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81
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dccuchile/albert-base-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 14 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/gpt2 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/albert-base-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"AlbertForQuestionAnswering"
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| 3 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/typeform/distilbert-base-uncased-mnli with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/albert-base-spanish-finetuned-xnli
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 28 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/openai/whisper-tiny.en with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/albert-large-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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"AlbertForSequenceClassification"
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| 27 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: tyavika/TfBert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tyavika/TfBert-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8658
- Train End Logits Accuracy: 0.6640
- Train Start Logits Accuracy: 0.6673
- Validation Loss: 0.3282
- Validation End Logits Accuracy: 0.8793
- Validation Start Logits Accuracy: 0.8857
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 13162, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.8658 | 0.6640 | 0.6673 | 0.3282 | 0.8793 | 0.8857 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.12.0
- Tokenizers 0.13.2
|
dccuchile/albert-large-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 25 | null |
---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- XaviArmengol/autotrain-data-titanic
co2_eq_emissions:
emissions: 0.8485943767026511
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 54879128171
- CO2 Emissions (in grams): 0.8486
## Validation Metrics
- Loss: 0.344
- Accuracy: 0.877
- Precision: 0.862
- Recall: 0.812
- AUC: 0.913
- F1: 0.836
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
```
|
dccuchile/albert-tiny-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 32 | null |
---
datasets:
- OpenAssistant/oasst1
license:
- other
---
Model reference: https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor/
These ggml model quantizations are modified weights (through the nature of quantization) shared for scientific research and other non-commercial purposes.
The GGML converted FP16 model is also made available in case you wish to perform your own quantization.
The files were archived into parts using 7zip https://www.7-zip.org/
Please see and agree to the LLAMA license below prior to usage.
---
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If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Meta to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Meta regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Meta regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Meta.
---
|
dccuchile/albert-tiny-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"AlbertForTokenClassification"
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| 8 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuned-Sentiment-classfication-ROBERTA-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-Sentiment-classfication-ROBERTA-model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6328
- Rmse: 0.6374
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6513 | 4.0 | 500 | 0.6328 | 0.6374 |
| 0.2245 | 8.0 | 1000 | 0.8692 | 0.6295 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/albert-tiny-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"AlbertForTokenClassification"
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| 5 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12')
model = AutoModel.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7200 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
dccuchile/albert-tiny-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 7 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 261.95 +/- 29.48
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-tiny-spanish-finetuned-xnli
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 31 | null |
---
license: mit
language:
- en
library_name: transformers
inference: false
datasets:
- databricks/databricks-dolly-15k
duplicated_from: databricks/dolly-v2-3b
---
# dolly-v2-3b Model Card
# Fork of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b)
Original README copied below:
## Summary
Databricks’ `dolly-v2-3b`, an instruction-following large language model trained on the Databricks machine learning platform
that is licensed for commercial use. Based on `pythia-2.8b`, Dolly is trained on ~15k instruction/response fine tuning records
[`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated
by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation,
information extraction, open QA and summarization. `dolly-v2-3b` is not a state-of-the-art model, but does exhibit surprisingly
high quality instruction following behavior not characteristic of the foundation model on which it is based.
Dolly v2 is also available in these larger models sizes:
* [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b`
* [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b`
Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on
running inference for various GPU configurations.
**Owner**: Databricks, Inc.
## Model Overview
`dolly-v2-3b` is a 2.8 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from
[EleutherAI’s](https://www.eleuther.ai/) [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) and fine-tuned
on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed.
In a Databricks notebook you could run:
```python
%pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"
```
The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline`
found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required.
Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality.
It is also fine to remove it if there is sufficient memory.
```python
import torch
from transformers import pipeline
generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
```
You can then use the pipeline to answer instructions:
```python
res = generate_text("Explain to me the difference between nuclear fission and fusion.")
print(res[0]["generated_text"])
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py),
store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from instruct_pipeline import InstructionTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16)
generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
```
### LangChain Usage
To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned
and the default for the pipeline is to only return the new text.
```python
import torch
from transformers import pipeline
generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16,
trust_remote_code=True, device_map="auto", return_full_text=True)
```
You can create a prompt that either has only an instruction or has an instruction with context:
```python
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline
# template for an instrution with no input
prompt = PromptTemplate(
input_variables=["instruction"],
template="{instruction}")
# template for an instruction with input
prompt_with_context = PromptTemplate(
input_variables=["instruction", "context"],
template="{instruction}\n\nInput:\n{context}")
hf_pipeline = HuggingFacePipeline(pipeline=generate_text)
llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
```
Example predicting using a simple instruction:
```python
print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip())
```
Example predicting using an instruction with context:
```python
context = """George Washington (February 22, 1732[b] – December 14, 1799) was an American military officer, statesman,
and Founding Father who served as the first president of the United States from 1789 to 1797."""
print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip())
```
## Known Limitations
### Performance Limitations
**`dolly-v2-3b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform
competitively with more modern model architectures or models subject to larger pretraining corpuses.
The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community.
In particular, `dolly-v2-3b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors,
dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.
Moreover, we find that `dolly-v2-3b` does not have some capabilities, such as well-formatted letter writing, present in the original model.
### Dataset Limitations
Like all language models, `dolly-v2-3b` reflects the content and limitations of its training corpuses.
- **The Pile**: GPT-J’s pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,
it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly
in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit
associations.
- **`databricks-dolly-15k`**: The training data on which `dolly-v2-3b` is instruction tuned represents natural language instructions generated
by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages
for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or
personally identifying information about non-public figures, but it may contain typos and factual errors.
The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects
the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large.
Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that
maximize the potential of all individuals and organizations.
### Benchmark Metrics
Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness);
model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-3b` is not state of the art.
It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters.
| model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean |
| --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------|
| EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 |
| EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 |
| databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 |
| EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 |
| EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 |
| databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 |
| databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 |
| databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 |
| EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 |
# Happy Hacking!
|
dccuchile/albert-xlarge-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
}
| 26 | null |
---
tags:
- LunarLander-v2
- ddpg
model-index:
- name: beerlincur
results:
- task:
type: LunarLander-v2
name: LunarLander-v2
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 245.87 +/- 73.52
name: mean_reward
verified: false
---
# DESCRIPTION
|
dccuchile/albert-xlarge-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | null |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- speedppc/autotrain-data-beeline-human-v3
co2_eq_emissions:
emissions: 0.21671004361111368
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 54880128179
- CO2 Emissions (in grams): 0.2167
## Validation Metrics
- Loss: 0.083
- Accuracy: 0.970
- Precision: 0.988
- Recall: 0.978
- AUC: 0.993
- F1: 0.983
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/speedppc/autotrain-beeline-human-v3-54880128179
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("speedppc/autotrain-beeline-human-v3-54880128179", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("speedppc/autotrain-beeline-human-v3-54880128179", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
dccuchile/albert-xlarge-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
| 24 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: canine_deasciifier_0205
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# canine_deasciifier_0205
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0004
- Precision: 0.9983
- Recall: 0.9991
- F1: 0.9987
- Accuracy: 0.9999
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 244 | 0.0462 | 0.8117 | 0.8638 | 0.8370 | 0.9829 |
| No log | 2.0 | 488 | 0.0335 | 0.8432 | 0.9286 | 0.8838 | 0.9878 |
| 0.1144 | 3.0 | 732 | 0.0173 | 0.9182 | 0.9569 | 0.9371 | 0.9939 |
| 0.1144 | 4.0 | 976 | 0.0101 | 0.9567 | 0.9705 | 0.9636 | 0.9966 |
| 0.0241 | 5.0 | 1220 | 0.0067 | 0.9716 | 0.9786 | 0.9751 | 0.9978 |
| 0.0241 | 6.0 | 1464 | 0.0049 | 0.9784 | 0.9846 | 0.9815 | 0.9984 |
| 0.0137 | 7.0 | 1708 | 0.0039 | 0.9830 | 0.9886 | 0.9858 | 0.9987 |
| 0.0137 | 8.0 | 1952 | 0.0030 | 0.9870 | 0.9911 | 0.9891 | 0.9990 |
| 0.0088 | 9.0 | 2196 | 0.0024 | 0.9893 | 0.9933 | 0.9913 | 0.9992 |
| 0.0088 | 10.0 | 2440 | 0.0019 | 0.9916 | 0.9947 | 0.9932 | 0.9994 |
| 0.0061 | 11.0 | 2684 | 0.0013 | 0.9941 | 0.9962 | 0.9952 | 0.9996 |
| 0.0061 | 12.0 | 2928 | 0.0010 | 0.9955 | 0.9971 | 0.9963 | 0.9997 |
| 0.0045 | 13.0 | 3172 | 0.0010 | 0.9952 | 0.9973 | 0.9963 | 0.9997 |
| 0.0045 | 14.0 | 3416 | 0.0008 | 0.9966 | 0.9980 | 0.9973 | 0.9998 |
| 0.0033 | 15.0 | 3660 | 0.0006 | 0.9973 | 0.9982 | 0.9978 | 0.9998 |
| 0.0033 | 16.0 | 3904 | 0.0006 | 0.9975 | 0.9986 | 0.9980 | 0.9998 |
| 0.0027 | 17.0 | 4148 | 0.0004 | 0.9982 | 0.9988 | 0.9985 | 0.9999 |
| 0.0027 | 18.0 | 4392 | 0.0004 | 0.9982 | 0.9990 | 0.9986 | 0.9999 |
| 0.0023 | 19.0 | 4636 | 0.0004 | 0.9984 | 0.9991 | 0.9988 | 0.9999 |
| 0.0023 | 20.0 | 4880 | 0.0004 | 0.9983 | 0.9991 | 0.9987 | 0.9999 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/albert-xlarge-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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},
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| 3 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/nlpconnect/vit-gpt2-image-captioning with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/albert-base-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
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"prefix": null
},
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},
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}
}
}
| 586 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/google/vit-base-patch16-224 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
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},
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},
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},
"translation_en_to_fr": {
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}
}
| 81 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/facebook/detr-resnet-50-panoptic with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
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},
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},
"translation_en_to_fr": {
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}
| 1 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/openai/clip-vit-base-patch32 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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}
}
| 5 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gptneo-txt2ARXMLv1.3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gptneo-txt2ARXMLv1.3
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 345 | 0.6273 |
| 1.2323 | 2.0 | 690 | 0.4253 |
| 0.3605 | 3.0 | 1035 | 0.2673 |
| 0.3605 | 4.0 | 1380 | 0.1702 |
| 0.1588 | 5.0 | 1725 | 0.1347 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
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}
}
| 28 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/facebook/detr-resnet-50 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 39 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
}
}
| 29 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.76 +/- 13.05
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-1
|
[
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
}
}
| 7 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/distilbert-base-uncased-distilled-squad with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
CennetOguz/distilbert-base-uncased-finetuned-recipe
|
[
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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},
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}
}
| 2 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gptneo-txt2ARXMLv1.3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gptneo-txt2ARXMLv1.3
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5190
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2367 | 0.99 | 32 | 2.1758 |
| 0.9974 | 1.98 | 64 | 0.9551 |
| 0.664 | 2.98 | 96 | 0.7031 |
| 0.5799 | 4.0 | 129 | 0.5972 |
| 0.4837 | 4.96 | 160 | 0.5190 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chae/botman
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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}
| 5 | null |
---
library_name: "transformers.js"
---
https://huggingface.co/facebook/bart-large-cnn with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
Chaima/TunBerto
|
[] | null |
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}
| 0 | null |
# Introduction
This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch.
Some of the code here will be included in upstream Pytorch eventually.
The intent of Apex is to make up-to-date utilities available to users as quickly as possible.
## Full API Documentation: [https://nvidia.github.io/apex](https://nvidia.github.io/apex)
## [GTC 2019](https://github.com/mcarilli/mixed_precision_references/tree/master/GTC_2019) and [Pytorch DevCon 2019](https://github.com/mcarilli/mixed_precision_references/tree/master/Pytorch_Devcon_2019) Slides
# Contents
## 1. Amp: Automatic Mixed Precision
**Deprecated. Use [PyTorch AMP](https://pytorch.org/docs/stable/amp.html)**
`apex.amp` is a tool to enable mixed precision training by changing only 3 lines of your script.
Users can easily experiment with different pure and mixed precision training modes by supplying
different flags to `amp.initialize`.
[Webinar introducing Amp](https://info.nvidia.com/webinar-mixed-precision-with-pytorch-reg-page.html)
(The flag `cast_batchnorm` has been renamed to `keep_batchnorm_fp32`).
[API Documentation](https://nvidia.github.io/apex/amp.html)
[Comprehensive Imagenet example](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
[DCGAN example coming soon...](https://github.com/NVIDIA/apex/tree/master/examples/dcgan)
[Moving to the new Amp API](https://nvidia.github.io/apex/amp.html#transition-guide-for-old-api-users) (for users of the deprecated "Amp" and "FP16_Optimizer" APIs)
## 2. Distributed Training
**`apex.parallel.DistributedDataParallel` is deprecated. Use [`torch.nn.parallel.DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html?highlight=distributeddataparallel#torch.nn.parallel.DistributedDataParallel)**
`apex.parallel.DistributedDataParallel` is a module wrapper, similar to
`torch.nn.parallel.DistributedDataParallel`. It enables convenient multiprocess distributed training,
optimized for NVIDIA's NCCL communication library.
[API Documentation](https://nvidia.github.io/apex/parallel.html)
[Python Source](https://github.com/NVIDIA/apex/tree/master/apex/parallel)
[Example/Walkthrough](https://github.com/NVIDIA/apex/tree/master/examples/simple/distributed)
The [Imagenet example](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
shows use of `apex.parallel.DistributedDataParallel` along with `apex.amp`.
### Synchronized Batch Normalization
**Deprecated. Use [`torch.nn.SyncBatchNorm`](https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html)**
`apex.parallel.SyncBatchNorm` extends `torch.nn.modules.batchnorm._BatchNorm` to
support synchronized BN.
It allreduces stats across processes during multiprocess (DistributedDataParallel) training.
Synchronous BN has been used in cases where only a small
local minibatch can fit on each GPU.
Allreduced stats increase the effective batch size for the BN layer to the
global batch size across all processes (which, technically, is the correct
formulation).
Synchronous BN has been observed to improve converged accuracy in some of our research models.
### Checkpointing
To properly save and load your `amp` training, we introduce the `amp.state_dict()`, which contains all `loss_scalers` and their corresponding unskipped steps,
as well as `amp.load_state_dict()` to restore these attributes.
In order to get bitwise accuracy, we recommend the following workflow:
```python
# Initialization
opt_level = 'O1'
model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)
# Train your model
...
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
...
# Save checkpoint
checkpoint = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'amp': amp.state_dict()
}
torch.save(checkpoint, 'amp_checkpoint.pt')
...
# Restore
model = ...
optimizer = ...
checkpoint = torch.load('amp_checkpoint.pt')
model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
amp.load_state_dict(checkpoint['amp'])
# Continue training
...
```
Note that we recommend restoring the model using the same `opt_level`. Also note that we recommend calling the `load_state_dict` methods after `amp.initialize`.
# Installation
Each [`apex.contrib`](./apex/contrib) module requires one or more install options other than `--cpp_ext` and `--cuda_ext`.
Note that contrib modules do not necessarily support stable PyTorch releases.
## Containers
NVIDIA PyTorch Containers are available on NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch.
The containers come with all the custom extensions available at the moment.
See [the NGC documentation](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html) for details such as:
- how to pull a container
- how to run a pulled container
- release notes
## From Source
To install Apex from source, we recommend using the nightly Pytorch obtainable from https://github.com/pytorch/pytorch.
The latest stable release obtainable from https://pytorch.org should also work.
### Linux
For performance and full functionality, we recommend installing Apex with
CUDA and C++ extensions via
```bash
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```
APEX also supports a Python-only build via
```bash
pip install -v --disable-pip-version-check --no-cache-dir ./
```
A Python-only build omits:
- Fused kernels required to use `apex.optimizers.FusedAdam`.
- Fused kernels required to use `apex.normalization.FusedLayerNorm` and `apex.normalization.FusedRMSNorm`.
- Fused kernels that improve the performance and numerical stability of `apex.parallel.SyncBatchNorm`.
- Fused kernels that improve the performance of `apex.parallel.DistributedDataParallel` and `apex.amp`.
`DistributedDataParallel`, `amp`, and `SyncBatchNorm` will still be usable, but they may be slower.
### [Experimental] Windows
`pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .` may work if you were able to build Pytorch from source
on your system. A Python-only build via `pip install -v --no-cache-dir .` is more likely to work.
If you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.
## Custom C++/CUDA Extensions and Install Options
If a requirement of a module is not met, then it will not be built.
| Module Name | Install Option | Misc |
|---------------|------------------|--------|
| `apex_C` | `--cpp_ext` | |
| `amp_C` | `--cuda_ext` | |
| `syncbn` | `--cuda_ext` | |
| `fused_layer_norm_cuda` | `--cuda_ext` | [`apex.normalization`](./apex/normalization) |
| `mlp_cuda` | `--cuda_ext` | |
| `scaled_upper_triang_masked_softmax_cuda` | `--cuda_ext` | |
| `generic_scaled_masked_softmax_cuda` | `--cuda_ext` | |
| `scaled_masked_softmax_cuda` | `--cuda_ext` | |
| `fused_weight_gradient_mlp_cuda` | `--cuda_ext` | Requires CUDA>=11 |
| `permutation_search_cuda` | `--permutation_search` | [`apex.contrib.sparsity`](./apex/contrib/sparsity) |
| `bnp` | `--bnp` | [`apex.contrib.groupbn`](./apex/contrib/groupbn) |
| `xentropy` | `--xentropy` | [`apex.contrib.xentropy`](./apex/contrib/xentropy) |
| `focal_loss_cuda` | `--focal_loss` | [`apex.contrib.focal_loss`](./apex/contrib/focal_loss) |
| `fused_index_mul_2d` | `--index_mul_2d` | [`apex.contrib.index_mul_2d`](./apex/contrib/index_mul_2d) |
| `fused_adam_cuda` | `--deprecated_fused_adam` | [`apex.contrib.optimizers`](./apex/contrib/optimizers) |
| `fused_lamb_cuda` | `--deprecated_fused_lamb` | [`apex.contrib.optimizers`](./apex/contrib/optimizers) |
| `fast_layer_norm` | `--fast_layer_norm` | [`apex.contrib.layer_norm`](./apex/contrib/layer_norm). different from `fused_layer_norm` |
| `fmhalib` | `--fmha` | [`apex.contrib.fmha`](./apex/contrib/fmha) |
| `fast_multihead_attn` | `--fast_multihead_attn` | [`apex.contrib.multihead_attn`](./apex/contrib/multihead_attn) |
| `transducer_joint_cuda` | `--transducer` | [`apex.contrib.transducer`](./apex/contrib/transducer) |
| `transducer_loss_cuda` | `--transducer` | [`apex.contrib.transducer`](./apex/contrib/transducer) |
| `cudnn_gbn_lib` | `--cudnn_gbn` | Requires cuDNN>=8.5, [`apex.contrib.cudnn_gbn`](./apex/contrib/cudnn_gbn) |
| `peer_memory_cuda` | `--peer_memory` | [`apex.contrib.peer_memory`](./apex/contrib/peer_memory) |
| `nccl_p2p_cuda` | `--nccl_p2p` | Requires NCCL >= 2.10, [`apex.contrib.nccl_p2p`](./apex/contrib/nccl_p2p) |
| `fast_bottleneck` | `--fast_bottleneck` | Requires `peer_memory_cuda` and `nccl_p2p_cuda`, [`apex.contrib.bottleneck`](./apex/contrib/bottleneck) |
| `fused_conv_bias_relu` | `--fused_conv_bias_relu` | Requires cuDNN>=8.4, [`apex.contrib.conv_bias_relu`](./apex/contrib/conv_bias_relu) |
|
Chakita/KNUBert
|
[
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 20 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573')
model = AutoModel.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7200 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Chakita/Kalbert
|
[
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
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| 5 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 63.80 +/- 55.47
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Chandanbhat/distilbert-base-uncased-finetuned-cola
|
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}
| 0 | null |
---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_bert_fusion_iemocap_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_bert_fusion_iemocap_1
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Cheatham/xlm-roberta-large-finetuned-d12_2
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9180645161290323
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7844
- Accuracy: 0.9181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.3042 | 1.0 | 318 | 3.3043 | 0.7403 |
| 2.6451 | 2.0 | 636 | 1.8920 | 0.8365 |
| 1.5585 | 3.0 | 954 | 1.1716 | 0.8881 |
| 1.0188 | 4.0 | 1272 | 0.8677 | 0.9142 |
| 0.8044 | 5.0 | 1590 | 0.7844 | 0.9181 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0.dev20230502
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Cheatham/xlm-roberta-large-finetuned
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"XLMRobertaForSequenceClassification"
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| 20 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: TDMX
---
### TDMX_Edge_of_Realism_Dreambooth Dreambooth model trained by timlenardo with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
TDMX (use that on your prompt)
|
Chiuchiyin/Donald
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
base_model: danbrown/AnyLora-v1
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - danbrown/testman-lora
These are LoRA adaption weights for danbrown/AnyLora-v1. The weights were fine-tuned on the danbrown/testman-dataset dataset. You can find some example images in the following.
|
ChristopherA08/IndoELECTRA
|
[
"pytorch",
"electra",
"pretraining",
"id",
"dataset:oscar",
"transformers"
] | null |
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| 4 | null |
---
license: apache-2.0
inference: false
---
**NOTE: This "delta model" cannot be used directly.**
Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights.
See https://github.com/haotian-liu/LLaVA#llava-weights for instructions.
<br>
<br>
# LLaVA Model Card
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-Lightning was trained in May 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
**License:**
Apache License 2.0
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
80K GPT-generated multimodal instruction-following data.
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs.
We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset.
See https://llava-vl.github.io/ for more details.
|
Chun/w-zh2en-hsk
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 3 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Hongjea/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Chungu424/DATA
|
[] | null |
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| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Hongjea/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Chuu/Chumar
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ratish/GPT2_CleanDesc_Fault-No_Fault_v1.1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ratish/GPT2_CleanDesc_Fault-No_Fault_v1.1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6000
- Validation Loss: 0.7735
- Train Accuracy: 0.5128
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 608, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.6000 | 0.7735 | 0.5128 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Cloudy/DialoGPT-CJ-large
|
[
"pytorch",
"conversational"
] |
conversational
|
{
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| 1 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ratish/GPT2_CleanDesc_Fault-No_Fault_v1.2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ratish/GPT2_CleanDesc_Fault-No_Fault_v1.2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5651
- Validation Loss: 0.6865
- Train Accuracy: 0.5128
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 608, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.7523 | 0.7722 | 0.4615 | 0 |
| 0.6486 | 0.6941 | 0.5897 | 1 |
| 0.6371 | 0.7005 | 0.5385 | 2 |
| 0.5907 | 0.6752 | 0.5897 | 3 |
| 0.5651 | 0.6865 | 0.5128 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CodeDanCode/CartmenBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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| 14 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: juan-barsce/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juan-barsce/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5461
- Validation Loss: 1.7530
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4979 | 2.1013 | 0 |
| 1.7855 | 1.7530 | 1 |
| 1.5461 | 1.7530 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CoffeeAddict93/gpt1-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
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}
}
| 8 | 2023-05-03T01:13:01Z |
---
tags:
- generated_from_trainer
model-index:
- name: fine-tuned-twitter-Roberta-base-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-twitter-Roberta-base-sentiment
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5453
- eval_accuracy: {'accuracy': 0.7915}
- eval_f1score: {'f1': 0.790972084150606}
- eval_runtime: 68.7486
- eval_samples_per_second: 29.092
- eval_steps_per_second: 3.636
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1399
- num_epochs: 7
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CoffeeAddict93/gpt2-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"do_sample": true,
"max_length": 50
},
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},
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}
| 6 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ratish/GPT2_CleanDesc_Fault-No_Fault_v1.3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ratish/GPT2_CleanDesc_Fault-No_Fault_v1.3
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3170
- Validation Loss: 0.3696
- Train Accuracy: 0.8974
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3040, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.9392 | 0.6046 | 0.6410 | 0 |
| 0.6261 | 0.5169 | 0.7692 | 1 |
| 0.5918 | 0.5108 | 0.7436 | 2 |
| 0.5610 | 0.4920 | 0.7179 | 3 |
| 0.5464 | 0.4895 | 0.7692 | 4 |
| 0.5153 | 0.4677 | 0.7179 | 5 |
| 0.4647 | 0.4019 | 0.8205 | 6 |
| 0.4231 | 0.3616 | 0.7692 | 7 |
| 0.3828 | 0.3766 | 0.7949 | 8 |
| 0.3170 | 0.3696 | 0.8974 | 9 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CogComp/bart-faithful-summary-detector
|
[
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
] |
text-classification
|
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": 1,
"max_length": 128,
"min_length": 12,
"no_repeat_ngram_size": null,
"num_beams": 4,
"prefix": null
},
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},
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}
| 234 | 2023-05-03T01:17:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-xc-nfl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-xc-nfl
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 4.1948
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0002 | 142.86 | 1000 | 0.0002 | 4.1948 |
| 0.0001 | 285.71 | 2000 | 0.0001 | 4.1948 |
| 0.0 | 428.57 | 3000 | 0.0000 | 4.1948 |
| 0.0 | 571.43 | 4000 | 0.0000 | 4.1948 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CogComp/roberta-temporal-predictor
|
[
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.00436",
"transformers",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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| 14 | 2023-05-03T01:19:48Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training_epoch3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training_epoch3
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0549
- Train Accuracy: 0.9840
- Validation Loss: 0.1688
- Validation Accuracy: 0.9358
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 3e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.0549 | 0.9840 | 0.1688 | 0.9358 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.7.0
- Datasets 2.10.1
- Tokenizers 0.12.1
|
Coldestadam/Breakout_Mentors_SpongeBob_Model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 10 | 2023-05-03T01:23:32Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015')
model = AutoModel.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7200 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
ComCom/gpt2-medium
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: chatgpt_eli5_text_classifier_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chatgpt_eli5_text_classifier_v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1411
- Accuracy: 0.9714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0872 | 1.0 | 1660 | 0.1256 | 0.9678 |
| 0.0424 | 2.0 | 3320 | 0.1411 | 0.9714 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ComCom-Dev/gpt2-bible-test
|
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| 0 | 2023-05-03T01:31:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9325
- name: F1
type: f1
value: 0.9325882582964963
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1517
- Accuracy: 0.9325
- F1: 0.9326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7478 | 1.0 | 250 | 0.2452 | 0.914 | 0.9142 |
| 0.1906 | 2.0 | 500 | 0.1661 | 0.9355 | 0.9355 |
| 0.1241 | 3.0 | 750 | 0.1527 | 0.93 | 0.9304 |
| 0.0982 | 4.0 | 1000 | 0.1474 | 0.936 | 0.9357 |
| 0.081 | 5.0 | 1250 | 0.1517 | 0.9325 | 0.9326 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Connor-tech/bert_cn_finetuning
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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| 27 | 2023-05-03T01:35:12Z |
---
language: en
license: other
commercial: no
inference: false
---
# PygmalionCoT-7b
## Model description
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
This is a merge between PygmalionAI's pygmalion-7b
**https://huggingface.co/PygmalionAI/pygmalion-7b**
And kaiokendev's 7b SuperCOT-LoRA (Chain of thought)
**https://huggingface.co/kaiokendev/SuperCOT-LoRA**
In safetensor format. Model may be less repetitive and follow events more logically yet outputs might be smaller and rely heavily on example dialogues. Using anything other than Pygmalion's prompt format might exacerbate this.
### Quantization Information
GPTQ cuda quantized with: https://github.com/0cc4m/GPTQ-for-LLaMa
```
python llama.py --wbits 4 models/PygmalionCoT-7b c4 --true-sequential --groupsize 128 --save_safetensors models/PygmalionCoT-7b/PygmalionCoT-7b-4bit-128g.safetensors
```
llama.cpp quantizations: ggml-q4_2, ggml-q5_1, ggml-q8_0
```
./quantize ./models/PygmalionCoT-7b/ggml-model-f16.bin ./models/PygmalionCoT-7b/PygmalionCoT-7b-ggml-q4_2.bin q4_2
./quantize ./models/PygmalionCoT-7b/ggml-model-f16.bin ./models/PygmalionCoT-7b/PygmalionCoT-7b-ggml-q5_1.bin q5_1
./quantize ./models/PygmalionCoT-7b/ggml-model-f16.bin ./models/PygmalionCoT-7b/PygmalionCoT-7b-ggml-q8_0.bin q8_0
```
|
ConstellationBoi/Oop
|
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| 0 | 2023-05-03T01:39:51Z |
---
tags:
- conversational
---
# Sonic the Hedgehog DialoGPT Model
|
Contrastive-Tension/BERT-Base-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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| 16 | null |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable diffusion
- anime
---
A merge mix based on the IDENTITY DISORDER LINE.
More information coming soon
Please do not onsell this model.
If you want to use it for a generation service please linkback.
Details mostly on Civit: https://civitai.com/models/30203/identity-disorder-nsfw-support
Sample Images:
So far the mix is LIKELY:
A couple of super spicy deleted hidden models which I won't admit to
Yohan-Spice, Possibly Aviary, - Identity Disorder's recent update - Whatever models are shoved into Liquid/Illustration Mix
I'll have a clear update soon.
|
Contrastive-Tension/BERT-Distil-CT-STSb
|
[
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"DistilBertModel"
],
"model_type": "distilbert",
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| 1 | 2023-05-03T01:43:40Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable diffusion
- illustration
---
A merge mix of several LORA, Marvels & Dungeons, Identity Disorder, Osenayan Mix & other models - Will add details to Osenayan mix's recipe card soon.
DO NOT ON SELL THIS MODEL.
Generation services: You better darn well hit the Ko-Fi button if you're gonna use it because this stuff costs me money: https://www.buymeacoffee.com/duskfallxcrew
|
Contrastive-Tension/BERT-Distil-CT
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"DistilBertForMaskedLM"
],
"model_type": "distilbert",
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| 9 | null |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Contrastive-Tension/BERT-Distil-NLI-CT
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
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}
}
}
| 6 | 2023-05-03T01:44:25Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Contrastive-Tension/BERT-Large-NLI-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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"conversational": {
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| 15 | null |
---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gpt-2-10000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-2-10000
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2551
- Precision: 0.1523
- Recall: 0.2608
- F1: 0.1923
- Accuracy: 0.9175
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4502 | 1.0 | 477 | 0.3178 | 0.1351 | 0.2289 | 0.1699 | 0.8953 |
| 0.3283 | 2.0 | 954 | 0.3014 | 0.1227 | 0.2220 | 0.1581 | 0.8985 |
| 0.3016 | 3.0 | 1431 | 0.2768 | 0.1441 | 0.2379 | 0.1795 | 0.9077 |
| 0.2824 | 4.0 | 1908 | 0.2687 | 0.1442 | 0.2415 | 0.1806 | 0.9103 |
| 0.2686 | 5.0 | 2385 | 0.2697 | 0.1374 | 0.2383 | 0.1743 | 0.9086 |
| 0.2568 | 6.0 | 2862 | 0.2573 | 0.1450 | 0.2525 | 0.1842 | 0.9140 |
| 0.2472 | 7.0 | 3339 | 0.2534 | 0.1492 | 0.2574 | 0.1889 | 0.9166 |
| 0.2405 | 8.0 | 3816 | 0.2548 | 0.1413 | 0.2515 | 0.1809 | 0.9153 |
| 0.2345 | 9.0 | 4293 | 0.2545 | 0.1489 | 0.2564 | 0.1884 | 0.9163 |
| 0.2299 | 10.0 | 4770 | 0.2551 | 0.1523 | 0.2608 | 0.1923 | 0.9175 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CouchCat/ma_ner_v7_distil
|
[
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] |
token-classification
|
{
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"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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}
}
}
| 13 | 2023-05-03T01:57:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Coyotl/DialoGPT-test2-arthurmorgan
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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},
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},
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}
}
| 7 | 2023-05-03T02:00:56Z |
---
license: cc0-1.0
---
a stylegan2ada network checkpoint trained on synthetic 1024x1024 images of curated generated potion item images .
more information here : [https://github.com/CorvaeOboro/gen_item](https://github.com/CorvaeOboro/gen_item).
|
CrisLeaf/generador-de-historias-de-tolkien
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
"min_length": null,
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"prefix": null
},
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},
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}
}
}
| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_es
model-index:
- name: bert-base-spanish_squad_es-TFM_4-Question-Answering
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish_squad_es-TFM_4-Question-Answering
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the squad_es dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_2
|
[] | null |
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}
| 0 | 2023-05-03T02:36:53Z |
---
license: openrail
datasets:
- allenai/soda
language:
- en
pipeline_tag: conversational
---
# What is Cadet-Medium?
Inspired by Allen AI's **Cosmo-XL**, **Cadet-Medium** is a somewhat small conversational model trained off of the **SODA** dataset. **Cadet-Medium** is intended for inference at the edge (on something as small as a 2GB RAM Raspberry Pi).
**Cadet-Medium** is trained off of the **t5-base** pretrained model from Google.
If you have any questions, or any comments on improvements, please contact me at: **[email protected]**
# Google Colab Link
Here is the link to the Google Colab file, where I walk through the process of training the model and using the SODA public dataset from AI2.
https://colab.research.google.com/drive/1uekZ0gO3GqjPwno16tV1A4Gitrl7p3ur?usp=sharing
# Get Started With Cadet-Medium
Use the code snippet below to get started with Cadet-Medium!
```
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import colorful as cf
cf.use_true_colors()
cf.use_style('monokai')
class CadetMedAgent:
def __init__(self):
print(cf.bold | cf.purple("Waking up Cadet-Medium..."))
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", model_max_length=512)
self.model = AutoModelForSeq2SeqLM.from_pretrained("ToddGoldfarb/Cadet-Medium", low_cpu_mem_usage=True).to(self.device)
self.conversation_history = ""
def observe(self, observation):
self.conversation_history = self.conversation_history + observation
# The number 400 below is just a truncation safety net. It leaves room for 112 input tokens.
if len(self.conversation_history) > 400:
self.conversation_history = self.conversation_history[112:]
def set_input(self, situation_narrative="", role_instruction=""):
input_text = "dialog: "
if situation_narrative != "":
input_text = input_text + situation_narrative
if role_instruction != "":
input_text = input_text + " <SEP> " + role_instruction
input_text = input_text + " <TURN> " + self.conversation_history
# Uncomment the line below to see what is fed to the model.
# print(input_text)
return input_text
def generate(self, situation_narrative, role_instruction, user_response):
user_response = user_response + " <TURN> "
self.observe(user_response)
input_text = self.set_input(situation_narrative, role_instruction)
inputs = self.tokenizer([input_text], return_tensors="pt").to(self.device)
# I encourage you to change the hyperparameters of the model! Start by trying to modify the temperature.
outputs = self.model.generate(inputs["input_ids"], max_new_tokens=512, temperature=1, top_p=.95,
do_sample=True)
cadet_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
added_turn = cadet_response + " <TURN> "
self.observe(added_turn)
return cadet_response
def reset_history(self):
self.conversation_history = []
def run(self):
def get_valid_input(prompt, default):
while True:
user_input = input(prompt)
if user_input in ["Y", "N", "y", "n"]:
return user_input
if user_input == "":
return default
while True:
continue_chat = ""
# MODIFY THESE STRINGS TO YOUR LIKING :)
situation_narrative = "Imagine you are Cadet-Medium talking to ???."
role_instruction = "You are Cadet-Medium, and you are talking to ???."
self.chat(situation_narrative, role_instruction)
continue_chat = get_valid_input(cf.purple("Start a new conversation with new setup? [Y/N]:"), "Y")
if continue_chat in ["N", "n"]:
break
print(cf.blue("CM: See you!"))
def chat(self, situation_narrative, role_instruction):
print(cf.green(
"Cadet-Medium is running! Input [RESET] to reset the conversation history and [END] to end the conversation."))
while True:
user_input = input("You: ")
if user_input == "[RESET]":
self.reset_history()
print(cf.green("[Conversation history cleared. Chat with Cadet-Medium!]"))
continue
if user_input == "[END]":
break
response = self.generate(situation_narrative, role_instruction, user_input)
print(cf.blue("CM: " + response))
def main():
print(cf.bold | cf.blue("LOADING MODEL"))
CadetMed = CadetMedAgent()
CadetMed.run()
if __name__ == '__main__':
main()
```
# Citations and Special Thanks
Special thanks to Hyunwoo Kim for discussing with me the best way to use the SODA dataset. If you haven't looked into their work with SODA, Prosocial-Dialog, or COSMO, I recommend you do so! As well, read the paper on SODA!
The article is listed below.
```
@article{kim2022soda,
title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
journal={ArXiv},
year={2022},
volume={abs/2212.10465}
}
```
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ancc
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9493548387096774
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3288
- Accuracy: 0.9494
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.9476 | 1.0 | 318 | 2.9510 | 0.7468 |
| 2.2551 | 2.0 | 636 | 1.4760 | 0.8555 |
| 1.1113 | 3.0 | 954 | 0.7582 | 0.9126 |
| 0.5674 | 4.0 | 1272 | 0.4822 | 0.9326 |
| 0.3386 | 5.0 | 1590 | 0.3837 | 0.9435 |
| 0.2399 | 6.0 | 1908 | 0.3515 | 0.9432 |
| 0.1951 | 7.0 | 2226 | 0.3370 | 0.9465 |
| 0.1736 | 8.0 | 2544 | 0.3320 | 0.9468 |
| 0.1631 | 9.0 | 2862 | 0.3286 | 0.9471 |
| 0.1575 | 10.0 | 3180 | 0.3288 | 0.9494 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0.dev20230502
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CyberMuffin/DialoGPT-small-ChandlerBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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| 9 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-finetuned-jv-to-id
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-finetuned-jv-to-id
This model is a fine-tuned version of [hermanshid/opus-mt-finetuned-id-to-jv](https://huggingface.co/hermanshid/opus-mt-finetuned-id-to-jv) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6790
- Bleu: 45.3883
- Gen Len: 57.2188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.8743 | 1.0 | 2500 | 0.8210 | 40.2457 | 57.0176 |
| 0.7743 | 2.0 | 5000 | 0.7618 | 42.225 | 56.9092 |
| 0.7225 | 3.0 | 7500 | 0.7329 | 43.3571 | 57.0074 |
| 0.6783 | 4.0 | 10000 | 0.7116 | 44.2165 | 57.084 |
| 0.6467 | 5.0 | 12500 | 0.6995 | 44.5317 | 57.0166 |
| 0.6268 | 6.0 | 15000 | 0.6919 | 44.8921 | 57.1716 |
| 0.61 | 7.0 | 17500 | 0.6854 | 45.162 | 57.1296 |
| 0.5956 | 8.0 | 20000 | 0.6819 | 45.2834 | 57.2868 |
| 0.5816 | 9.0 | 22500 | 0.6789 | 45.3757 | 57.2016 |
| 0.5717 | 10.0 | 25000 | 0.6790 | 45.3883 | 57.2188 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
D3vil/DialoGPT-smaall-harrypottery
|
[] | null |
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| 0 | 2023-05-03T02:57:53Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015-1bbc3e
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015-1bbc3e')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015-1bbc3e')
model = AutoModel.from_pretrained('rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015-1bbc3e')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/e5-base-unsupervised-pseudo-gpl-fiqa-131a12-d23573-4be015-1bbc3e)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7200 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DARKVIP3R/DialoGPT-medium-Anakin
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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| 13 | 2023-05-03T03:03:57Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: a women wearing coatyero coat
---
### coatyero Dreambooth model trained by pablodawson with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
a women wearing coatyero coat (use that on your prompt)
|
DHBaek/gpt2-stackoverflow-question-contents-generator
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
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}
}
| 14 | 2023-05-03T03:09:06Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rickybaker/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DSI/personal_sentiment
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 25 | 2023-05-03T03:18:34Z |
---
language:
- km
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- openslr
- google/fleurs
- seanghay/km-speech-corpus
metrics:
- wer
model-index:
- name: Whisper Small Khmer Spaced - Seanghay Yath
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Google FLEURS
type: google/fleurs
config: km_kh
split: test
metrics:
- name: Wer
type: wer
value: 0.6165
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-khmer-v2
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the `openslr`, `google/fleurs` and `km-speech-corpus` dataset.
It achieves the following results on the evaluation set:
- Loss: 0.26
- Wer: 0.6165
## Model description
This model is fine-tuned with Google FLEURS, OpenSLR (SLR42) and km-speech-corpus dataset.
```python
from transformers import pipeline
pipe = pipeline(
task="automatic-speech-recognition",
model="seanghay/whisper-small-khmer-v2",
)
result = pipe("audio.wav",
generate_kwargs={
"language":"<|km|>",
"task":"transcribe"},
batch_size=16
)
print(result["text"])
```
|
alexandrainst/da-subjectivivity-classification-base
|
[
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"transformers",
"license:cc-by-sa-4.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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}
| 846 | 2023-05-03T03:37:02Z |
---
language:
- en
pipeline_tag: text-generation
---
https://huggingface.co/ausboss/llama7b-wizardlm-unfiltered
|
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews
|
[
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
| 37 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distil_bert_second
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distil_bert_second
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Declan/Breitbart_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
| 3 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.88 +/- 0.70
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Declan/ChicagoTribune_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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}
| 3 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mattjmattj/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Declan/ChicagoTribune_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
}
| 7 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: HF_RL_unit2_taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mattjmattj/HF_RL_unit2_taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Declan/FoxNews_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
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}
| 3 | null |
---
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
widget:
- text: "On Unifying Misinformation Detection. In this paper, we introduce UNIFIEDM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news and verifying rumors. By grouping these tasks together, UNIFIEDM2 learns a richer representation of misinformation, which leads to stateof-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UNIFIEDM2's learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and model's generalizability to unseen events."
example_title: "Misinformation Detection"
---
# SciBERT NLP4SG
SciBERT NLP4SG is a SciBERT model fine-tuned to detect NLP4SG papers based on their title and abstract.
We present the details in the paper:
The training corpus is a combination of the [NLP4SGPapers training set](https://huggingface.co/datasets/feradauto/NLP4SGPapers) which is manually annotated, and some papers identified by keywords.
For more details about the training data and the model, visit the original repo [here](https://github.com/feradauto/nlp4sg).
Please cite the following paper:
```
@misc{gonzalez2023good,
title={Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good},
author={Fernando Gonzalez and Zhijing Jin and Jad Beydoun and Bernhard Schölkopf and Tom Hope and Mrinmaya Sachan and Rada Mihalcea},
year={2023},
eprint={2305.05471},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Declan/FoxNews_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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| 3 | null |
---
license: wtfpl
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- lora
- characters
- anime
---
# Multi-Character Loras
I made these. Enjoy. If you like my work please consider [leaving me a tip](https://ko-fi.com/holostrawberry) :)
You might wonder why all of these are schoolgirls. Well they're marketable and uniforms are easy for Stable Diffusion.
### DDLC / Doki Doki Literature Club
* Monika
* Sayori
* Natsuki
* Yuri
### Love is War / Kaguya-sama wa Kokurasetai
* Miko Iino
* Chika Fujiwara
* Kaguya Shinomiya
* Ai Hayasaka
* Kei Shirogane
### The Quintessential Quintuplets / Go-Tōbun no Hanayome
* Ichika Nakano
* Nino Nakano
* Miku Nakano
* Yotsuba Nakano
* Itsuki Nakano
### Rent-A-Girlfriend / Kanojo, Okarishimasu
* Mami Nanami
* Chizuru Ichinose / Chizuru Mizuhara
* Ruka Sarashina
* Sumi Sakurasawa
|
Declan/Politico_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
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| 7 | null |
---
license: apache-2.0
tags:
- merge
model-index:
- name: expert-uspto
results: []
---
# merge-arxiv-50_github-50_avg
This model is a merge between the following models:
- https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv
- https://huggingface.co/Multi-Domain-Expert-Layers/expert-github
Using a naive weight averaging strategy at a 50/50 ratio per model.
It has yet to be evaluated.
## Model description
More information needed
## Intended uses & limitations
More information needed
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/Reuters_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: longformer-base-4096-bne-es-finetuned-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longformer-base-4096-bne-es-finetuned-v2
This model is a fine-tuned version of [joheras/longformer-base-4096-bne-es-finetuned-clinais](https://huggingface.co/joheras/longformer-base-4096-bne-es-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0205
- Precision: 0.4420
- Recall: 0.6075
- F1: 0.5117
- Accuracy: 0.8420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 196 | 0.6855 | 0.1500 | 0.2840 | 0.1963 | 0.7991 |
| No log | 2.0 | 392 | 0.5677 | 0.2372 | 0.3962 | 0.2967 | 0.8271 |
| 0.7365 | 3.0 | 588 | 0.5457 | 0.3043 | 0.5142 | 0.3823 | 0.8412 |
| 0.7365 | 4.0 | 784 | 0.5818 | 0.3268 | 0.5208 | 0.4016 | 0.8303 |
| 0.7365 | 5.0 | 980 | 0.5958 | 0.3595 | 0.5538 | 0.4359 | 0.8385 |
| 0.3443 | 6.0 | 1176 | 0.6380 | 0.3916 | 0.5660 | 0.4630 | 0.8396 |
| 0.3443 | 7.0 | 1372 | 0.6835 | 0.3499 | 0.5594 | 0.4305 | 0.8272 |
| 0.2031 | 8.0 | 1568 | 0.6758 | 0.4088 | 0.5726 | 0.4770 | 0.8441 |
| 0.2031 | 9.0 | 1764 | 0.7236 | 0.3921 | 0.5792 | 0.4676 | 0.8397 |
| 0.2031 | 10.0 | 1960 | 0.7699 | 0.3941 | 0.5755 | 0.4678 | 0.8349 |
| 0.1283 | 11.0 | 2156 | 0.7788 | 0.4004 | 0.5745 | 0.4719 | 0.8315 |
| 0.1283 | 12.0 | 2352 | 0.7802 | 0.4164 | 0.6019 | 0.4923 | 0.8479 |
| 0.0861 | 13.0 | 2548 | 0.8092 | 0.4280 | 0.5915 | 0.4966 | 0.8394 |
| 0.0861 | 14.0 | 2744 | 0.8582 | 0.4211 | 0.5991 | 0.4945 | 0.8373 |
| 0.0861 | 15.0 | 2940 | 0.8581 | 0.3860 | 0.5925 | 0.4674 | 0.8407 |
| 0.0589 | 16.0 | 3136 | 0.9137 | 0.4213 | 0.6038 | 0.4963 | 0.8291 |
| 0.0589 | 17.0 | 3332 | 0.8669 | 0.4287 | 0.6038 | 0.5014 | 0.8448 |
| 0.0436 | 18.0 | 3528 | 0.8987 | 0.4365 | 0.6028 | 0.5063 | 0.8403 |
| 0.0436 | 19.0 | 3724 | 0.9389 | 0.4437 | 0.5991 | 0.5098 | 0.8360 |
| 0.0436 | 20.0 | 3920 | 0.9512 | 0.4479 | 0.6 | 0.5129 | 0.8348 |
| 0.0313 | 21.0 | 4116 | 0.9484 | 0.4535 | 0.6075 | 0.5194 | 0.8445 |
| 0.0313 | 22.0 | 4312 | 0.9715 | 0.4498 | 0.6123 | 0.5186 | 0.8438 |
| 0.0236 | 23.0 | 4508 | 0.9726 | 0.4542 | 0.6170 | 0.5232 | 0.8457 |
| 0.0236 | 24.0 | 4704 | 0.9586 | 0.4531 | 0.6066 | 0.5188 | 0.8427 |
| 0.0236 | 25.0 | 4900 | 0.9962 | 0.4634 | 0.6160 | 0.5290 | 0.8433 |
| 0.0206 | 26.0 | 5096 | 1.0098 | 0.4683 | 0.6198 | 0.5335 | 0.8429 |
| 0.0206 | 27.0 | 5292 | 0.9914 | 0.4527 | 0.6094 | 0.5195 | 0.8414 |
| 0.0206 | 28.0 | 5488 | 1.0146 | 0.4567 | 0.6113 | 0.5228 | 0.8436 |
| 0.0154 | 29.0 | 5684 | 1.0199 | 0.4468 | 0.6104 | 0.5159 | 0.8428 |
| 0.0154 | 30.0 | 5880 | 1.0205 | 0.4420 | 0.6075 | 0.5117 | 0.8420 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Declan/Reuters_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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| 3 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kogpt2-base-v2-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.37298165525403665
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kogpt2-base-v2-finetuned-klue-ner
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4076
- F1: 0.3730
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6084 | 1.0 | 876 | 0.5353 | 0.2118 |
| 0.3911 | 2.0 | 1752 | 0.4691 | 0.3041 |
| 0.2855 | 3.0 | 2628 | 0.4076 | 0.3730 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Declan/WallStreetJournal_model_v6
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: gold jewelry
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - aellaboudy/generativeModel
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on gold jewelry using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
Declan/test_model
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Mokaaa/distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Mokaaa/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8469
- Validation Loss: 2.6107
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8469 | 2.6107 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/test_push
|
[] | null |
{
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}
| 0 | null |
---
license: creativeml-openrail-m
---
<b>Please read this!</b><br>
This model is in the testing phase. The necessary VAE is already baked into the model.<br><hr>
<b>The recommended negative prompt:</b><br><br>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br><br>
<b>Recommended parameters for generation:</b><br><br>
<b>Sampling method:</b> Euler A<br>
<b>Resolution:</b> 384x512, 512x768, 640x960 / / / 512x384, 768x512, 960x640<br>
<b>CFG Scale:</b> 7<br><br>
<b>Hires.Fix Parameters:</b><br><br>
<b>Upscaler:</b> Latent or other<br>
<b>Hires Steps:</b> 0 or other<br>
<b>Denoising Strength:</b> 0.3 - 0.7<br>
<b>Upscaled by:</b> 1.1 - 2.0<br><hr>
<b>Examples:</b><br><br>
<a href='https://postimg.cc/3kxXkXSJ' target='_blank'><img src='https://i.postimg.cc/0ypcHZ7m/Pic1.png' border='0' alt='Pic1'/></a>
<a href='https://postimg.cc/2qmVqr8d' target='_blank'><img src='https://i.postimg.cc/q76n5vPF/Pic2.png' border='0' alt='Pic2'/></a>
<a href='https://postimg.cc/k6GM84rS' target='_blank'><img src='https://i.postimg.cc/sX9MkQwT/Pic3.png' border='0' alt='Pic3'/></a>
<a href='https://postimg.cc/gX7zKWdT' target='_blank'><img src='https://i.postimg.cc/j2xDtx1t/Pic4.png' border='0' alt='Pic4'/></a>
<a href='https://postimg.cc/Js81xKVM' target='_blank'><img src='https://i.postimg.cc/mgztb6mz/Pic5.png' border='0' alt='Pic5'/></a>
<a href='https://postimg.cc/Pp0HwQQG' target='_blank'><img src='https://i.postimg.cc/Zn55Xf9q/Pic6.png' border='0' alt='Pic6'/></a>
|
DeepESP/gpt2-spanish
|
[
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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},
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},
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"do_sample": true,
"max_length": 50
},
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},
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},
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"prefix": null
}
}
}
| 1,463 | null |
---
license: gpl-3.0
---
# train_MEGA
以马恩全集为主要数据集的训练，未完。<br>
The training using the complete works of Marx and Engels as the primary dataset is incomplete.<br>
Das Training mit den gesammelten Werken von Marx und Engels als primärem Datensatz ist unvollständig.<br>
2023年5月3日15点20分 还在手搓数据集，打算用训练小说的方法试试。
<br>
同步https://github.com/tmzncty/train_MEGA
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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}
| 1,614 | null |
---
language:
- ko
- en
pipeline_tag: text-generation
tags:
- vicuna
- llama
---
<p align="center" width="100%">
<img src="https://i.imgur.com/snFDU0P.png" alt="KoreanLM icon" style="width: 500px; display: block; margin: auto; border-radius: 10%;">
</p>
# KoreanLM: 한국어 언어모델 프로젝트
KoreanLM은 한국어 언어모델을 개발하기 위한 오픈소스 프로젝트입니다. 현재 대부분의 언어모델들은 영어에 초점을 맞추고 있어, 한국어에 대한 학습이 상대적으로 부족하고 토큰화 과정에서 비효율적인 경우가 있습니다. 이러한 문제를 해결하고 한국어에 최적화된 언어모델을 제공하기 위해 KoreanLM 프로젝트를 시작하게 되었습니다.
## 프로젝트 목표
1. 한국어에 특화된 언어모델 개발: 한국어의 문법, 어휘, 문화적 특성을 반영하여 한국어를 더 정확하게 이해하고 생성할 수 있는 언어모델을 개발합니다.
2. 효율적인 토큰화 방식 도입: 한국어 텍스트의 토큰화 과정에서 효율적이고 정확한 분석이 가능한 새로운 토큰화 방식을 도입하여 언어모델의 성능을 향상시킵니다.
3. 거대 언어모델의 사용성 개선: 현재 거대한 사이즈의 언어모델들은 기업이 자사의 데이터를 파인튜닝하기 어려운 문제가 있습니다. 이를 해결하기 위해 한국어 언어모델의 크기를 조절하여 사용성을 개선하고, 자연어 처리 작업에 더 쉽게 적용할 수 있도록 합니다.
## 사용 방법
KoreanLM은 GitHub 저장소를 통해 배포됩니다. 프로젝트를 사용하려면 다음과 같은 방법으로 설치하실 수 있습니다.
```bash
git clone https://github.com/quantumaikr/KoreanLM.git
cd KoreanLM
pip install -r requirements.txt
```
## 예제
다음은 transformers 라이브러리를 통해 모델과 토크나이저를 로딩하는 예제입니다.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained("quantumaikr/KoreanLM")
tokenizer = transformers.AutoTokenizer.from_pretrained("quantumaikr/KoreanLM")
```
## 훈련 (파인튜닝)
```bash
torchrun --nproc_per_node=4 --master_port=1004 train.py \
--model_name_or_path quantumaikr/KoreanLM \
--data_path korean_data.json \
--num_train_epochs 3 \
--cache_dir './data' \
--bf16 True \
--tf32 True \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 500 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'OPTDecoderLayer' \
```
```bash
pip install deepspeed
torchrun --nproc_per_node=4 --master_port=1004 train.py \
--deepspeed "./deepspeed.json" \
--model_name_or_path quantumaikr/KoreanLM \
--data_path korean_data.json \
--num_train_epochs 3 \
--cache_dir './data' \
--bf16 True \
--tf32 True \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 2000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
```
## 훈련 (LoRA)
```bash
python finetune-lora.py \
--base_model 'quantumaikr/KoreanLM' \
--data_path './korean_data.json' \
--output_dir './KoreanLM-LoRA' \
--cache_dir './data'
```
## 추론
```bash
python generate.py \
--load_8bit \
--share_gradio \
--base_model 'quantumaikr/KoreanLM' \
--lora_weights 'quantumaikr/KoreanLM-LoRA' \
--cache_dir './data'
```
## 사전학습 모델 공개 및 웹 데모
[학습모델](https://huggingface.co/quantumaikr/KoreanLM/tree/main)
<i>* 데모 링크는 추후 공계예정</i>
## 기여방법
1. 이슈 제기: KoreanLM 프로젝트와 관련된 문제점이나 개선사항을 이슈로 제기해주세요.
2. 코드 작성: 개선사항이나 새로운 기능을 추가하기 위해 코드를 작성하실 수 있습니다. 작성된 코드는 Pull Request를 통해 제출해주시기 바랍니다.
3. 문서 작성 및 번역: 프로젝트의 문서 작성이나 번역 작업에 참여하여 프로젝트의 질을 높여주세요.
4. 테스트 및 피드백: 프로젝트를 사용하면서 발견한 버그나 개선사항을 피드백해주시면 큰 도움이 됩니다.
## 라이선스
KoreanLM 프로젝트는 Apache 2.0 License 라이선스를 따릅니다. 프로젝트를 사용하실 때 라이선스에 따라 주의사항을 지켜주시기 바랍니다.
## 기술 문의
KoreanLM 프로젝트와 관련된 문의사항이 있으시면 이메일 또는 GitHub 이슈를 통해 문의해주시기 바랍니다. 이 프로젝트가 한국어 언어모델에 대한 연구와 개발에 도움이 되길 바라며, 많은 관심과 참여 부탁드립니다.
이메일: [email protected]
---
This repository has implementations inspired by [open_llama](https://github.com/openlm-research/open_llama), [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [alpaca-lora](https://github.com/tloen/alpaca-lora) projects.
|
DeepPavlov/bert-base-cased-conversational
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
] |
feature-extraction
|
{
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],
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}
}
}
| 3,009 | null |
# LoRA au TOM's GR Supra
このモデルは「au * MOTORSPORT」の画像を使用して学習をしました。
https://www.au.com/motorsport/
---
license: unknown
---
|
DeepPavlov/distilrubert-base-cased-conversational
|
[
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null |
{
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"model_type": "distilbert",
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}
| 6,324 | null |
---
language:
- en
- ja
tags:
- art
license: openrail
---
# 【LoRA】 fushigidesho
* [LoRA for SD ](https://huggingface.co/PastelPlanet/fushigidesho/resolve/main/fushigidesho1.0.safetensors)
fushigidesho is a LoRA file that was studied using the Hyakunin Isshu calligraphy. This is based on a piece of calligraphy that #kazunoko_sho wrote.
* [Download material Zip](https://huggingface.co/PastelPlanet/fushigidesho/blob/main/HyakuninIssyu.zip)
Study material for LoRA files. They may be used free of charge for research and educational purposes. (Copyright © PastelPlanet)
LoRA ファイルの学習素材です。研究・教育目的においては、無償でご利用いただけます。（著作権はパステルプラネットにあります）
## Sample Prompts
* shodo
* calligraphy
* japanese calligraphy
* long sentence
* short sentence
## Recommendations
* use weight between 0.1 - 1.0 (depends on subject and touch you want)
## Information
https://twitter.com/pastelplanetllc/
|
DeepPavlov/rubert-base-cased-conversational
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
"has_space"
] |
feature-extraction
|
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"BertModel"
],
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}
}
| 17,362 | null |
---
license: apache-2.0
---
This model accompanies the following paper:
__ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain__
Mike Zhang, Rob van der Goot, and Barbara Plank. In ACL (2023).
If you use this work please cite the following:
```
<citation coming soon>
```
Find more information in the Github repository: https://github.com/jjzha/escoxlmr
|
DeepPavlov/rubert-base-cased
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers",
"has_space"
] |
feature-extraction
|
{
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"BertModel"
],
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},
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},
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},
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}
}
}
| 148,127 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 826.50 +/- 305.96
name: mean_reward
verified: false
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -orga virusapex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -orga virusapex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga virusapex
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 10000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
|
DeltaHub/adapter_t5-3b_qnli
|
[
"pytorch",
"transformers"
] | null |
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| 3 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.051447714764297686
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0293
- F1: 0.0514
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1388 | 1.0 | 876 | 1.2026 | 0.0 |
| 1.0464 | 2.0 | 1752 | 1.1176 | 0.0052 |
| 0.969 | 3.0 | 2628 | 1.0668 | 0.0422 |
| 0.9015 | 4.0 | 3504 | 1.0293 | 0.0514 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Deniskin/essays_small_2000
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: dgx1_whisper_base_libri360_noisy_teacher_distil_epochs_50_batch_8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dgx1_whisper_base_libri360_noisy_teacher_distil_epochs_50_batch_8
This model is a fine-tuned version of [rohitp1/subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8](https://huggingface.co/rohitp1/subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5761
- Wer: 10.6733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0423 | 1.48 | 150 | 0.1620 | 10.8902 |
| 0.0999 | 2.96 | 300 | 0.2030 | 10.7882 |
| 0.1577 | 4.45 | 450 | 0.2511 | 10.7937 |
| 0.2078 | 5.94 | 600 | 0.2966 | 10.7827 |
| 0.252 | 7.42 | 750 | 0.3321 | 10.7524 |
| 0.2841 | 8.91 | 900 | 0.3625 | 10.7588 |
| 0.3189 | 10.39 | 1050 | 0.3858 | 10.7772 |
| 0.341 | 11.88 | 1200 | 0.4090 | 10.7505 |
| 0.5277 | 13.36 | 1350 | 0.5461 | 11.1926 |
| 0.8342 | 14.85 | 1500 | 0.5250 | 10.8415 |
| 0.8278 | 16.33 | 1650 | 0.5543 | 10.7478 |
| 0.8255 | 17.82 | 1800 | 0.5481 | 10.6761 |
| 0.822 | 19.31 | 1950 | 0.5504 | 10.6650 |
| 0.8204 | 20.79 | 2100 | 0.5556 | 10.6650 |
| 0.8246 | 22.28 | 2250 | 0.5598 | 10.6586 |
| 0.8228 | 23.76 | 2400 | 0.5634 | 10.6770 |
| 0.8282 | 25.25 | 2550 | 0.5670 | 10.6706 |
| 0.8264 | 26.73 | 2700 | 0.5702 | 10.6752 |
| 0.8298 | 28.22 | 2850 | 0.5731 | 10.6908 |
| 0.8273 | 29.7 | 3000 | 0.5761 | 10.6733 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Deniskin/essays_small_2000i
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_keras_callback
model-index:
- name: unsup-simcse-roberta-large-semeval2016-laptops
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# unsup-simcse-roberta-large-semeval2016-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
DeskDown/MarianMixFT_en-id
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
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| 3 | null |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1604728250218979330/8t7FPU54_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Julio004👤</div>
<div style="text-align: center; font-size: 14px;">@julio004</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Julio004👤.
| Data | Julio004👤 |
| --- | --- |
| Tweets downloaded | 3034 |
| Retweets | 468 |
| Short tweets | 256 |
| Tweets kept | 2310 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nr8xg912/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @julio004's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bpb2px4j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bpb2px4j/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/julio004')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
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| 5 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# IslemTouati/sentence-camembert-base
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("IslemTouati/sentence-camembert-base")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
DimaOrekhov/cubert-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"EncoderDecoderModel"
],
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| 10 | null |
---
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.12.1
- Datasets 2.11.0
- Tokenizers 0.11.0
|
DimaOrekhov/transformer-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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"max_length": null
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| 8 | null |
---
license: cc-by-nc-4.0
metrics:
- accuracy
- f1
datasets:
- universal_dependencies
language:
- eu
pipeline_tag: token-classification
tags:
- POS tagging
---
[ElhBERTeu](https://huggingface.co/orai-nlp/ElhBERTeu) model finetuned for POS tagging on Universal Dependencies 1.2 corpus (eiec).
Results on test set:
* accuracy = 0.9793222286042504
* f1 = 0.9712429365605912
* loss = 0.12006692268620504
* precision = 0.9708653494328576
* recall = 0.9716208175035475
Tagset:
[ADJ, ADP, ADV, AUX, CONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SYM, VERB, X]
Finetuning details: 10 epochs, batch size 32, learning rate 3e-5.
|
Dimedrolza/DialoGPT-small-cyberpunk
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
| 9 | null |
---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
duplicated_from: THUDM/chatglm-6b
---
# ChatGLM-6B
<p align="center">
🌐 <a href="https://chatglm.cn/blog" target="_blank">Blog</a> • 💻 <a href="https://github.com/THUDM/ChatGLM-6B" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br>
</p>
<p align="center">
👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1th2q5u69-7tURzFuOPanmuHy9hsZnKA" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
</p>
## 介绍
ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 [ChatGLM](https://chatglm.cn) 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。
ChatGLM-6B is an open bilingual language model based on [General Language Model (GLM)](https://github.com/THUDM/GLM) framework, with 6.2 billion parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese QA and dialogue. The model is trained for about 1T tokens of Chinese and English corpus, supplemented by supervised fine-tuning, feedback bootstrap, and reinforcement learning wit human feedback. With only about 6.2 billion parameters, the model is able to generate answers that are in line with human preference.
## 软件依赖
```shell
pip install protobuf==3.20.0 transformers==4.27.1 icetk cpm_kernels
```
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM-6B)。
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM-6B).
## Change Log
* v0.1.0 ([f83182](https://huggingface.co/THUDM/chatglm-6b/commit/f83182484538e663a03d3f73647f10f89878f438))
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文：
```
@inproceedings{
zeng2023glm-130b,
title={{GLM}-130B: An Open Bilingual Pre-trained Model},
author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=-Aw0rrrPUF}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
```
|
DivyanshuSheth/T5-Seq2Seq-Final
|
[] | null |
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| 0 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 644.00 +/- 209.03
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mattjmattj -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mattjmattj -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mattjmattj
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Dmitriiserg/Pxd
|
[] | null |
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| 0 | 2023-05-03T09:05:15Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: VinayakMane47/finetuned-en-to-hi
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# VinayakMane47/finetuned-en-to-hi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7741
- Validation Loss: 0.9086
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 8202, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.2989 | 0.9978 | 0 |
| 0.9339 | 0.9287 | 1 |
| 0.7741 | 0.9086 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Doiman/DialoGPT-medium-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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| 13 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Regression_bert_NOaug_CustomLoss
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Regression_bert_NOaug_CustomLoss
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0264
- Train Mae: 0.1981
- Train Mse: 0.0536
- Train R2-score: 0.9557
- Validation Loss: 0.1484
- Validation Mae: 0.3703
- Validation Mse: 0.2656
- Validation R2-score: 0.8862
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.1477 | 0.5158 | 0.3587 | 0.8489 | 0.1118 | 0.5348 | 0.3366 | 0.8997 | 0 |
| 0.1280 | 0.4634 | 0.2930 | 0.8414 | 0.1375 | 0.4847 | 0.3121 | 0.8873 | 1 |
| 0.1232 | 0.4331 | 0.2728 | -0.3855 | 0.1453 | 0.5454 | 0.4140 | 0.8773 | 2 |
| 0.0862 | 0.3752 | 0.2042 | 0.8843 | 0.1683 | 0.4117 | 0.2940 | 0.8728 | 3 |
| 0.0827 | 0.3573 | 0.1824 | 0.9046 | 0.1383 | 0.3792 | 0.2434 | 0.8940 | 4 |
| 0.0701 | 0.4034 | 0.2084 | 0.8164 | 0.1313 | 0.4766 | 0.3297 | 0.8879 | 5 |
| 0.0473 | 0.2988 | 0.1245 | 0.8744 | 0.1544 | 0.4001 | 0.2930 | 0.8780 | 6 |
| 0.0370 | 0.2501 | 0.0887 | 0.8672 | 0.1464 | 0.4236 | 0.3019 | 0.8809 | 7 |
| 0.0346 | 0.3122 | 0.1224 | 0.9196 | 0.1296 | 0.4837 | 0.3147 | 0.8885 | 8 |
| 0.0303 | 0.2493 | 0.0864 | 0.9624 | 0.1399 | 0.4292 | 0.2975 | 0.8876 | 9 |
| 0.0312 | 0.2527 | 0.0862 | 0.9426 | 0.1436 | 0.3984 | 0.2722 | 0.8876 | 10 |
| 0.0301 | 0.2160 | 0.0657 | 0.6312 | 0.1479 | 0.3819 | 0.2836 | 0.8849 | 11 |
| 0.0275 | 0.2286 | 0.0712 | 0.9543 | 0.1473 | 0.3770 | 0.2634 | 0.8851 | 12 |
| 0.0272 | 0.2209 | 0.0656 | 0.9691 | 0.1372 | 0.4141 | 0.2886 | 0.8899 | 13 |
| 0.0264 | 0.1981 | 0.0536 | 0.9557 | 0.1484 | 0.3703 | 0.2656 | 0.8862 | 14 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DongHai/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 9 | null |
---
license: mit
language: fr
datasets:
- ccnet
tags:
- deberta
- deberta-v3
inference: false
---
# CamemBERTa: A French language model based on DeBERTa V3
CamemBERTa, a French language model based on DeBERTa V3, which is a DeBerta V2 with ELECTRA style pretraining using the Replaced Token Detection (RTD) objective.
RTD uses a generator model, trained using the MLM objective, to replace masked tokens with plausible candidates, and a discriminator model trained to detect which tokens were replaced by the generator.
Usually the generator and discriminator share the same embedding matrix, but the authors of DeBERTa V3 propose a new technique to disentagle the gradients of the shared embedding between the generator and discriminator called gradient-disentangled embedding sharing (GDES)
*This the first publicly available implementation of DeBERTa V3, and the first publicly DeBERTaV3 model outside of the original Microsoft release.*
Preprint Paper: https://inria.hal.science/hal-03963729/
Pre-training Code: https://gitlab.inria.fr/almanach/CamemBERTa
## How to use CamemBERTa
Our pretrained weights are available on the HuggingFace model hub, you can load them using the following code:
```python
from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM
CamemBERTa = AutoModel.from_pretrained("almanach/camemberta-base")
tokenizer = AutoTokenizer.from_pretrained("almanach/camemberta-base")
CamemBERTa_gen = AutoModelForMaskedLM.from_pretrained("almanach/camemberta-base-generator")
tokenizer_gen = AutoTokenizer.from_pretrained("almanach/camemberta-base-generator")
```
We also include the TF2 weights including the weights for the model's RTD head for the discriminator, and the MLM head for the generator.
CamemBERTa is compatible with most finetuning scripts from the transformers library.
## Pretraining Setup
The model was trained on the French subset of the CCNet corpus (the same subset used in CamemBERT and PaGNOL) and is available on the HuggingFace model hub: CamemBERTa and CamemBERTa Generator.
To speed up the pre-training experiments, the pre-training was split into two phases;
in phase 1, the model is trained with a maximum sequence length of 128 tokens for 10,000 steps with 2,000 warm-up steps and a very large batch size of 67,584.
In phase 2, maximum sequence length is increased to the full model capacity of 512 tokens for 3,300 steps with 200 warm-up steps and a batch size of 27,648.
The model would have seen 133B tokens compared to 419B tokens for CamemBERT-CCNet which was trained for 100K steps, this represents roughly 30% of CamemBERT’s full training.
To have a fair comparison, we trained a RoBERTa model, CamemBERT30%, using the same exact pretraining setup but with the MLM objective.
## Pretraining Loss Curves
check the tensorboard logs and plots
## Fine-tuning results
Datasets: POS tagging and Dependency Parsing (GSD, Rhapsodie, Sequoia, FSMB), NER (FTB), the FLUE benchmark (XNLI, CLS, PAWS-X), and the French Question Answering Dataset (FQuAD)
| Model | UPOS | LAS | NER | CLS | PAWS-X | XNLI | F1 (FQuAD) | EM (FQuAD) |
|-------------------|-----------|-----------|-----------|-----------|-----------|-----------|------------|------------|
| CamemBERT (CCNet) | **97.59** | **88.69** | 89.97 | 94.62 | 91.36 | 81.95 | 80.98 | **62.51** |
| CamemBERT (30%) | 97.53 | 87.98 | **91.04** | 93.28 | 88.94 | 79.89 | 75.14 | 56.19 |
| CamemBERTa | 97.57 | 88.55 | 90.33 | **94.92** | **91.67** | **82.00** | **81.15** | 62.01 |
The following table compares CamemBERTa's performance on XNLI against other models under different training setups, which demonstrates the data efficiency of CamemBERTa.
| Model | XNLI (Acc.) | Training Steps | Tokens seen in pre-training | Dataset Size in Tokens |
|-------------------|-------------|----------------|-----------------------------|------------------------|
| mDeBERTa | 84.4 | 500k | 2T | 2.5T |
| CamemBERTa | 82.0 | 33k | 0.139T | 0.319T |
| XLM-R | 81.4 | 1.5M | 6T | 2.5T |
| CamemBERT - CCNet | 81.95 | 100k | 0.419T | 0.319T |
*Note: The CamemBERTa training steps was adjusted for a batch size of 8192.*
## License
The public model weights are licensed under MIT License.
This code is licensed under the Apache License 2.0.
## Citation
Paper accepted to Findings of ACL 2023.
You can use the preprint citation for now
```
@article{antoun2023camemberta
TITLE = {{Data-Efficient French Language Modeling with CamemBERTa}},
AUTHOR = {Antoun, Wissam and Sagot, Beno{\^i}t and Seddah, Djam{\'e}},
URL = {https://inria.hal.science/hal-03963729},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Jan,
PDF = {https://inria.hal.science/hal-03963729/file/French_DeBERTa___ACL_2023%20to%20be%20uploaded.pdf},
HAL_ID = {hal-03963729},
HAL_VERSION = {v1},
}
```
## Contact
Wissam Antoun: `wissam (dot) antoun (at) inria (dot) fr`
Benoit Sagot: `benoit (dot) sagot (at) inria (dot) fr`
Djame Seddah: `djame (dot) seddah (at) inria (dot) fr`
|
Dongjae/mrc2reader
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"XLMRobertaForQuestionAnswering"
],
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}
}
}
| 3 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: Gold Jewelry
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - ameersjewelrygenerativemodel
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "Gold Jewelry" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
Doohae/p_encoder
|
[
"pytorch"
] | null |
{
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5353
- Bleu: 0.0
- Gen Len: 9.6552
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|
| No log | 1.0 | 8 | 4.6558 | 0.0 | 9.1034 |
| No log | 2.0 | 16 | 4.5353 | 0.0 | 9.6552 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Doquey/DialoGPT-small-Michaelbot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 10 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 105 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.BatchHardTripletLoss.BatchHardTripletLoss`
Parameters of the fit()-Method:
```
{
"epochs": 50,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 524.5,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DoyyingFace/bert-asian-hate-tweets-asonam-unclean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 30 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: french_51_small_updated
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# french_51_small_updated
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 7
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 5
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.13.2
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 38,156 | 2023-05-03T09:40:57Z |
Hi,guys!
这是一个以石窟中的菩萨为母题制作的Lora，还在实验中，大家在使用后可否给我提点意见，感激不尽！
此Lora搭配CounterfeitV30大模型效果还行，如果想生成菩萨的样子，触发词可用“Bodhisattva”。
我在测试中发现，采样器使用“DPM++ SDE Karras”最佳，迭代步数需要开到30，CFG最好是10。
关于这个Lora的权重，实测中0.2-0.4风格较为多变和自由，0.6-1则出现菩萨的样貌最为明显。当然，在搭配其他Lora使用后，效果也还不错。
(以下是机器翻译内容：)
This is a Lora made with the bodhisattva in the grotto as the mother theme, still experimenting, can you give me some advice after using it, thank you very much!
This Lora works fine with CounterfeitV30 large model, if you want to generate the appearance of Bodhisattva, the trigger word can be used "Bodhisattva".
I found in the test, the sampler using "DPM++ SDE Karras" best, the number of iterative steps need to open to 30, CFG preferably 10.
Regarding the weight of this Lora, the actual measurement of 0.2-0.4 style is more variable and free, and 0.6-1 appears the most obvious Bodhisattva-like. Of course, after using with other Lora, the effect is not bad.
|
albert-large-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 687 | 2023-05-03T09:42:33Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bbillapati/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
albert-large-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 26,792 | 2023-05-03T09:45:11Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Regression_bert_aug_CustomLoss_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Regression_bert_aug_CustomLoss_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2338
- Train Mae: 0.5263
- Train Mse: 0.4258
- Train R2-score: 0.7899
- Validation Loss: 0.2340
- Validation Mae: 0.5490
- Validation Mse: 0.4329
- Validation R2-score: 0.7254
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2004 | 0.4982 | 0.3687 | 0.6907 | 0.1488 | 0.4239 | 0.3023 | 0.7428 | 0 |
| 0.1118 | 0.4054 | 0.2460 | 0.7552 | 0.0783 | 0.3502 | 0.1873 | 0.8501 | 1 |
| 0.0531 | 0.3256 | 0.1543 | 0.8049 | 0.0489 | 0.3257 | 0.1489 | 0.8412 | 2 |
| 0.0342 | 0.2826 | 0.1151 | 0.7986 | 0.0328 | 0.2697 | 0.1215 | 0.9246 | 3 |
| 0.0266 | 0.2587 | 0.0962 | 0.8802 | 0.0713 | 0.2884 | 0.1297 | 0.8729 | 4 |
| 0.0543 | 0.3022 | 0.1388 | 0.7724 | 0.0609 | 0.3238 | 0.1524 | 0.7723 | 5 |
| 0.0380 | 0.2756 | 0.1114 | 0.8822 | 0.0421 | 0.1984 | 0.0700 | 0.9070 | 6 |
| 0.0593 | 0.3134 | 0.1537 | 0.8764 | 0.2335 | 0.5183 | 0.4636 | 0.7816 | 7 |
| 0.2330 | 0.5234 | 0.4182 | -1.5020 | 0.2656 | 0.5726 | 0.3948 | 0.5533 | 8 |
| 0.2359 | 0.5149 | 0.4195 | 0.7932 | 0.2347 | 0.5263 | 0.4461 | 0.7502 | 9 |
| 0.2341 | 0.5204 | 0.4268 | 0.8100 | 0.2341 | 0.5509 | 0.4307 | 0.7220 | 10 |
| 0.2335 | 0.5250 | 0.4235 | 0.8053 | 0.2328 | 0.5433 | 0.4334 | 0.7322 | 11 |
| 0.2319 | 0.5217 | 0.4244 | 0.7825 | 0.2352 | 0.5549 | 0.4296 | 0.7158 | 12 |
| 0.2323 | 0.5243 | 0.4234 | 0.7877 | 0.2346 | 0.5536 | 0.4286 | 0.7170 | 13 |
| 0.2338 | 0.5263 | 0.4258 | 0.7899 | 0.2340 | 0.5490 | 0.4329 | 0.7254 | 14 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 341 | 2023-05-03T09:46:32Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- guriko/autotrain-data-resume
co2_eq_emissions:
emissions: 0.0031738020850551043
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 55035128532
- CO2 Emissions (in grams): 0.0032
## Validation Metrics
- Loss: 0.658
- Accuracy: 0.812
- Macro F1: 0.759
- Micro F1: 0.812
- Weighted F1: 0.787
- Macro Precision: 0.884
- Micro Precision: 0.812
- Weighted Precision: 0.856
- Macro Recall: 0.750
- Micro Recall: 0.812
- Weighted Recall: 0.812
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/guriko/autotrain-resume-55035128532
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("guriko/autotrain-resume-55035128532", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("guriko/autotrain-resume-55035128532", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
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