\",\"vro\",\"apc\",\"jdt\",\"fur\",\"che\",\"haw\",\"yor\",\"crh\",\"pdc\",\"ppl\",\"kin\",\"shs\",\"mnw\",\"tet\",\"sah\",\"kum\",\"ngt\",\"nya\",\"pus\",\"hif\",\"mya\",\"moh\",\"wol\",\"tir\",\"ton\",\"lzz\",\"oar\",\"lug\",\"brx\",\"non\",\"mww\",\"hak\",\"nlv\",\"ngu\",\"bua\",\"aym\",\"vec\",\"ibo\",\"tkl\",\"bam\",\"kha\",\"ceb\",\"lou\",\"fuc\",\"smo\",\"gag\",\"lfn\",\"arg\",\"umb\",\"tyv\",\"kjh\",\"oji\",\"cyo\",\"urh\",\"kzj\",\"pam\",\"srd\",\"lmo\",\"swg\",\"mdf\",\"gil\",\"snd\",\"tso\",\"sot\",\"zza\",\"tsn\",\"pau\",\"som\",\"egl\",\"ady\",\"asm\",\"ori\",\"dtp\",\"cho\",\"max\",\"kam\",\"niu\",\"sag\",\"ilo\",\"kaa\",\"fuv\",\"nch\",\"hoc\",\"iba\",\"gbm\",\"sun\",\"war\",\"mvv\",\"pap\",\"ary\",\"kxi\",\"csb\",\"pag\",\"cos\",\"rif\",\"kek\",\"krc\",\"aii\",\"ban\",\"ssw\",\"tvl\",\"mfe\",\"tah\",\"bvy\",\"bcl\",\"hnj\",\"nau\",\"nst\",\"afb\",\"quc\",\"min\",\"tmw\",\"mad\",\"bjn\",\"mai\",\"cjy\",\"got\",\"hsn\",\"gan\",\"tzl\",\"dws\",\"ldn\",\"afh\",\"sgs\",\"krl\",\"vep\",\"rue\",\"tly\",\"mic\",\"ext\",\"izh\",\"sma\",\"jam\",\"cmo\",\"mwl\",\"kpv\",\"koi\",\"bis\",\"ike\",\"run\",\"evn\",\"ryu\",\"mnc\",\"aoz\",\"otk\",\"kas\",\"aln\",\"akl\",\"yua\",\"shy\",\"fkv\",\"gos\",\"fij\",\"thv\",\"zgh\",\"gcf\",\"cay\",\"xmf\",\"tig\",\"div\",\"lij\",\"rap\",\"hrx\",\"cpi\",\"tts\",\"gaa\",\"tmr\",\"iii\",\"ltg\",\"bzt\",\"syc\",\"emx\",\"gom\",\"chg\",\"osp\",\"stq\",\"frr\",\"fro\",\"nys\",\"toi\",\"new\",\"phn\",\"jpa\",\"rel\",\"drt\",\"chn\",\"pli\",\"laa\",\"bal\",\"hdn\",\"hax\",\"mik\",\"ajp\",\"xqa\",\"pal\",\"crk\",\"mni\",\"lut\",\"ayl\",\"ood\",\"sdh\",\"ofs\",\"nus\",\"kiu\",\"diq\",\"qxq\",\"alt\",\"bfz\",\"klj\",\"mus\",\"srn\",\"guc\",\"lim\",\"zea\",\"shi\",\"mnr\",\"bom\",\"sat\",\"szl\"]\r\nfeatures = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})\r\nnum_labels = features['label'].num_classes\r\ndata_files = { \"train\": \"train.csv\", \"test\": \"test.csv\" }\r\nsentences = load_dataset(\r\n \"loretoparisi/tatoeba-sentences\",\r\n data_files=data_files,\r\n delimiter='\\t', \r\n column_names=['label', 'text'],\r\n)\r\n# You can make this part faster with num_proc=<some int>\r\nsentences = sentences.map(lambda ex: features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None, features=features)\r\n```\r\n\r\n@lhoestq IIRC, I suggested adding `cast_to_storage` to `ClassLabel` + `table_cast` to the packaged loaders if the `ClassLabel`/`Image`/`Audio` type is present in `features` to avoid this kind of error, but your concern was speed. IMO shouldn't be a problem if we do `table_cast` only when these features are present.",
"I agree packaged loaders should support `ClassLabel` feature without throwing an error.",
"@albertvillanova @mariosasko thank you, with that change now I get\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n[<ipython-input-9-eeb68eeb9bec>](https://localhost:8080/#) in <module>()\r\n 11 )\r\n 12 # You can make this part faster with num_proc=<some int>\r\n---> 13 sentences = sentences.map(lambda ex: features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None, features=features)\r\n 14 sentences = sentences.shuffle()\r\n\r\n8 frames\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n 2193 if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table)):\r\n 2194 raise TypeError(\r\n-> 2195 f\"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.\"\r\n 2196 )\r\n 2197 elif isinstance(indices, list) and isinstance(processed_inputs, Mapping):\r\n\r\nTypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'int'>. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.\r\n```\r\n\r\nthe error is raised by [this](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2221)\r\n\r\n```\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n```",
"@mariosasko changed it like\r\n\r\n```python\r\nsentences = sentences.map(lambda ex: {\"label\" : features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None}, features=features)\r\n```\r\n\r\nto avoid the above errorr.",
"Any update on this? Is this correct ?\r\n> @mariosasko changed it like\r\n> \r\n> ```python\r\n> sentences = sentences.map(lambda ex: {\"label\" : features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None}, features=features)\r\n> ```\r\n> \r\n> to avoid the above errorr.\r\n\r\n"
] | 2022-04-25T07:28:42 | 2022-05-31T12:16:31 | 2022-05-31T12:16:31 |
NONE
| null | null | null |
### System Info
```shell
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.10.0+cu111 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
```
### Who can help?
@LysandreJik
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
features = features
```
ERROR:
```
ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None)
Value(dtype='string', id=None)
Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39
Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...
Downloading data files: 100%
2/2 [00:18<00:00, 8.06s/it]
Downloading data: 100%
391M/391M [00:13<00:00, 35.3MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 36.5MB/s]
Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
15 frames
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
ValueError: invalid literal for int() with base 10: 'cmn'
```
while loading without `features` it loads without errors
```
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text']
)
```
but the `label` col seems to be wrong (without the `ClassLabel` object):
```
sentences['train'].features
{'label': Value(dtype='string', id=None),
'text': Value(dtype='string', id=None)}
```
The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences
Dataset format is:
```
ces Nechci vědět, co je tam uvnitř.
ces Kdo o tom chce slyšet?
deu Tom sagte, er fühle sich nicht wohl.
ber Mel-iyi-d anida-t tura ?
hun Gondom lesz rá rögtön.
ber Mel-iyi-d anida-tt tura ?
deu Ich will dich nicht reden hören.
```
### Expected behavior
```shell
correctly load train and test files.
```
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[
"Hi ! Maybe one of the objects in the function is not deterministic across sessions ? You can read more about it and how to investigate here: https://huggingface.co/docs/datasets/about_cache",
"Hi @apsdehal! Can you verify that replacing\r\n```python\r\ndef fetch_single_image(image_url, timeout=None, retries=0):\r\n for _ in range(retries + 1):\r\n try:\r\n request = urllib.request.Request(\r\n image_url,\r\n data=None,\r\n headers={\"user-agent\": get_datasets_user_agent()},\r\n )\r\n with urllib.request.urlopen(request, timeout=timeout) as req:\r\n image = PIL.Image.open(io.BytesIO(req.read()))\r\n break\r\n except Exception:\r\n image = None\r\n return image\r\n```\r\nwith \r\n```python\r\nUSER_AGENT = get_datasets_user_agent()\r\n\r\ndef fetch_single_image(image_url, timeout=None, retries=0):\r\n for _ in range(retries + 1):\r\n try:\r\n request = urllib.request.Request(\r\n image_url,\r\n data=None,\r\n headers={\"user-agent\": USER_AGENT},\r\n )\r\n with urllib.request.urlopen(request, timeout=timeout) as req:\r\n image = PIL.Image.open(io.BytesIO(req.read()))\r\n break\r\n except Exception:\r\n image = None\r\n return image\r\n```\r\nfixes the issue?",
"Thanks @mariosasko. That does fix the issue. In general, I think these image downloading utilities since they are being used by a lot of image dataset should be provided as a part of `datasets` library right to keep the logic consistent and READMEs smaller? If they already exists, that is also great, please point me to those. I saw that `http_get` does exist.",
"You can find my rationale (and a proposed solution) for why these utilities are not a part of `datasets` here: https://github.com/huggingface/datasets/pull/4100#issuecomment-1097994003.",
"Makes sense. But, I think as the number of image datasets as grow, more people are copying pasting original code from docs to work as it is while we make fixes to them later. I think we do need a central place for these to avoid that confusion as well as more easier access to image datasets. Should we restart that discussion, possible on slack?"
] | 2022-04-22T07:47:08 | 2022-04-26T17:00:32 | 2022-04-26T13:38:26 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch.
## Steps to reproduce the bug
Using the example provided in `red_caps` dataset.
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
import datasets
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": get_datasets_user_agent()},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"]))
return batch
def process_image_urls(batch):
processed_batch_image_urls = []
for image_url in batch["image_url"]:
processed_example_image_urls = []
image_url_splits = re.findall(r"http\S+", image_url)
for image_url_split in image_url_splits:
if "imgur" in image_url_split and "," in image_url_split:
for image_url_part in image_url_split.split(","):
if not image_url_part:
continue
image_url_part = image_url_part.strip()
root, ext = os.path.splitext(image_url_part)
if not root.startswith("http"):
root = "http://i.imgur.com/" + root
root = root.split("#")[0]
if not ext:
ext = ".jpg"
ext = re.split(r"[?%]", ext)[0]
image_url_part = root + ext
processed_example_image_urls.append(image_url_part)
else:
processed_example_image_urls.append(image_url_split)
processed_batch_image_urls.append(processed_example_image_urls)
batch["image_url"] = processed_batch_image_urls
return batch
dset = load_dataset("red_caps", "jellyfish")
dset = dset.map(process_image_urls, batched=True, num_proc=4)
features = dset["train"].features.copy()
features["image"] = datasets.Sequence(datasets.Image())
num_threads = 5
dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads})
```
Run this in an interpretor or as a script twice and see that the cache is missed the second time.
## Expected results
At reload there should not be any cache miss
## Actual results
Every time script is run, cache is missed and dataset is built from scratch.
## Environment info
- `datasets` version: 2.1.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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| 4,198 |
There is no dataset
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[] | 2022-04-21T19:19:26 | 2022-05-03T11:29:05 | 2022-04-22T06:12:25 |
NONE
| null | null | null |
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
|
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| 4,196 |
Embed image and audio files in `save_to_disk`
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[] | 2022-04-21T16:25:18 | 2022-12-14T18:22:59 | 2022-12-14T18:22:59 |
MEMBER
| null | null | null |
Following https://github.com/huggingface/datasets/pull/4184, currently a dataset saved using `save_to_disk` doesn't actually contain the bytes of the image or audio files. Instead it stores the path to your local files.
Adding `embed_external_files` and set it to True by default to save_to_disk would be kind of a breaking change since some users will get bigger Arrow files when updating the lib, but the advantages are nice:
- the resulting dataset is self contained, in case you want to delete your cache for example or share it with someone else
- users also upload these Arrow files to cloud storage via the fs parameter, and in this case they would expect to upload a self-contained dataset
- consistency with push_to_hub
This can be implemented at the same time as sharding for `save_to_disk` for efficiency, and reuse the helpers from `push_to_hub` to embed the external files.
cc @mariosasko
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I_kwDODunzps5IKbPK
| 4,192 |
load_dataset can't load local dataset,Unable to find ...
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[
"Hi! :)\r\n\r\nI believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please?",
"Hi @ahf876828330, \r\n\r\nAs @stevhliu pointed out, the proper way to load a dataset is not trying to load its metadata file.\r\n\r\nIn your case, as the dataset script is local, you should better point to your local loading script:\r\n```python\r\ndataset = load_dataset(\"dataset/opus_books.py\")\r\n```\r\n\r\nPlease, feel free to re-open this issue if the previous code snippet does not work for you.",
"> Hi! :)\r\n> \r\n> I believe that should work unless `dataset_infos.json` isn't actually a dataset. For Hugging Face datasets, there is usually a file named `dataset_infos.json` which contains metadata about the dataset (eg. the dataset citation, license, description, etc). Can you double-check that `dataset_infos.json` isn't just metadata please?\r\n\r\nYes，you are right!So if I have a metadata dataset local,How can I turn it to a dataset that can be used by the load_dataset() function？Are there some examples?",
"The metadata file isn't a dataset so you can't turn it into one. You should try @albertvillanova's code snippet above (now merged in the docs [here](https://huggingface.co/docs/datasets/master/en/loading#local-loading-script)), which uses your local loading script `opus_books.py` to:\r\n\r\n1. Download the actual dataset. \r\n2. Once the dataset is downloaded, `load_dataset` will load it for you."
] | 2022-04-21T08:28:58 | 2022-04-25T16:51:57 | 2022-04-22T07:39:53 |
NONE
| null | null | null |
Traceback (most recent call last):
File "/home/gs603/ahf/pretrained/model.py", line 48, in <module>
dataset = load_dataset("json",data_files="dataset/dataset_infos.json")
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset
**config_kwargs,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder
data_files=data_files,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory
download_mode=download_mode,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module
data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained
the code is in the model.py,why I can't use the load_dataset function to load my local dataset?
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feat: create an `Array3D` column from a list of arrays of dimension 2
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"Hi @SaulLu, thanks for your proposal.\r\n\r\nJust I got a bit confused about the dimensions...\r\n- For the 2D case, you mention it is possible to create an `Array2D` from a list of arrays of dimension 1\r\n- However, you give an example of creating an `Array2D` from arrays of dimension 2:\r\n - the values of `data_map` are arrays of dimension 2\r\n - the outer list in `prepare_dataset_2D` should not be taken into account in the dimension counting, as it is used because in `map` you pass `batched=True`\r\n\r\nNote that for the 3D alternatives you mention:\r\n- In `prepare_dataset_3D_ter`, you create an `Array3D` from arrays of dimension 3:\r\n - the array `data_map[index][np.newaxis, :, :]` has dimension 3\r\n - the outer list in `prepare_dataset_3D_ter` is the one used by `batched=True`\r\n- In `prepare_dataset_3D_bis`, you create an `Array3D` from a list of list of lists:\r\n - the value of `data_map[index].tolist()` is a list of lists\r\n - it is enclosed by another list `[data_map[index].tolist()]`, thus giving a list of list of lists\r\n - the outer list is the one used by `batched=True`\r\n\r\nTherefore, if I understand correctly, your request would be to be able to create an `Array3D` from a list of an array of dimension 2:\r\n- In `prepare_dataset_3D`, `data_map[index]` is an array of dimension 2\r\n- it is enclosed by a list `[data_map[index]]`, thus giving a list of an array of dimension 2\r\n- the outer list is the one used by `batched=True`\r\n\r\nPlease, feel free to tell me if I did not understand you correctly.",
"Hi @albertvillanova ,\r\n\r\nIndeed my message was confusing and you guessed right :smile: : I think would be interesting to be able to create an Array3D from a list of an array of dimension 2. \r\n\r\nFor the 2D case I should have given as a \"similar\" example:\r\n```python\r\n\r\ndata_map_1D = {\r\n 1: np.array([0.2, 0.4]),\r\n 2: np.array([0.1, 0.4]),\r\n}\r\n\r\ndef prepare_dataset_2D(batch):\r\n batch[\"pixel_values\"] = [[data_map_1D[index]] for index in batch[\"id\"]]\r\n return batch\r\n \r\nds_2D = ds.map(\r\n prepare_dataset_2D, \r\n batched=True, \r\n remove_columns=ds.column_names, \r\n features=features.Features({\"pixel_values\": features.Array2D(shape=(1, 2), dtype=\"float32\")})\r\n)\r\n```"
] | 2022-04-20T18:04:32 | 2022-05-12T15:08:40 | 2022-05-12T15:08:40 |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
It is possible to create an `Array2D` column from a list of arrays of dimension 1. Similarly, I think it might be nice to be able to create a `Array3D` column from a list of lists of arrays of dimension 1.
To illustrate my proposal, let's take the following toy dataset t:
```python
import numpy as np
from datasets import Dataset, features
data_map = {
1: np.array([[0.2, 0,4],[0.19, 0,3]]),
2: np.array([[0.1, 0,4],[0.19, 0,3]]),
}
def create_toy_ds():
my_dict = {"id":[1, 2]}
return Dataset.from_dict(my_dict)
ds = create_toy_ds()
```
The following 2D processing works without any errors raised:
```python
def prepare_dataset_2D(batch):
batch["pixel_values"] = [data_map[index] for index in batch["id"]]
return batch
ds_2D = ds.map(
prepare_dataset_2D,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array2D(shape=(2, 3), dtype="float32")})
)
```
The following 3D processing doesn't work:
```python
def prepare_dataset_3D(batch):
batch["pixel_values"] = [[data_map[index]] for index in batch["id"]]
return batch
ds_3D = ds.map(
prepare_dataset_3D,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3, dtype="float32")})
)
```
The error raised is:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
[<ipython-input-6-676547e4cd41>](https://localhost:8080/#) in <module>()
3 batched=True,
4 remove_columns=ds.column_names,
----> 5 features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
6 )
12 frames
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1971 new_fingerprint=new_fingerprint,
1972 disable_tqdm=disable_tqdm,
-> 1973 desc=desc,
1974 )
1975 else:
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
518 self: "Dataset" = kwargs.pop("self")
519 # apply actual function
--> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
522 for dataset in datasets:
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
485 }
486 # apply actual function
--> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
489 # re-apply format to the output
[/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
456 # Call actual function
457
--> 458 out = func(self, *args, **kwargs)
459
460 # Update fingerprint of in-place transforms + update in-place history of transforms
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2354 writer.write_table(batch)
2355 else:
-> 2356 writer.write_batch(batch)
2357 if update_data and writer is not None:
2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_batch(self, batch_examples, writer_batch_size)
505 col_try_type = try_features[col] if try_features is not None and col in try_features else None
506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 507 arrays.append(pa.array(typed_sequence))
508 inferred_features[col] = typed_sequence.get_inferred_type()
509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in __arrow_array__(self, type)
175 storage = list_of_np_array_to_pyarrow_listarray(data, type=pa_type.value_type)
176 else:
--> 177 storage = pa.array(data, pa_type.storage_dtype)
178 return pa.ExtensionArray.from_storage(pa_type, storage)
179
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Can only convert 1-dimensional array values
```
**Describe the solution you'd like**
No error in the second scenario and an identical result to the following snippets.
**Describe alternatives you've considered**
There are other alternatives that work such as:
```python
def prepare_dataset_3D_bis(batch):
batch["pixel_values"] = [[data_map[index].tolist()] for index in batch["id"]]
return batch
ds_3D_bis = ds.map(
prepare_dataset_3D_bis,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
)
```
or
```python
def prepare_dataset_3D_ter(batch):
batch["pixel_values"] = [data_map[index][np.newaxis, :, :] for index in batch["id"]]
return batch
ds_3D_ter = ds.map(
prepare_dataset_3D_ter,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
)
```
But both solutions require the user to be aware that `data_map[index]` is an `np.array` type.
cc @lhoestq as we discuss this offline :smile:
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I_kwDODunzps5IFm7v
| 4,185 |
Librispeech documentation, clarification on format
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[
"(@patrickvonplaten )",
"Also cc @lhoestq here",
"The documentation in the code is definitely outdated - thanks for letting me know, I'll remove it in https://github.com/huggingface/datasets/pull/4184 .\r\n\r\nYou're exactly right `audio` `array` already decodes the audio file to the correct waveform. This is done on the fly, which is also why one should **not** do `ds[\"audio\"][\"array\"][0]` as this will decode all dataset samples, but instead `ds[0][\"audio\"][\"array\"]` see: https://huggingface.co/docs/datasets/audio_process#audio-datasets\r\n\r\n",
"So, again to clarify: On disk, only the raw flac file content is stored? Is this also the case after `save_to_disk`?\r\n\r\nAnd is it simple to also store it re-encoded as ogg or mp3 instead?\r\n",
"Hey, \r\n\r\nSorry yeah I was just about to look into this! We actually had an outdated version of Librispeech ASR that didn't save any files, but instead converted the audio files to a byte string, then was then decoded on-the-fly. This however is not very user-friendly so we recently decided to instead show the full path of the audio files with the `path` parameter.\r\n\r\nI'm currently changing this for Librispeech here: https://github.com/huggingface/datasets/pull/4184 .\r\nYou should be able to see the audio file in the original `flac` format under `path` then. I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ? ",
"> I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ?\r\n\r\nSure, I would expect that `load_dataset(\"librispeech_asr\")` would give you the original (not re-encoded) data (flac or already decoded). So such re-encoding logic would be some separate generic function. So I could do sth like `dataset.reencode_as_ogg(**ogg_encode_opts).save_to_disk(...)` or so.\r\n",
"A follow-up question: I wonder whether a Parquet dataset is maybe more what we actually want to have? (Following also my comment here: https://github.com/huggingface/datasets/pull/4184#issuecomment-1105045491.) Because I think we actually would prefer to embed the data content in the dataset.\r\n\r\nSo, instead of `save_to_disk`/`load_from_disk`, we would use `to_parquet`,`from_parquet`? Is there any downside? Are arrow files more efficient?\r\n\r\nRelated is also the doc update in #4193.\r\n",
"`save_to_disk` saves the dataset as an Arrow file, which is the format we use to load a dataset using memory mapping. This way the dataset does not fill your RAM, but is read from your disk instead.\r\n\r\nTherefore you can directly reload a dataset saved with `save_to_disk` using `load_from_disk`.\r\n\r\nParquet files are used for cold storage: to use memory mapping on a Parquet dataset, you first have to convert it to Arrow. We use Parquet to reduce the I/O when pushing/downloading data from the Hugging face Hub. When you load a Parquet file from the Hub, it is converted to Arrow on the fly during the download."
] | 2022-04-20T09:35:55 | 2022-04-21T11:00:53 | null |
NONE
| null | null | null |
https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53
> Note that in order to limit the required storage for preparing this dataset, the audio
> is stored in the .flac format and is not converted to a float32 array. To convert, the audio
> file to a float32 array, please make use of the `.map()` function as follows:
>
> ```python
> import soundfile as sf
> def map_to_array(batch):
> speech_array, _ = sf.read(batch["file"])
> batch["speech"] = speech_array
> return batch
> dataset = dataset.map(map_to_array, remove_columns=["file"])
> ```
Is this still true?
In my case, `ds["train.100"]` returns:
```
Dataset({
features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],
num_rows: 28539
})
```
and taking the first instance yields:
```
{'file': '374-180298-0000.flac',
'audio': {'path': '374-180298-0000.flac',
'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'sampling_rate': 16000},
'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED',
'speaker_id': 374,
'chapter_id': 180298,
'id': '374-180298-0000'}
```
The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong?
But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk?
Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk?
A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert?
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I_kwDODunzps5IBPgz
| 4,182 |
Zenodo.org download is not responding
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[
"[Off topic but related: Is the uptime of S3 provably better than Zenodo's?]",
"Hi @dkajtoch, please note that at HuggingFace we are not hosting this dataset: we are just using a script to download their data file and create a dataset from it.\r\n\r\nIt was the dataset owners decision to host their data at Zenodo. You can see this on their website: https://marcobaroni.org/composes/sick.html\r\n\r\nAnd yes, you are right: Zenodo is currently having some incidents and people are reporting problems from it.\r\n\r\nOn the other hand, we could contact the data owners and propose them to host their data at our Hugging Face Hub.\r\n\r\n@julien-c I guess so.\r\n",
"Thanks @albertvillanova. I know that the problem lies in the source data. I just wanted to point out that these kind of problems are unavoidable without having one place where data sources are cached. Websites may go down or data sources may move. Having a copy in Hugging Face Hub would be a great solution. ",
"Definitely, @dkajtoch! But we have to ask permission to the data owners. And many dataset licenses directly forbid data redistribution: in those cases we are not allowed to host their data on our Hub.",
"Ahhh good point! License is the problem :("
] | 2022-04-19T12:26:57 | 2022-04-20T07:11:05 | 2022-04-20T07:11:05 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
Source download_url from zenodo.org does not respond.
`_DOWNLOAD_URL = "https://zenodo.org/record/2787612/files/SICK.zip?download=1"`
Other datasets also use zenodo.org to store data and they cannot be downloaded as well.
It would be better to actually use more reliable way to store original data like s3 bucket.
## Steps to reproduce the bug
```python
load_dataset("sick")
```
## Expected results
Dataset should be downloaded.
## Actual results
ConnectionError: Couldn't reach https://zenodo.org/record/2787612/files/SICK.zip?download=1 (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out. (read timeout=100)")))
## Environment info
- `datasets` version: 2.1.0
- Platform: Darwin-21.4.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
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I_kwDODunzps5IA5b1
| 4,181 |
Support streaming FLEURS dataset
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[
"Yes, you just have to use `dl_manager.iter_archive` instead of `dl_manager.download_and_extract`.\r\n\r\nThat's because `download_and_extract` doesn't support TAR archives in streaming mode.",
"Tried to make it streamable, but I don't think it's really possible. @lhoestq @polinaeterna maybe you guys can check: \r\nhttps://huggingface.co/datasets/google/fleurs/commit/dcf80160cd77977490a8d32b370c027107f2407b \r\n\r\nreal quick. \r\n\r\nI think the problem is that we cannot ensure that the metadata file is found before the audio. Or is this possible somehow @lhoestq ? ",
"@patrickvonplaten I think the metadata file should be found first because the audio files are contained in a folder next to the metadata files (just as in common voice), so the metadata files should be \"on top of the list\" as they are closer to the root in the directories hierarchy ",
"@patrickvonplaten but apparently it doesn't... I don't really know why.",
"Yeah! Any ideas what could be the reason here? cc @lhoestq ?",
"The order of the files is determined when the TAR archive is created, depending on the commands the creator ran.\r\nIf the metadata file is not at the beginning of the file, that makes streaming completely inefficient. In this case the TAR archive needs to be recreated in an appropriate order.",
"Actually we could maybe just host the metadata file ourselves and then stream the audio data only. Don't think that this would be a problem for the FLEURS authors (I can ask them :-)) ",
"I made a PR to their repo to support streaming (by uploading the metadata file to the Hub). See:\r\n- https://huggingface.co/datasets/google/fleurs/discussions/4",
"I'm closing this issue as the PR above has been merged."
] | 2022-04-19T11:09:56 | 2022-07-25T11:44:02 | 2022-07-25T11:44:02 |
CONTRIBUTOR
| null | null | null |
## Dataset viewer issue for '*name of the dataset*'
https://huggingface.co/datasets/google/fleurs
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
Am I the one who added this dataset ? Yes
Can I fix this somehow in the script? @lhoestq @severo
|
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I_kwDODunzps5IAUNQ
| 4,180 |
Add some iteration method on a dataset column (specific for inference)
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[
"Thanks for the suggestion ! I agree it would be nice to have something directly in `datasets` to do something as simple as that\r\n\r\ncc @albertvillanova @mariosasko @polinaeterna What do you think if we have something similar to pandas `Series` that wouldn't bring everything in memory when doing `dataset[\"audio\"]` ? Currently it returns a list with all the decoded audio data in memory.\r\n\r\nIt would be a breaking change though, since `isinstance(dataset[\"audio\"], list)` wouldn't work anymore, but we could implement a `Sequence` so that `dataset[\"audio\"][0]` still works and only loads one item in memory.\r\n\r\nYour alternative suggestion with `iterate` is also sensible, though maybe less satisfactory in terms of experience IMO",
"I agree that current behavior (decoding all audio file sin the dataset when accessing `dataset[\"audio\"]`) is not useful, IMHO. Indeed in our docs, we are constantly warning our collaborators not to do that.\r\n\r\nTherefore I upvote for a \"useful\" behavior of `dataset[\"audio\"]`. I don't think the breaking change is important in this case, as I guess no many people use it with its current behavior. Therefore, for me it seems reasonable to return a generator (instead of an in-memeory list) for \"special\" features, like Audio/Image.\r\n\r\n@lhoestq on the other hand I don't understand your proposal about Pandas-like... ",
"I recall I had the same idea while working on the `Image` feature, so I agree implementing something similar to `pd.Series` that lazily brings elements in memory would be beneficial.",
"@lhoestq @mariosasko Could you please give a link to that new feature of `pandas.Series`? As far as I remember since I worked with pandas for more than 6 years, there was no lazy in-memory feature; it was everything in-memory; that was the reason why other frameworks were created, like Vaex or Dask, e.g. ",
"Yea pandas doesn't do lazy loading. I was referring to pandas.Series to say that they have a dedicated class to represent a column ;)"
] | 2022-04-19T09:15:45 | 2022-04-21T10:30:58 | null |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
Currently, `dataset["audio"]` will load EVERY element in the dataset in RAM, which can be quite big for an audio dataset.
Having an iterator (or sequence) type of object, would make inference with `transformers` 's `pipeline` easier to use and not so memory hungry.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
For a non breaking change:
```python
for audio in dataset.iterate("audio"):
# {"array": np.array(...), "sampling_rate":...}
```
For a breaking change solution (not necessary), changing the type of `dataset["audio"]` to a sequence type so that
```python
pipe = pipeline(model="...")
for out in pipe(dataset["audio"]):
# {"text":....}
```
could work
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
```python
def iterate(dataset, key):
for item in dataset:
yield dataset[key]
for out in pipeline(iterate(dataset, "audio")):
# {"array": ...}
```
This works but requires the helper function which feels slightly clunky.
**Additional context**
Add any other context about the feature request here.
The context is actually to showcase better integration between `pipeline` and `datasets` in the Quicktour demo: https://github.com/huggingface/transformers/pull/16723/files
@lhoestq
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I_kwDODunzps5IAKJe
| 4,179 |
Dataset librispeech_asr fails to load
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[
"@patrickvonplaten Hi! I saw that you prepared this? :)",
"Another thing, but maybe this should be a separate issue: As I see from the code, it would try to use up to 16 simultaneous downloads? This is problematic for Librispeech or anything on OpenSLR. On [the homepage](https://www.openslr.org/), it says:\r\n\r\n> If you want to download things from this site, please download them one at a time, and please don't use any fancy software-- just download things from your browser or use 'wget'. We have a firewall rule to drop connections from hosts with more than 5 simultaneous connections, and certain types of download software may activate this rule.\r\n\r\nRelated: https://github.com/tensorflow/datasets/issues/3885",
"Hey @albertz,\r\n\r\nNice to see you here! It's been a while ;-) ",
"Sorry maybe the docs haven't been super clear here. By `split` we mean one of `train.500`, `train.360`, `train.100`, `validation`, `test`. For Librispeech, you'll have to specific a config (either `other` or `clean`) though:\r\n\r\n```py\r\ndatasets.load_dataset(\"librispeech_asr\", \"clean\")\r\n```\r\n\r\nshould work and give you all splits (being \"train\", \"test\", ...) for the clean config of the dataset.\r\n",
"If you need both `\"clean\"` and `\"other\"` I think you'll have to do concatenate them as follows: \r\n\r\n```py\r\nfrom datasets import concatenate_datasets, load_dataset\r\n\r\nother = load_dataset(\"librispeech_asr\", \"other\")\r\nclean = load_dataset(\"librispeech_asr\", \"clean\")\r\n\r\nlibrispeech = concatenate_datasets([other, clean])\r\n```\r\n\r\nSee https://huggingface.co/docs/datasets/v2.1.0/en/process#concatenate",
"Downloading one split would be:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\n\r\nother = load_dataset(\"librispeech_asr\", \"other\", split=\"train.500\")\r\n```\r\n\r\n\r\n",
"cc @lhoestq FYI maybe the docs can be improved here",
"Ah thanks. But wouldn't it be easier/nicer (and more canonical) to just make it in a way that simply `load_dataset(\"librispeech_asr\")` works?",
"Pinging @lhoestq here, think this could make sense! Not sure however how the dictionary would then look like",
"Would it make sense to have `clean` as the default config ?\r\n\r\nAlso I think `load_dataset(\"librispeech_asr\")` should have raised you an error that says that you need to specify a config\r\n\r\nI also opened a PR to improve the doc: https://github.com/huggingface/datasets/pull/4183",
"> Would it make sense to have `clean` as the default config ?\r\n\r\nI think a user would expect that the default would give you the full dataset.\r\n\r\n> Also I think `load_dataset(\"librispeech_asr\")` should have raised you an error that says that you need to specify a config\r\n\r\nIt does raise an error, but this error confused me because I did not understand why I needed a config, or why I could not simply download the whole dataset, which is what people usually do with Librispeech.\r\n",
"+1 for @albertz. Also think lots of people download the whole dataset (`\"clean\"` + `\"other\"`) for Librispeech.\r\n\r\nThink there are also some people though who:\r\n- a) Don't have the memory to store the whole dataset\r\n- b) Just want to evaluate on one of the two configs",
"Ok ! Adding the \"all\" configuration would do the job then, thanks ! In the \"all\" configuration we can merge all the train.xxx splits into one \"train\" split, or keep them separate depending on what's the most practical to use (probably put everything in \"train\" no ?)",
"I'm not too familiar with how to work with HuggingFace datasets, but people often do some curriculum learning scheme, where they start with train.100, later go over to train.100 + train.360, and then later use the whole train (960h). It would be good if this is easily possible.\r\n",
"Hey @albertz, \r\n\r\nopened a PR here. Think by adding the \"subdataset\" class to each split \"train\", \"dev\", \"other\" as shown here: https://github.com/huggingface/datasets/pull/4184/files#r853272727 it should be easily possible (e.g. with the filter function https://huggingface.co/docs/datasets/v2.1.0/en/package_reference/main_classes#datasets.Dataset.filter )",
"But also since everything is cached one could also just do:\r\n\r\n```python\r\nload_dataset(\"librispeech\", \"clean\", \"train.100\")\r\nload_dataset(\"librispeech\", \"clean\", \"train.100+train.360\")\r\nload_dataset(\"librispeech\" \"all\", \"train\") \r\n```",
"Hi @patrickvonplaten ,\r\n\r\nload_dataset(\"librispeech_asr\", \"clean\", \"train.100\") actually downloads the whole dataset and not the 100 hr split, is this a bug?",
"Hmm, I don't really see how that's possible: https://github.com/huggingface/datasets/blob/d22e39a0693d4be7410cf9a5d41fd5aac22be3cc/datasets/librispeech_asr/librispeech_asr.py#L51\r\n\r\nNote that all datasets related to `\"clean\"` are downloaded, but only `\"train.100\"` should be used. \r\n\r\ncc @lhoestq @albertvillanova @mariosasko can we do anything against download dataset links that are not related to the \"split\" that one actually needs. E.g. why should the split `\"train.360\"` be downloaded if for the user executes the above command:\r\n\r\n```py\r\nload_dataset(\"librispeech_asr\", \"clean\", \"train.100\")\r\n```",
"@patrickvonplaten This problem is a bit harder than it may seem, and it has to do with how our scripts are structured - `_split_generators` downloads data for a split before its definition. There was an attempt to fix this in https://github.com/huggingface/datasets/pull/2249, but it wasn't flexible enough. Luckily, I have a plan of attack, and this issue is on our short-term roadmap, so I'll work on it soon.\r\n\r\nIn the meantime, one can use streaming or manually download a dataset script, remove unwanted splits and load a dataset via `load_dataset`.",
"> load_dataset(\"librispeech_asr\", \"clean\", \"train.100\") actually downloads the whole dataset and not the 100 hr split, is this a bug?\r\n\r\nSince this bug is still there and google led me here when I was searching for a solution, I am writing down how to quickly fix it (as suggested by @mariosasko) for whoever else is not familiar with how the HF Hub works.\r\n\r\nDownload the [librispeech_asr.py](https://huggingface.co/datasets/librispeech_asr/blob/main/librispeech_asr.py) script and remove the unwanted splits both from the [`_DL_URLS` dictionary](https://huggingface.co/datasets/librispeech_asr/blob/main/librispeech_asr.py#L47-L68) and from the [`_split_generators` function](https://huggingface.co/datasets/librispeech_asr/blob/main/librispeech_asr.py#L121-L241).\r\n[Here ](https://huggingface.co/datasets/andreagasparini/librispeech_test_only) I made an example with only the test sets.\r\n\r\nThen either save the script locally and load the dataset via \r\n```python\r\nload_dataset(\"${local_path}/librispeech_asr.py\")\r\n```\r\n\r\nor [create a new dataset repo on the hub](https://huggingface.co/new-dataset) named \"librispeech_asr\" and upload the script there, then you can just run\r\n```python\r\nload_dataset(\"${hugging_face_username}/librispeech_asr\")\r\n```",
"Fixed by https://github.com/huggingface/datasets/pull/4184"
] | 2022-04-19T08:45:48 | 2022-07-27T16:10:00 | 2022-07-27T16:10:00 |
NONE
| null | null | null |
## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.
## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```
## Expected results
It should download and prepare the whole dataset (all subsets).
In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.
Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.
## Actual results
```
...
File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
locals:
archive_path = <not found>
dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
_DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
self.config.name = <local> 'default', len = 7
KeyError: 'default'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
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I_kwDODunzps5H6fdr
| 4,176 |
Very slow between two operations
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[] | 2022-04-17T23:52:29 | 2022-04-18T00:03:00 | 2022-04-18T00:03:00 |
NONE
| null | null | null |
Hello, in the processing stage, I use two operations. The first one : map + filter, is very fast and it uses the full cores, while the socond step is very slow and did not use full cores.
Also, there is a significant lag between them. Am I missing something ?
```
raw_datasets = raw_datasets.map(split_func,
batched=False,
num_proc=args.preprocessing_num_workers,
load_from_cache_file=not args.overwrite_cache,
desc = "running split para ==>")\
.filter(lambda example: example['text1']!='' and example['text2']!='',
num_proc=args.preprocessing_num_workers, desc="filtering ==>")
processed_datasets = raw_datasets.map(
preprocess_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on dataset===>",
)
```
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Timit_asr dataset cannot be previewed recently
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[
"Thanks for reporting. The bug has already been detected, and we hope to fix it soon.",
"TIMIT is now a dataset that requires manual download, see #4145 \r\n\r\nTherefore it might take a bit more time to fix it",
"> TIMIT is now a dataset that requires manual download, see #4145\r\n> \r\n> Therefore it might take a bit more time to fix it\r\n\r\nThank you for your quickly response. Exactly, I also found the manual download issue in the morning. But when I used *list_datasets()* to check the available datasets, *'timit_asr'* is still in the list. So I am a little bit confused. If *'timit_asr'* need to be manually downloaded, does that mean we can **not** automatically download it **any more** in the future?",
"Yes exactly. If you try to load the dataset it will ask you to download it manually first, and to pass the downloaded and extracted data like `load_dataset(\"timir_asr\", data_dir=\"path/to/extracted/data\")`\r\n\r\nThe URL we were using was coming from a host that doesn't have the permission to redistribute the data, and the dataset owners (LDC) notified us about it.",
"I downloaded the timit_asr data and unzipped. But I can't run my code. Could you resolve this problem for me? Thanks\r\n\r\n import soundfile as sf\r\n import torch\r\n from datasets import load_dataset\r\n dataset = load_dataset(\"timit_asr\", data_dir=\"/Users/nguyenvannham/Documents/test_case/data\")\r\n \r\n \r\n Generating train split: 0 examples [00:00, ? examples/s]\r\n\r\nGenerating train split: 0 examples [00:00, ? examples/s]Traceback (most recent call last):\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/site-packages/datasets/builder.py\", line 1571, in _prepare_split_single\r\n for key, record in generator:\r\n\r\n File \"/Users/nguyenvannham/.cache/huggingface/modules/datasets_modules/datasets/timit_asr/43f9448dd5db58e95ee48a277f466481b151f112ea53e27f8173784da9254fb2/timit_asr.py\", line 138, in _generate_examples\r\n with txt_path.open(encoding=\"utf-8\") as op:\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/pathlib.py\", line 1252, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/pathlib.py\", line 1120, in _opener\r\n return self._accessor.open(self, flags, mode)\r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '/Users/nguyenvannham/Documents/test_case/data/train/DR1/FCJF0/SA1.WAV.TXT'\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n\r\n File \"/var/folders/t9/l8d3rwpn1k33_gjtqs732lzc0000gn/T/ipykernel_3891/1203313828.py\", line 1, in <module>\r\n dataset = load_dataset(\"timit_asr\", data_dir=\"/Users/nguyenvannham/Documents/test_case/data\")\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/site-packages/datasets/load.py\", line 1758, in load_dataset\r\n builder_instance.download_and_prepare(\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/site-packages/datasets/builder.py\", line 860, in download_and_prepare\r\n self._download_and_prepare(\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/site-packages/datasets/builder.py\", line 1612, in _download_and_prepare\r\n super()._download_and_prepare(\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/site-packages/datasets/builder.py\", line 953, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/site-packages/datasets/builder.py\", line 1450, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n\r\n File \"/opt/anaconda3/envs/audio/lib/python3.9/site-packages/datasets/builder.py\", line 1607, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n\r\nDatasetGenerationError: An error occurred while generating the dataset"
] | 2022-04-14T03:28:31 | 2023-02-03T04:54:57 | 2022-05-06T16:06:51 |
NONE
| null | null | null |
## Dataset viewer issue for '*timit_asr*'
**Link:** *https://huggingface.co/datasets/timit_asr*
Issue: The timit-asr dataset cannot be previewed recently.
Am I the one who added this dataset ? Yes-No
No
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Optional Content Warning for Datasets
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[
"Hi! You can use the `extra_gated_prompt` YAML field in a dataset card for displaying custom messages/warnings that the user must accept before gaining access to the actual dataset. This option also keeps the viewer hidden until the user agrees to terms. ",
"Hi @mariosasko, thanks for explaining how to add this feature. \r\n\r\nIf the current dataset yaml is:\r\n```\r\n---\r\nannotations_creators:\r\n- expert\r\nlanguage_creators:\r\n- expert-generated\r\nlanguages:\r\n- en\r\nlicense:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- monolingual\r\npretty_name: HatemojiBuild\r\nsize_categories:\r\n- 1K<n<10K\r\nsource_datasets:\r\n- original\r\ntask_categories:\r\n- text-classification\r\ntask_ids:\r\n- hate-speech-detection\r\n---\r\n```\r\n\r\nCan you provide a minimal working example of how to added the gated prompt?\r\n\r\nThanks!",
"```\r\n---\r\nannotations_creators:\r\n- expert\r\nlanguage_creators:\r\n- expert-generated\r\nlanguages:\r\n- en\r\nlicense:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- monolingual\r\npretty_name: HatemojiBuild\r\nsize_categories:\r\n- 1K<n<10K\r\nsource_datasets:\r\n- original\r\ntask_categories:\r\n- text-classification\r\ntask_ids:\r\n- hate-speech-detection\r\nextra_gated_prompt: \"This repository contains harmful content.\"\r\n---\r\n```\r\n\\+ enable `User Access requests` under the Settings pane.\r\n\r\nThere's a brief guide here https://discuss.huggingface.co/t/how-to-customize-the-user-access-requests-message/13953 , and you can see the field in action here, https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/blob/main/README.md (you need to agree the terms in the Dataset Card pane to be able to access the files pane, so this comes up 403 at first).\r\n\r\nAnd a working example here! https://huggingface.co/datasets/DDSC/dkhate :) Great to be able to mitigate harms in text.",
"-- is there a way to gate content anonymously, i.e. without registering which users access it?",
"+1 to @leondz's question. One scenario is if you don't want the dataset to be indexed by search engines or viewed in browser b/c of upstream conditions on data, but don't want to collect emails. Some ability to turn off the dataset viewer or add a gating mechanism without emails would be fantastic."
] | 2022-04-13T16:38:01 | 2022-06-09T20:39:02 | null |
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
We now have hate speech datasets on the hub, like this one: https://huggingface.co/datasets/HannahRoseKirk/HatemojiBuild
I'm wondering if there is an option to select a content warning message that appears before the dataset preview? Otherwise, people immediately see hate speech when clicking on this dataset.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
Implementation of a content warning message that separates users from the dataset preview until they click out of the warning.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
Possibly just a way to remove the dataset preview completely? I think I like the content warning option better, though.
**Additional context**
Add any other context about the feature request here.
|
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RGBA images not showing
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[
"Thanks for reporting. It's a known issue, and we hope to fix it soon.",
"Fixed, thanks!"
] | 2022-04-13T06:59:23 | 2022-06-21T16:43:11 | 2022-06-21T16:43:11 |
CONTRIBUTOR
| null | null | null |
## Dataset viewer issue for ceyda/smithsonian_butterflies_transparent
[**Link:** *link to the dataset viewer page*](https://huggingface.co/datasets/ceyda/smithsonian_butterflies_transparent)
Am I the one who added this dataset ? Yes
👉 More of a general issue of 'RGBA' png images not being supported
(the dataset itself is just for the huggan sprint and not that important, consider it just an example)
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ArrayND error in pyarrow 5
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[
"Where do we bump the required pyarrow version? Any inputs on how I fix this issue? ",
"We need to bump it in `setup.py` as well as update some CI job to use pyarrow 6 instead of 5 in `.circleci/config.yaml` and `.github/workflows/benchmarks.yaml`"
] | 2022-04-12T15:41:40 | 2022-05-04T09:29:46 | 2022-05-04T09:29:46 |
MEMBER
| null | null | null |
As found in https://github.com/huggingface/datasets/pull/3903, The ArrayND features fail on pyarrow 5:
```python
import pyarrow as pa
from datasets import Array2D
from datasets.table import cast_array_to_feature
arr = pa.array([[[0]]])
feature_type = Array2D(shape=(1, 1), dtype="int64")
cast_array_to_feature(arr, feature_type)
```
raises
```python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-8-04610f9fa78c> in <module>
----> 1 cast_array_to_feature(pa.array([[[0]]]), Array2D(shape=(1, 1), dtype="int32"))
~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1672 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1673 else:
-> 1674 return func(array, *args, **kwargs)
1675
1676 return wrapper
~/Desktop/hf/datasets/src/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1806 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
1807 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1808 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
1809 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
1810
~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1672 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1673 else:
-> 1674 return func(array, *args, **kwargs)
1675
1676 return wrapper
~/Desktop/hf/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_number_to_str)
1705 array = array.storage
1706 if isinstance(pa_type, pa.ExtensionType):
-> 1707 return pa_type.wrap_array(array)
1708 elif pa.types.is_struct(array.type):
1709 if pa.types.is_struct(pa_type) and (
AttributeError: 'Array2DExtensionType' object has no attribute 'wrap_array'
```
The thing is that `cast_array_to_feature` is called when writing an Arrow file, so creating an Arrow dataset using any ArrayND type currently fails.
`wrap_array` has been added in pyarrow 6, so we can either bump the required pyarrow version or fix this for pyarrow 5
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Inconsistent splits generation for datasets without loading script (packaged dataset puts everything into a single split)
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[] | 2022-04-12T11:15:55 | 2022-04-28T21:02:44 | 2022-04-28T21:02:44 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
Splits for dataset loaders without scripts are prepared inconsistently. I think it might be confusing for users.
## Steps to reproduce the bug
* If you load a packaged datasets from Hub, it infers splits from directory structure / filenames (check out the data [here](https://huggingface.co/datasets/nateraw/test-imagefolder-dataset)):
```python
ds = load_dataset("nateraw/test-imagefolder-dataset")
print(ds)
### Output:
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 6
})
test: Dataset({
features: ['image', 'label'],
num_rows: 4
})
})
```
* If you do the same from locally stored data specifying only directory path you'll get the same:
```python
ds = load_dataset("/path/to/local/data/test-imagefolder-dataset")
print(ds)
### Output:
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 6
})
test: Dataset({
features: ['image', 'label'],
num_rows: 4
})
})
```
* However, if you explicitely specify package name (like `imagefolder`, `csv`, `json`), all the data is put into a single split:
```python
ds = load_dataset("imagefolder", data_dir="/path/to/local/data/test-imagefolder-dataset")
print(ds)
### Output:
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 10
})
})
```
## Expected results
For `load_dataset("imagefolder", data_dir="/path/to/local/data/test-imagefolder-dataset")` I expect the same output as of the two first options.
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I_kwDODunzps5Hm76l
| 4,149 |
load_dataset for winoground returning decoding error
|
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[
"I thought I had fixed it with this after some helpful hints from @severo\r\n```python\r\nimport datasets \r\ntoken = 'hf_XXXXX'\r\ndataset = datasets.load_dataset(\r\n 'facebook/winoground', \r\n name='facebook--winoground', \r\n split='train', \r\n streaming=True,\r\n use_auth_token=token,\r\n)\r\n```\r\nbut I found out that wasn't the case\r\n```python\r\n[x for x in dataset]\r\n...\r\nClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/facebook/winoground/resolve/a86a60456fbbd242e9a744199071a6bd3e7fd9de/examples.jsonl')\r\n```",
"Hi ! This dataset structure (image + labels in a JSON file) is not supported yet, though we're adding support for this in in #4069 \r\n\r\nThe following structure will be supported soon:\r\n```\r\nmetadata.json\r\nimages/\r\n image0.png\r\n image1.png\r\n ...\r\n```\r\nWhere `metadata.json` is a JSON Lines file with labels or other metadata, and each line must have a \"file_name\" field with the name of the image file.\r\n\r\nFor the moment are only supported:\r\n- JSON files only\r\n- image files only\r\n\r\nSince this dataset is a mix of the two, at the moment it fails trying to read the images as JSON.\r\n\r\nTherefore to be able to load this dataset we need to wait for the new structure to be supported (very soon ^^), or add a dataset script in the repository that reads both the JSON and the images cc @TristanThrush \r\n",
"We'll also investigate the issue with the streaming download manager in https://github.com/huggingface/datasets/issues/4139 ;) thanks for reporting",
"Are there any updates on this?",
"In the meantime, anyone can always download the images.zip and examples.jsonl files directly from huggingface.co - let me know if anyone has issues with that.",
"I mirrored the files at https://huggingface.co/datasets/facebook/winoground in a folder on my local machine `winground`\r\nand when I tried\r\n```python\r\nimport datasets\r\nds = datasets.load_from_disk('./winoground')\r\n```\r\nI get the following error\r\n```python\r\n--------------------------------------------------------------------------\r\nFileNotFoundError Traceback (most recent call last)\r\nInput In [2], in <cell line: 1>()\r\n----> 1 ds = datasets.load_from_disk('./winoground')\r\n\r\nFile ~/.local/lib/python3.8/site-packages/datasets/load.py:1759, in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1757 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1758 else:\r\n-> 1759 raise FileNotFoundError(\r\n 1760 f\"Directory {dataset_path} is neither a dataset directory nor a dataset dict directory.\"\r\n 1761 )\r\n\r\nFileNotFoundError: Directory ./winoground is neither a dataset directory nor a dataset dict directory.\r\n```\r\nso still some work to be done on the backend imo.",
"Note that `load_from_disk` is the function that reloads an Arrow dataset saved with `my_dataset.save_to_disk`.\r\n\r\nOnce we do support images with metadata you'll be able to use `load_dataset(\"facebook/winoground\")` directly (or `load_dataset(\"./winoground\")` of you've cloned the winoground repository locally).",
"Apologies for the delay. I added a custom dataset loading script for winoground. It should work now, with an auth token:\r\n\r\n`examples = load_dataset('facebook/winoground', use_auth_token=<your auth token>)`\r\n\r\nLet me know if there are any issues",
"Adding the dataset loading script definitely didn't take as long as I thought it would 😅",
"killer"
] | 2022-04-12T08:16:16 | 2022-05-04T23:40:38 | 2022-05-04T23:40:38 |
CONTRIBUTOR
| null | null | null |
## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.
## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```
## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json
with open('examples.jsonl', 'r') as f:
examples = f.read().split('\n')
# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
'caption_1': 'someone is doing it over',
'collapsed_tag': 'Relation',
'id': 399,
'image_0': 'ex_399_img_0',
'image_1': 'ex_399_img_1',
'num_main_preds': 1,
'secondary_tag': 'Morpheme-Level',
'tag': 'Scope, Preposition'}
```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.
## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
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| 1,201,169,242 |
I_kwDODunzps5HmGNa
| 4,148 |
fix confusing bleu metric example
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[] | 2022-04-12T06:18:26 | 2022-04-13T14:16:34 | 2022-04-13T14:16:34 |
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
I would like to see the example in "Metric Card for BLEU" changed.
The 0th element in the predictions list is not closed in square brackets, and the 1st list is missing a comma.
The BLEU score are calculated correctly, but it is difficult to understand, so it would be helpful if you could correct this.
```
>> predictions = [
... ["hello", "there", "general", "kenobi", # <- no closing square bracket.
... ["foo", "bar" "foobar"] # <- no comma between "bar" and "foobar"
... ]
>>> references = [
... [["hello", "there", "general", "kenobi"]],
... [["foo", "bar", "foobar"]]
... ]
>>> bleu = datasets.load_metric("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results)
{'bleu': 0.6370964381207871, ...
```
**Describe the solution you'd like**
```
>> predictions = [
... ["hello", "there", "general", "kenobi", # <- no closing square bracket.
... ["foo", "bar" "foobar"] # <- no comma between "bar" and "foobar"
... ]
# and
>>> print(results)
{'bleu':1.0, ...
```
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I_kwDODunzps5Hidbt
| 4,146 |
SAMSum dataset viewer not working
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[
"https://huggingface.co/datasets/samsum\r\n\r\n```\r\nStatus code: 400\r\nException: ValueError\r\nMessage: Cannot seek streaming HTTP file\r\n```",
"Currently, only the datasets that can be streamed support the dataset viewer. Maybe @lhoestq @albertvillanova or @mariosasko could give more details about why the dataset cannot be streamed.",
"It looks like the host (https://arxiv.org) doesn't allow HTTP Range requests, which is what we use to stream data.\r\n\r\nThis can be fix if we host the data ourselves, which is ok since the dataset is under CC BY-NC-ND 4.0"
] | 2022-04-11T16:22:57 | 2022-04-29T16:26:09 | 2022-04-29T16:26:09 |
NONE
| null | null | null |
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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I_kwDODunzps5HhZmp
| 4,143 |
Unable to download `Wikepedia` 20220301.en version
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[
"Hi! We've recently updated the Wikipedia script, so these changes are only available on master and can be fetched as follows:\r\n```python\r\ndataset_wikipedia = load_dataset(\"wikipedia\", \"20220301.en\", revision=\"master\")\r\n```",
"Hi, how can I load the previous \"20200501.en\" version of wikipedia which had been downloaded to the default path? Thanks!",
"@JiaQiSJTU just reinstall the previous verision of the package, e.g. `!pip install -q datasets==1.0.0`"
] | 2022-04-11T13:00:14 | 2022-08-17T00:37:55 | 2022-04-21T17:04:14 |
NONE
| null | null | null |
## Describe the bug
Unable to download `Wikepedia` dataset, 20220301.en version
## Steps to reproduce the bug
```python
!pip install apache_beam mwparserfromhell
dataset_wikipedia = load_dataset("wikipedia", "20220301.en")
```
## Actual results
```
ValueError: BuilderConfig 20220301.en not found.
Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
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| 1,199,794,750 |
I_kwDODunzps5Hg2o-
| 4,142 |
Add ObjectFolder 2.0 dataset
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"Datasets are not tracked in this repository anymore."
] | 2022-04-11T10:57:51 | 2022-10-05T10:30:49 | null |
MEMBER
| null | null | null |
## Adding a Dataset
- **Name:** ObjectFolder 2.0
- **Description:** ObjectFolder 2.0 is a dataset of 1,000 objects in the form of implicit representations. It contains 1,000 Object Files each containing the complete multisensory profile for an object instance.
- **Paper:** [*link to the dataset paper if available*](https://arxiv.org/abs/2204.02389)
- **Data:** https://github.com/rhgao/ObjectFolder
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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I_kwDODunzps5HgJwF
| 4,141 |
Why is the dataset not visible under the dataset preview section?
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[] | 2022-04-11T08:36:42 | 2022-04-11T18:55:32 | 2022-04-11T17:09:49 |
NONE
| null | null | null |
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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Error loading arxiv data set
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[
"Hi! I think this error may be related to using an older version of the library. I was able to load the dataset without any issues using the latest version of `datasets`. Can you upgrade to the latest version of `datasets` and try again? :)",
"Hi! As @stevhliu suggested, to fix the issue, update the lib to the newest version with:\r\n```\r\npip install -U datasets\r\n```\r\nand download the dataset as follows:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset('scientific_papers', 'arxiv', download_mode=\"force_redownload\")\r\n```",
"Thanks for the quick response! It works now. The problem is that I used nlp. load_dataset instead of datasets. load_dataset."
] | 2022-04-11T07:06:34 | 2022-04-12T16:24:08 | 2022-04-12T16:24:08 |
NONE
| null | null | null |
## Describe the bug
A clear and concise description of what the bug is.
I met the error below when loading arxiv dataset via `nlp.load_dataset('scientific_papers', 'arxiv',)`.
```
Traceback (most recent call last):
File "scripts/summarization.py", line 354, in <module>
main(args)
File "scripts/summarization.py", line 306, in main
model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv')
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 522, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']
```
I then tried to ignore verification steps by `ignore_verifications=True` and there is another error.
```
Traceback (most recent call last):
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 537, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 810, in _prepare_split
for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/datasets/scientific_papers/9e4f2cfe3d8494e9f34a84ce49c3214605b4b52a3d8eb199104430d04c52cc12/scientific_papers.py", line 108, in _generate_examples
with open(path, encoding="utf-8") as f:
NotADirectoryError: [Errno 20] Not a directory: '/home/username/.cache/huggingface/datasets/downloads/c0deae7af7d9c87f25dfadf621f7126f708d7dcac6d353c7564883084a000076/arxiv-dataset/train.txt'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "scripts/summarization.py", line 354, in <module>
main(args)
File "scripts/summarization.py", line 306, in main
model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv', ignore_verifications=True)
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 539, in _download_and_prepare
raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))
OSError: Cannot find data file.
```
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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I_kwDODunzps5Hfg9u
| 4,139 |
Dataset viewer issue for Winoground
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[
"related (same dataset): https://github.com/huggingface/datasets/issues/4149. But the issue is different. Looking at it",
"I thought this issue was related to the error I was seeing, but upon consideration I'd think the dataset viewer would return a 500 (unable to create the split like me) or a 404 (unable to load split b/c it was never created) error if it was having the issue I was seeing in #4149. 401 message makes it look like dataset viewer isn't passing through the identity of the user who has signed the licensing agreement when making the request to GET [examples.jsonl](https://huggingface.co/datasets/facebook/winoground/resolve/a86a60456fbbd242e9a744199071a6bd3e7fd9de/examples.jsonl).",
"Pinging @SBrandeis, as it seems related to gated datasets and access tokens.",
"To replicate:\r\n\r\n```python\r\n>>> import datasets\r\n>>> dataset= datasets.load_dataset('facebook/winoground', name='facebook--winoground', split='train', use_auth_token=\"hf_app_...\", streaming=True)\r\n>>> next(iter(dataset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 497, in __iter__\r\n for key, example in self._iter():\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 494, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 87, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 439, in wrapper\r\n for key, table in generate_tables_fn(**kwargs):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py\", line 85, in _generate_tables\r\n for file_idx, file in enumerate(files):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py\", line 679, in __iter__\r\n yield from self.generator(*self.args, **self.kwargs)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py\", line 731, in _iter_from_urlpaths\r\n for dirpath, _, filenames in xwalk(urlpath, use_auth_token=use_auth_token):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py\", line 623, in xwalk\r\n for dirpath, dirnames, filenames in fs.walk(main_hop):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 372, in walk\r\n listing = self.ls(path, detail=True, **kwargs)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/asyn.py\", line 85, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/asyn.py\", line 65, in sync\r\n raise return_result\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/asyn.py\", line 25, in _runner\r\n result[0] = await coro\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 196, in _ls\r\n out = await self._ls_real(url, detail=detail, **kwargs)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 150, in _ls_real\r\n self._raise_not_found_for_status(r, url)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 208, in _raise_not_found_for_status\r\n response.raise_for_status()\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py\", line 1004, in raise_for_status\r\n raise ClientResponseError(\r\naiohttp.client_exceptions.ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/facebook/winoground/resolve/a86a60456fbbd242e9a744199071a6bd3e7fd9de/examples.jsonl')\r\n```\r\n\r\n*edited to fix `use_token` -> `use_auth_token`, thx @odellus*",
"~~Using your command to replicate and changing `use_token` to `use_auth_token` fixes the problem I was seeing in #4149.~~\r\nNevermind it gave me an iterator to a method returning the same 401s. Changing `use_token` to `use_auth_token` does not fix the issue.",
"After investigation with @severo , we found a potential culprit: https://github.com/huggingface/datasets/blob/3cd0a009a43f9f174056d70bfa2ca32216181926/src/datasets/utils/streaming_download_manager.py#L610-L624\r\n\r\nThe streaming manager does not seem to pass `use_auth_token` to `fsspec` when streaming and not iterating content of a zip archive\r\n\r\ncc @albertvillanova @lhoestq ",
"I was able to reproduce it on a private dataset, let me work on a fix",
"Hey @lhoestq, Thanks for working on a fix! Any plans to merge #4173 into master? ",
"Thanks for the heads up, I still need to fix some tests that are failing in the CI before merging ;)",
"The fix has been merged, we'll do a new release soon, and update the dataset viewer",
"Fixed, thanks!\r\n<img width=\"1119\" alt=\"Capture d’écran 2022-06-21 à 18 41 09\" src=\"https://user-images.githubusercontent.com/1676121/174853571-afb0749c-4178-4c89-ab40-bb162a449788.png\">\r\n"
] | 2022-04-11T06:11:41 | 2022-06-21T16:43:58 | 2022-06-21T16:43:58 |
NONE
| null | null | null |
## Dataset viewer issue for 'Winoground'
**Link:** [*link to the dataset viewer page*](https://huggingface.co/datasets/facebook/winoground/viewer/facebook--winoground/train)
*short description of the issue*
Getting 401, message='Unauthorized'
The dataset is subject to authorization, but I can access the files from the interface, so I assume I'm granted to access it. I'd assume the permission somehow doesn't propagate to the dataset viewer tool.
Am I the one who added this dataset ? No
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Incorrect Russian filenames encoding after extraction by datasets.DownloadManager.download_and_extract()
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[
"To reproduce:\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.get_dataset_split_names('MalakhovIlya/RuREBus', config_name='raw_txt')\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 280, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/MalakhovIlya--RuREBus/21046f5f1a0cf91187d68c30918d78d934ec7113ec435e146776d4f28f12c4ed/RuREBus.py\", line 101, in _split_generators\r\n decode_file_names(folder)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/MalakhovIlya--RuREBus/21046f5f1a0cf91187d68c30918d78d934ec7113ec435e146776d4f28f12c4ed/RuREBus.py\", line 26, in decode_file_names\r\n for root, dirs, files in os.walk(folder, topdown=False):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/streaming.py\", line 66, in wrapper\r\n return function(*args, use_auth_token=use_auth_token, **kwargs)\r\nTypeError: xwalk() got an unexpected keyword argument 'topdown'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 323, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 285, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nIt's not related to the dataset viewer. Maybe @albertvillanova or @lhoestq could help more on this issue.",
"Hi! This issue stems from the fact that `xwalk`, which is a streamable version of `os.walk`, doesn't support the `topdown` param due to `fsspec`'s `walk` also not supporting it, so fixing this issue could be tricky. \r\n\r\n@MalakhovIlyaPavlovich You can avoid the error by tweaking your data processing and not using this param. (and `Path.rename`, which also cannot be streamed) ",
"@mariosasko thank you for your reply. I couldn't reproduce error showed by @severo either on Ubuntu 20.04.3 LTS, Windows 10 and Google Colab environments. But trying to avoid using os.walk(topdown=False) and Path.rename(), In _split_generators I replaced\r\n```\r\ndef decode_file_names(folder):\r\n for root, dirs, files in os.walk(folder, topdown=False):\r\n root = Path(root)\r\n for file in files:\r\n old_name = root / Path(file)\r\n new_name = root / Path(\r\n file.encode('cp437').decode('cp866'))\r\n old_name.rename(new_name)\r\n for dir in dirs:\r\n old_name = root / Path(dir)\r\n new_name = root / Path(dir.encode('cp437').decode('cp866'))\r\n old_name.rename(new_name)\r\n\r\nfolder = dl_manager.download_and_extract(self._RAW_TXT_URLS)['raw_txt']\r\ndecode_file_names(folder)\r\n```\r\nby\r\n```\r\ndef extract(zip_file_path):\r\n p = Path(zip_file_path)\r\n dest_dir = str(p.parent / 'extracted' / p.stem)\r\n os.makedirs(dest_dir, exist_ok=True)\r\n with zipfile.ZipFile(zip_file_path) as archive:\r\n for file_info in tqdm(archive.infolist(), desc='Extracting'):\r\n filename = file_info.filename.encode('cp437').decode('cp866')\r\n target = os.path.join(dest_dir, *filename.split('/'))\r\n os.makedirs(os.path.dirname(target), exist_ok=True)\r\n if not file_info.is_dir():\r\n with archive.open(file_info) as source, open(target, 'wb') as dest:\r\n shutil.copyfileobj(source, dest)\r\n return dest_dir\r\n\r\nzip_file = dl_manager.download(self._RAW_TXT_URLS)['raw_txt']\r\nif not is_url(zip_file):\r\n folder = extract(zip_file)\r\nelse:\r\n folder = None\r\n```\r\nand now everything works well except data viewer for \"raw_txt\" subset: dataset preview on hub shows \"No data.\". As far as I understand dl_manager.download returns original URL when we are calling datasets.get_dataset_split_names and my suspicions are that dataset viewer can do smth similar. I couldn't find information about how it works. I would be very grateful, if you could tell me how to fix this)",
"This is what I get when I try to stream the `raw_txt` subset:\r\n```python\r\n>>> dset = load_dataset(\"MalakhovIlya/RuREBus\", \"raw_txt\", split=\"raw_txt\", streaming=True)\r\n>>> next(iter(dset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nStopIteration\r\n```\r\nSo there is a bug in your script.",
"streaming=True helped me to find solution. I fixed\r\n```\r\ndef extract(zip_file_path):\r\n p = Path(zip_file_path)\r\n dest_dir = str(p.parent / 'extracted' / p.stem)\r\n os.makedirs(dest_dir, exist_ok=True)\r\n with zipfile.ZipFile(zip_file_path) as archive:\r\n for file_info in tqdm(archive.infolist(), desc='Extracting'):\r\n filename = file_info.filename.encode('cp437').decode('cp866')\r\n target = os.path.join(dest_dir, *filename.split('/'))\r\n os.makedirs(os.path.dirname(target), exist_ok=True)\r\n if not file_info.is_dir():\r\n with archive.open(file_info) as source, open(target, 'wb') as dest:\r\n shutil.copyfileobj(source, dest)\r\n return dest_dir\r\n\r\nzip_file = dl_manager.download(self._RAW_TXT_URLS)['raw_txt']\r\nfolder = extract(zip_file)\r\n```\r\nby \r\n```\r\nfolder = dl_manager.download_and_extract(self._RAW_TXT_URLS)['raw_txt']\r\npath = os.path.join(folder, 'MED_txt/unparsed_txt')\r\nfor root, dirs, files in os.walk(path):\r\n decoded_root_name = Path(root).name.encode('cp437').decode('cp866')\r\n```\r\n@mariosasko thank you for your help :)"
] | 2022-04-11T02:07:13 | 2022-04-19T03:15:46 | 2022-04-16T15:46:29 |
NONE
| null | null | null |
## Dataset viewer issue for 'MalakhovIlya/RuREBus'
**Link:** https://huggingface.co/datasets/MalakhovIlya/RuREBus
**Description**
Using os.walk(topdown=False) in DatasetBuilder causes following error:
Status code: 400
Exception: TypeError
Message: xwalk() got an unexpected keyword argument 'topdown'
Couldn't find where "xwalk" come from. How can I fix this?
Am I the one who added this dataset ? Yes
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ELI5 supporting documents
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"Hi ! Please post your question on the [forum](https://discuss.huggingface.co/), more people will be able to help you there ;)"
] | 2022-04-08T23:36:27 | 2022-04-13T13:52:46 | null |
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if i am using dense search to create supporting documents for eli5 how much time it will take bcz i read somewhere that it takes about 18 hrs??
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HANS dataset preview broken
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"The dataset cannot be loaded, be it in normal or streaming mode.\r\n\r\n```python\r\n>>> import datasets\r\n>>> dataset=datasets.load_dataset(\"hans\", split=\"train\", streaming=True)\r\n>>> next(iter(dataset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 497, in __iter__\r\n for key, example in self._iter():\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 494, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 87, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/hans/1bbcb735c482acd54f2e118074b59cfd2bf5f7a5a285d4d540d1e632216672ac/hans.py\", line 121, in _generate_examples\r\n for idx, line in enumerate(open(filepath, \"rb\")):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 1595, in __next__\r\n out = self.readline()\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 1592, in readline\r\n return self.readuntil(b\"\\n\")\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 1581, in readuntil\r\n self.seek(start + found + len(char))\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 676, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n>>> dataset=datasets.load_dataset(\"hans\", split=\"train\", streaming=False)\r\nDownloading and preparing dataset hans/plain_text (download: 29.51 MiB, generated: 30.34 MiB, post-processed: Unknown size, total: 59.85 MiB) to /home/slesage/.cache/huggingface/datasets/hans/plain_text/1.0.0/1bbcb735c482acd54f2e118074b59cfd2bf5f7a5a285d4d540d1e632216672ac...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1687, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1104, in _download_and_prepare\r\n super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 694, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1087, in _prepare_split\r\n for key, record in logging.tqdm(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/tqdm/std.py\", line 1180, in __iter__\r\n for obj in iterable:\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/hans/1bbcb735c482acd54f2e118074b59cfd2bf5f7a5a285d4d540d1e632216672ac/hans.py\", line 121, in _generate_examples\r\n for idx, line in enumerate(open(filepath, \"rb\")):\r\nValueError: readline of closed file\r\n```\r\n\r\n",
"Hi! I've opened a PR that should make this dataset stremable. You can test it as follows:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset(\"hans\", split=\"train\", streaming=True, revision=\"49decd29839c792ecc24ac88f861cbdec30c1c40\")\r\n```\r\n\r\n@severo The current script doesn't throw an error in normal mode (only in streaming mode) on my local machine or in Colab. Can you update your installation of `datasets` and see if that fixes the issue?",
"Thanks for this. It works well, thanks! The dataset viewer is using https://github.com/huggingface/datasets/releases/tag/2.0.0, I'm eager to upgrade to 2.0.1 😉"
] | 2022-04-08T21:06:15 | 2022-04-13T11:57:34 | 2022-04-13T11:57:34 |
NONE
| null | null | null |
## Dataset viewer issue for '*hans*'
**Link:** [https://huggingface.co/datasets/hans](https://huggingface.co/datasets/hans)
HANS dataset preview is broken with error 400
Am I the one who added this dataset ? No
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dataset metadata for reproducibility
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"+1 on this idea. This could be powerful for helping better track datasets used for model training and help with automatic model card creation. \r\n\r\nOne possible way of doing this would be to store some/most/all the arguments passed to `load_dataset` if a hub id is passed. i.e. store the Hub ID, configuration, etc. \r\n\r\ncc @tomaarsen"
] | 2022-04-08T14:17:28 | 2023-09-29T09:23:56 | null |
NONE
| null | null | null |
When pulling a dataset from the hub, it would be useful to have some metadata about the specific dataset and version that is used. The metadata could then be passed to the `Trainer` which could then be saved to a model card. This is useful for people who run many experiments on different versions (commits/branches) of the same dataset.
The dataset could have a list of “source datasets” metadata and ignore what happens to them before arriving in the Trainer (i.e. ignore mapping, filtering, etc.).
Here is a basic representation (made by @lhoestq )
```python
>>> from datasets import load_dataset
>>>
>>> my_dataset = load_dataset(...)["train"]
>>> my_dataset = my_dataset.map(...)
>>>
>>> my_dataset.sources
[HFHubDataset(repo_id=..., revision=..., arguments={...})]
```
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dataset viewer issue for common_voice
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"Yes, it's a known issue, and we expect to fix it soon.",
"Fixed.\r\n\r\n<img width=\"1393\" alt=\"Capture d’écran 2022-04-25 à 15 42 05\" src=\"https://user-images.githubusercontent.com/1676121/165101176-d729d85b-efff-45a8-bad1-b69223edba5f.png\">\r\n"
] | 2022-04-07T23:34:28 | 2022-04-25T13:42:17 | 2022-04-25T13:42:16 |
NONE
| null | null | null |
## Dataset viewer issue for 'common_voice'
**Link:** https://huggingface.co/datasets/common_voice
Server Error
Status code: 400
Exception: TypeError
Message: __init__() got an unexpected keyword argument 'audio_column'
Am I the one who added this dataset ? No
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Image decoding often fails when transforming Image datasets
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[
"A quick hack I have found is that we can call the image first before running the transforms and it makes sure the image is decoded before being passed on.\r\n\r\nFor this I just needed to add `example['img'] = example['img']` to the top of my `generate_flipped_data` function, defined above, so that image decode in invoked.\r\n\r\nAfter this minor change this function works:\r\n```python\r\ndef generate_flipped_data(example, p=0.5):\r\n \"\"\"\r\n A Dataset mapping functions that transforms some of the image up-side-down.\r\n If the probability value (p) is 0.5 approximately half the images will be flipped upside-down\r\n Args:\r\n example: An example from the dataset containing a Python dictionary with \"img\" and \"is_flipped\" key-value pair\r\n p: probability of flipping the image up-side-down, Default 0.5\r\n\r\n Returns:\r\n example: A Dataset object\r\n\r\n \"\"\"\r\n example['img'] = example['img'] # <<< This is the only change\r\n if rng.random() > p: # the flip the image and set is_flipped column to 1\r\n example['img'] = example['img'].transpose(\r\n 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM)\r\n example['is_flipped'] = 1\r\n\r\n return example\r\n```",
"Hi @RafayAK, thanks for reporting.\r\n\r\nCurrent implementation of the Image feature performs the decoding only if the \"img\" field is accessed by the mapped function.\r\n\r\nIn your original `generate_flipped_data` function:\r\n- it only accesses the \"img\" field (and thus performs decoding) if `rng.random() > p`;\r\n- on the other hand, for the cases where `rng.random() <= p`, the \"img\" field is not accessed and thus no decoding is performed for those examples\r\n\r\nBy adding the code line `example['img'] = example['img']`, you make sure the \"img\" field is accessed in all cases, and the decoding is done for all examples.\r\n\r\nAlso note that there is a little bug in your implementation: `p` is not the probability of flipping, but the probability of not-flipping; the larger is `p`, the smaller is the probability of flipping.\r\n\r\nSome refactoring (fixing also `p`):\r\n```python\r\ndef generate_flipped_data(example, p=0.5):\r\n \"\"\"\r\n A Dataset mapping functions that transforms some of the image up-side-down.\r\n If the probability value (p) is 0.5 approximately half the images will be flipped upside-down.\r\n\r\n Args:\r\n example: An example from the dataset containing a Python dictionary with \"img\" and \"is_flipped\" key-value pair\r\n p: probability of flipping the image up-side-down, Default 0.5\r\n\r\n Returns:\r\n example: A Dataset object\r\n\r\n \"\"\"\r\n do_flip = rng.random() < p # Note the \"<\" sign here instead of \">\"\r\n example['img'] = example['img'].transpose(1) if do_flip else example['img'] # Note \"img\" is always accessed\r\n example['is_flipped'] = 1 if do_flip else 0\r\n return example",
"@albertvillanova Thanks for letting me know this is intended behavior. The docs are severely lacking on this, if I hadn't posted this here I would have never found out how I'm actually supposed to modify images in a Dataset object.",
"@albertvillanova Secondly if you check the error message it shows that around 1999 images were successfully created, I'm pretty sure some of them were also flipped during the process. Back to my main contention, sometimes the decoding takes place other times it fails. \r\n\r\nI suppose to run `map` on any dataset all the examples should be invoked even if on some of them we end up doing nothing, is that right?",
"Hi @RafayAK! I've opened a PR with the fix, which adds a fallback to reattempt casting to PyArrow format with a more robust (but more expensive) procedure if the first attempt fails. Feel free to test it by installing `datasets` from the PR branch with the following command:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@fix-4124\r\n```",
"@mariosasko I'll try this right away and report back.",
"@mariosasko Thanks a lot for looking into this, now the `map` function at least behaves as one would expect a function to behave. \r\n\r\nLooking forward to exploring Hugging Face more and even contributing 😃.\r\n\r\n```bash\r\n $ conda list | grep datasets\r\ndatasets 2.0.1.dev0 pypi_0 pypi\r\n\r\n```\r\n\r\n```python\r\ndef preprocess_data(dataset):\r\n \"\"\"\r\n Helper funtion to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and\r\n add is_flipped column\r\n Args:\r\n dataset: HuggingFace CIFAR-100 Dataset Object\r\n\r\n Returns:\r\n new_dataset: A Dataset object with \"img\" and \"is_flipped\" columns only\r\n\r\n \"\"\"\r\n # remove fine_label and coarse_label columns\r\n new_dataset = dataset.remove_columns(['fine_label', 'coarse_label'])\r\n # add the column for is_flipped\r\n new_dataset = new_dataset.add_column(name=\"is_flipped\", column=np.zeros((len(new_dataset)), dtype=np.uint8))\r\n\r\n return new_dataset\r\n\r\n\r\ndef generate_flipped_data(example, p=0.5):\r\n \"\"\"\r\n A Dataset mapping functions that transforms some of the image up-side-down.\r\n If the probability value (p) is 0.5 approximately half the images will be flipped upside-down\r\n Args:\r\n example: An example from the dataset containing a Python dictionary with \"img\" and \"is_flipped\" key-value pair\r\n p: probability of flipping the image up-side-down, Default 0.5\r\n\r\n Returns:\r\n example: A Dataset object\r\n\r\n \"\"\"\r\n # example['img'] = example['img']\r\n if rng.random() > p: # the flip the image and set is_flipped column to 1\r\n example['img'] = example['img'].transpose(\r\n 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM)\r\n example['is_flipped'] = 1\r\n\r\n return example\r\n\r\nmy_test = preprocess_data(test_dataset)\r\nmy_test = my_test.map(generate_flipped_data)\r\n```\r\n\r\nThe output now show the function was applied successfully:\r\n``` bash\r\n/home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py\r\nDownloading builder script: 5.61kB [00:00, 3.16MB/s] \r\nDownloading metadata: 4.21kB [00:00, 2.56MB/s] \r\nReusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)\r\nReusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)\r\n100%|██████████| 10000/10000 [00:01<00:00, 5149.15ex/s]\r\n```\r\n"
] | 2022-04-07T19:17:25 | 2022-04-13T14:01:16 | 2022-04-13T14:01:16 |
NONE
| null | null | null |
## Describe the bug
When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors.
Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes:
```
[{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf ....
```
## Steps to reproduce the bug
```python
from datasets import load_dataset, Dataset
import numpy as np
# seeded NumPy random number generator for reprodducinble results.
rng = np.random.default_rng(seed=0)
test_dataset = load_dataset('cifar100', split="test")
def preprocess_data(dataset):
"""
Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and
add is_flipped column
Args:
dataset: HuggingFace CIFAR-100 Dataset Object
Returns:
new_dataset: A Dataset object with "img" and "is_flipped" columns only
"""
# remove fine_label and coarse_label columns
new_dataset = dataset.remove_columns(['fine_label', 'coarse_label'])
# add the column for is_flipped
new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8))
return new_dataset
def generate_flipped_data(example, p=0.5):
"""
A Dataset mapping function that transforms some of the images up-side-down.
If the probability value (p) is 0.5 approximately half the images will be flipped upside-down
Args:
example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair
p: the probability of flipping the image up-side-down, Default 0.5
Returns:
example: A Dataset object
"""
# example['img'] = example['img']
if rng.random() > p: # the flip the image and set is_flipped column to 1
example['img'] = example['img'].transpose(
1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM)
example['is_flipped'] = 1
return example
my_test = preprocess_data(test_dataset)
my_test = my_test.map(generate_flipped_data)
```
## Expected results
The dataset should be transformed without problems.
## Actual results
```
/home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py
Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)
Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)
20%|█▉ | 1999/10000 [00:00<00:01, 5560.44ex/s]
Traceback (most recent call last):
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single
writer.write(example)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write
self.write_examples_on_file()
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__
out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
File "pyarrow/array.pxi", line 316, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module>
my_test = my_test.map(generate_flipped_data)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map
return self._map_single(
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single
writer.finalize()
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize
self.write_examples_on_file()
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__
out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
File "pyarrow/array.pxi", line 316, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type
Process finished with exit code 1
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux(Fedora 35)
- Python version: 3.10
- PyArrow version: 7.0.0
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I_kwDODunzps5HTx6Y
| 4,123 |
Building C4 takes forever
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[
"Hi @StellaAthena, thanks for reporting.\r\n\r\nPlease note, that our `datasets` library performs several operations in order to load a dataset, among them:\r\n- it downloads all the required files: for C4 \"en\", 378.69 GB of JSON GZIPped files\r\n- it parses their content to generate the dataset\r\n- it caches the dataset in an Arrow file: for C4 \"en\", this file size is 1.87 TB\r\n- it memory-maps the Arrow file\r\n\r\nIf it suits your use case, you might load this dataset in streaming mode:\r\n- no Arrow file is generated\r\n- you can iterate over elements immediately (no need to wait to download all the entire files)\r\n\r\n```python\r\nIn [45]: from datasets import load_dataset\r\n ...: ds = load_dataset(\"c4\", \"en\", split=\"train\", streaming=True)\r\n ...: for item in ds:\r\n ...: print(item)\r\n ...: break\r\n ...: \r\n{'text': 'Beginners BBQ Class Taking Place in Missoula!\\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z', 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/'}\r\n```\r\nI hope this is useful for your use case."
] | 2022-04-07T17:41:30 | 2023-06-26T22:01:29 | 2023-06-26T22:01:29 |
NONE
| null | null | null |
## Describe the bug
C4-en is a 300 GB dataset. However, when I try to download it through the hub it takes over _six hours_ to generate the train/test split from the downloaded files. This is an absurd amount of time and an unnecessary waste of resources.
## Steps to reproduce the bug
```python
c4 = datasets.load("c4", "en")
```
## Expected results
I would like to be able to download pre-split data.
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.13.0-35-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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medical_dialog zh has very slow _generate_examples
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[
"Hi @nbroad1881, thanks for reporting.\r\n\r\nLet me have a look to try to improve its performance. ",
"Thanks @nbroad1881 for reporting! I don't recall it taking so long. I will also have a look at this. \r\n@albertvillanova please let me know if I am doing something unnecessary or time consuming.",
"Hi @nbroad1881 and @vrindaprabhu,\r\n\r\nAs a workaround for the performance of the parsing of the raw data files (this could be addressed in a subsequent PR), I have found that there are also processed data files, that do not require parsing. I have added these as new configurations `processed.en` and `processed.zh`:\r\n```python\r\nds = load_dataset(\"medical_dialog\", \"processed.zh\")\r\n```"
] | 2022-04-07T14:00:51 | 2022-04-08T16:20:51 | 2022-04-08T16:20:51 |
NONE
| null | null | null |
## Describe the bug
After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours.
## Steps to reproduce the bug
The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud.
```python
file_ids = [
"1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E",
"1tt7weAT1SZknzRFyLXOT2fizceUUVRXX",
"1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc",
"1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J",
"1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu",
"1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP",
"1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c",
"1pA3bCFA5nZDhsQutqsJcH3d712giFb0S",
"1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU",
"1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD",
"1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH",
]
for i in file_ids:
url = f"https://drive.google.com/uc?id={i}"
!gdown $url
from datasets import load_dataset
ds = load_dataset("medical_dialog", "zh", data_dir="./")
```
## Expected results
Faster load time
## Actual results
`Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]`
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
@vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten
|
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| 1,196,000,018 |
I_kwDODunzps5HSYMS
| 4,121 |
datasets.load_metric can not load a local metirc
|
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[
"Hello, could you tell me how this issue can be fixed? I'm coming across the same issue."
] | 2022-04-07T12:48:56 | 2023-01-18T14:30:46 | 2022-04-07T13:53:27 |
NONE
| null | null | null |
## Describe the bug
No matter how I hard try to tell load_metric that I want to load a local metric file, it still continues to fetch things on the Internet. And unfortunately it says 'ConnectionError: Couldn't reach'. However I can download this file without connectionerror and tell load_metric its local directory. And it comes back where it begins...
## Steps to reproduce the bug
```python
metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py
metric = load_metric(path='bleu')
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.12.1/metrics/bleu/bleu.py
metric = load_metric(path='./blue/bleu.py')
ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py
```
## Expected results
I do read the docs [here](https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_metric). There are no other parameters that help function to distinguish from local and online file but path. As what I code above, it should load from local.
## Actual results
> metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
> ~\AppData\Local\Temp\ipykernel_19636\1855752034.py in <module>
----> 1 metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
817 if data_files is None and data_dir is not None:
818 data_files = os.path.join(data_dir, "**")
--> 819
820 self.name = name
821 self.revision = revision
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)
639 self,
640 path: str,
--> 641 download_config: Optional[DownloadConfig] = None,
642 download_mode: Optional[DownloadMode] = None,
643 dynamic_modules_path: Optional[str] = None,
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
297 token = hf_api.HfFolder.get_token()
298 if token:
--> 299 headers["authorization"] = f"Bearer {token}"
300 return headers
301
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
604 def _resumable_file_manager():
605 with open(incomplete_path, "a+b") as f:
--> 606 yield f
607
608 temp_file_manager = _resumable_file_manager
ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Windows-10-10.0.22000-SP0
- Python version: 3.7.13
- PyArrow version: 7.0.0
- Pandas version: 1.3.4
Any advice would be appreciated.
|
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| 1,195,887,430 |
I_kwDODunzps5HR8tG
| 4,120 |
Representing dictionaries (json) objects as features
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[] | 2022-04-07T11:07:41 | 2022-04-07T11:07:41 | null |
CONTRIBUTOR
| null | null | null |
In the process of adding a new dataset to the hub, I stumbled upon the inability to represent dictionaries that contain different key names, unknown in advance (and may differ between samples), original asked in the [forum](https://discuss.huggingface.co/t/representing-nested-dictionary-with-different-keys/16442).
For instance:
```
sample1 = {"nps": {
"a": {"id": 0, "text": "text1"},
"b": {"id": 1, "text": "text2"},
}}
sample2 = {"nps": {
"a": {"id": 0, "text": "text1"},
"b": {"id": 1, "text": "text2"},
"c": {"id": 2, "text": "text3"},
}}
sample3 = {"nps": {
"a": {"id": 0, "text": "text1"},
"b": {"id": 1, "text": "text2"},
"c": {"id": 2, "text": "text3"},
"d": {"id": 3, "text": "text4"},
}}
```
the `nps` field cannot be represented as a Feature while maintaining its original structure.
@lhoestq suggested to add JSON as a new feature type, which will solve this problem.
It seems like an alternative solution would be to change the original data format, which isn't an optimal solution in my case. Moreover, JSON is a common structure, that will likely to be useful in future datasets as well.
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