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Captain-1337/CrudeBERT | [
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] | text-classification | {
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} | 28 | 2021-12-03T11:19:24Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
Captain272/lstm | [] | null | {
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} | 0 | null | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
Carlork314/Xd | [] | null | {
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}
} | 0 | null | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
CarlosPR/mt5-spanish-memmories-analysis | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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}
}
} | 7 | 2021-12-03T11:19:44Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
CarlosTron/Yo | [] | null | {
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}
} | 0 | null | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
CasualHomie/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 11 | 2021-11-26T14:56:21Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
Cat/Kitty | [] | null | {
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},
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}
} | 0 | 2021-12-03T11:20:04Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
Cathy/reranking_model | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
} | 27 | 2021-11-26T14:58:06Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
Cdial/hausa-asr | [
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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},
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},
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},
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}
}
} | 8 | 2021-11-26T14:57:30Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
dccuchile/albert-large-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 27 | 2021-07-08T19:37:33Z | ---
language: code
thumbnail: https://doesnotexist.codes/messlab.png
tags:
- programming
- gpt2
- causal-lm
license: cc0-1.0
---
# GPT-CSRC
This is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according to the [Debian Popularity Contest](https://popcon.debian.org/). The source files were deduplicated using a process similar to the OpenWebText preprocessing (basically a locality-sensitive hash to detect near-duplicates). The model was originally trained using [NVIDIA's Megatron-LM](https://github.com/nvidia/Megatron-LM) but has been converted to Huggingface. Note that the tokenizer is *not* the standard GPT2 BPE vocab, but one that has been trained for this dataset; the tokenizer is also available from this repository.
The processed dataset (in JSON format) can be found here: [csrc\_dataset\_large.json.gz](https://moyix.net/~moyix/csrc_dataset_large.json.gz).
This model was used to generate snippets for the web site [This Code Does Not Exist](https://doesnotexist.codes/).
# Usage
```
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("moyix/csrc_774m")
>>> device = torch.device("cuda")
>>> model.to(device)
>>> tokenizer = AutoTokenizer.from_pretrained("moyix/csrc_774m")
>>> prompt = tokenizer.encode('// say hello\nvoid hello() {', return_tensors="pt")
>>> output = model.generate(input_ids=prompt.to(device), max_length=32, num_return_sequences=1, do_sample=True, num_beams=4)
>>> print(tokenizer.decode(output[0].tolist(),clean_up_tokenization_spaces=True))
// say hello
void hello() {
std::cout << "hello" << std::endl;
}
int main() {
``` |
dccuchile/albert-large-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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"max_length": null
},
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},
"text-generation": {
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},
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},
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}
} | 25 | 2021-05-04T18:48:29Z | ---
tags:
- asteroid
- audio
- ConvTasNet
datasets:
- LibriMix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model
Imported from this Zenodo [model page](https://zenodo.org/record/3970768).
## Description:
This model was trained by Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on the `enh_single` task of the Libri3Mix dataset.
## Training config:
```yaml
data:
n_src: 1
sample_rate: 8000
segment: 3
task: enh_single
train_dir: data/wav8k/min/train-360
valid_dir: data/wav8k/min/dev
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 1
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
training:
batch_size: 24
early_stop: True
epochs: 200
half_lr: True
```
## Results:
```yaml
si_sdr: 14.783675142685572
si_sdr_imp: 11.464625198953202
sdr: 15.497505907983102
sdr_imp: 12.07230150154914
sar: 15.497505907983102
sar_imp: 12.07230150154914
stoi: 0.9270030254700518
stoi_imp: 0.1320547197597893
```
## License notice:
This work "ConvTasNet_Libri1Mix_enhsingle_8k"
is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by
[Vassil Panayotov](https://github.com/vdp),
used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
"ConvTasNet_Libri1Mix_enhsingle_8k"
is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente. |
dccuchile/albert-large-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
},
"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": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 5 | 2020-12-07T16:33:40Z | ---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
---
## Asteroid model `mpariente/ConvTasNet_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! dataset.
### Training config:
```yaml
data:
n_src: 2
mode: min
nondefault_nsrc: None
sample_rate: 8000
segment: 3
task: sep_clean
train_dir: data/wav8k/min/tr/
valid_dir: data/wav8k/min/cv/
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
main_args:
exp_dir: exp/wham
gpus: -1
help: None
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 2
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 24
early_stop: True
epochs: 200
half_lr: True
num_workers: 4
```
### Results:
```yaml
si_sdr: 16.21326632846293
si_sdr_imp: 16.21441705664987
sdr: 16.615180021738933
sdr_imp: 16.464137807433435
sir: 26.860503975131923
sir_imp: 26.709461760826414
sar: 17.18312813480803
sar_imp: -131.99332048277296
stoi: 0.9619940905157323
stoi_imp: 0.2239480672473015
```
### License notice:
This work "ConvTasNet_WHAM!_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"ConvTasNet_WHAM!_sepclean" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente. |
dccuchile/albert-large-spanish-finetuned-xnli | [
"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,
"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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},
"translation_en_to_fr": {
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}
}
} | 29 | null | ---
tags:
- asteroid
- audio
- DPRNNTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
---
## Asteroid model `mpariente/DPRNNTasNet-ks2_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)
### Description:
This model was trained by Manuel Pariente
using the wham/DPRNN recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! dataset.
### Training config:
```yaml
data:
mode: min
nondefault_nsrc: None
sample_rate: 8000
segment: 2.0
task: sep_clean
train_dir: data/wav8k/min/tr
valid_dir: data/wav8k/min/cv
filterbank:
kernel_size: 2
n_filters: 64
stride: 1
main_args:
exp_dir: exp/train_dprnn_new/
gpus: -1
help: None
masknet:
bidirectional: True
bn_chan: 128
chunk_size: 250
dropout: 0
hid_size: 128
hop_size: 125
in_chan: 64
mask_act: sigmoid
n_repeats: 6
n_src: 2
out_chan: 64
optim:
lr: 0.001
optimizer: adam
weight_decay: 1e-05
positional arguments:
training:
batch_size: 3
early_stop: True
epochs: 200
gradient_clipping: 5
half_lr: True
num_workers: 8
```
### Results:
```yaml
si_sdr: 19.316743490695334
si_sdr_imp: 19.317895273889842
sdr: 19.68085347190952
sdr_imp: 19.5298092932871
sir: 30.362213998701232
sir_imp: 30.21116982007881
sar: 20.15553251343315
sar_imp: -129.02091762351188
stoi: 0.97772664309074
stoi_imp: 0.23968091518217424
```
### License notice:
This work "DPRNNTasNet-ks2_WHAM_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"DPRNNTasNet-ks2_WHAM_sepclean" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.
|
dccuchile/albert-tiny-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
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} | 32 | 2022-02-07T19:50:46Z | ---
license: apache-2.0
language: tr
tags:
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- common_voice
model-index:
- name: mpoyraz/wav2vec2-xls-r-300m-cv6-turkish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6.1
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
value: 8.83
- name: Test CER
type: cer
value: 2.37
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: tr
metrics:
- name: Test WER
type: wer
value: 32.81
- name: Test CER
type: cer
value: 11.22
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: tr
metrics:
- name: Test WER
type: wer
value: 34.86
---
# wav2vec2-xls-r-300m-cv6-turkish
## Model description
This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 6.1 TR](https://huggingface.co/datasets/common_voice) All `validated` split except `test` split was used for training.
- [MediaSpeech](https://www.openslr.org/108/)
## Training procedure
To support both of the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
### Training hyperparameters
The following hypermaters were used for finetuning:
- learning_rate 2e-4
- num_train_epochs 10
- warmup_steps 500
- freeze_feature_extractor
- mask_time_prob 0.1
- mask_feature_prob 0.1
- feat_proj_dropout 0.05
- attention_dropout 0.05
- final_dropout 0.1
- activation_dropout 0.05
- per_device_train_batch_size 8
- per_device_eval_batch_size 8
- gradient_accumulation_steps 8
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 1.18.3
- Tokenizers 0.10.3
## Language Model
N-gram language model is trained on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
## Evaluation Commands
Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
1. To evaluate on `common_voice` with split `test`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset common_voice --config tr --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
## Evaluation results:
| Dataset | WER | CER |
|---|---|---|
|Common Voice 6.1 TR test split| 8.83 | 2.37 |
|Speech Recognition Community dev data| 32.81 | 11.22 |
|
dccuchile/albert-tiny-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
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} | 29 | 2022-02-05T17:10:49Z | ---
license: apache-2.0
language: tr
tags:
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- tr
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: mpoyraz/wav2vec2-xls-r-300m-cv8-turkish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: tr
metrics:
- name: Test WER
type: wer
value: 10.61
- name: Test CER
type: cer
value: 2.67
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: tr
metrics:
- name: Test WER
type: wer
value: 36.46
- name: Test CER
type: cer
value: 12.38
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: tr
metrics:
- name: Test WER
type: wer
value: 40.91
---
# wav2vec2-xls-r-300m-cv8-turkish
## Model description
This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 8.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) All `validated` split except `test` split was used for training.
## Training procedure
To support the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
### Training hyperparameters
The following hypermaters were used for finetuning:
- learning_rate 2.5e-4
- num_train_epochs 20
- warmup_steps 500
- freeze_feature_extractor
- mask_time_prob 0.1
- mask_feature_prob 0.1
- feat_proj_dropout 0.05
- attention_dropout 0.05
- final_dropout 0.1
- activation_dropout 0.05
- per_device_train_batch_size 8
- per_device_eval_batch_size 8
- gradient_accumulation_steps 8
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
## Language Model
N-gram language model is trained on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
## Evaluation Commands
Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv8-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
## Evaluation results:
| Dataset | WER | CER |
|---|---|---|
|Common Voice 8 TR test split| 10.61 | 2.67 |
|Speech Recognition Community dev data| 36.46 | 12.38 |
|
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
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}
} | 26 | 2022-01-20T10:08:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model_index:
- name: run1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Bleu
type: bleu
value: 8.4217
---
<!-- 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. -->
# run1
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1740
- Bleu: 8.4217
- Gen Len: 15.9457
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 250 | 4.2342 | 0.8889 | 83.4022 |
| 4.6818 | 2.0 | 500 | 3.7009 | 4.1671 | 35.587 |
| 4.6818 | 3.0 | 750 | 3.4737 | 7.6414 | 23.9674 |
| 3.4911 | 4.0 | 1000 | 3.3713 | 7.7512 | 18.6957 |
| 3.4911 | 5.0 | 1250 | 3.2689 | 8.0901 | 19.4674 |
| 3.0164 | 6.0 | 1500 | 3.2194 | 8.5708 | 25.0543 |
| 3.0164 | 7.0 | 1750 | 3.1853 | 9.5275 | 23.9239 |
| 2.6954 | 8.0 | 2000 | 3.1562 | 8.5635 | 18.9674 |
| 2.6954 | 9.0 | 2250 | 3.1564 | 8.2031 | 17.5978 |
| 2.4503 | 10.0 | 2500 | 3.1314 | 8.5638 | 18.1522 |
| 2.4503 | 11.0 | 2750 | 3.1511 | 8.8428 | 17.913 |
| 2.2554 | 12.0 | 3000 | 3.1513 | 8.1244 | 17.0 |
| 2.2554 | 13.0 | 3250 | 3.1664 | 8.0157 | 16.2717 |
| 2.1202 | 14.0 | 3500 | 3.1656 | 8.7758 | 16.6087 |
| 2.1202 | 15.0 | 3750 | 3.1550 | 8.4637 | 16.4565 |
| 2.0082 | 16.0 | 4000 | 3.1702 | 8.2488 | 15.8587 |
| 2.0082 | 17.0 | 4250 | 3.1725 | 8.609 | 16.3043 |
| 1.9274 | 18.0 | 4500 | 3.1750 | 8.4476 | 15.8043 |
| 1.9274 | 19.0 | 4750 | 3.1734 | 8.4753 | 16.5543 |
| 1.888 | 20.0 | 5000 | 3.1740 | 8.4217 | 15.9457 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.1.dev0
- Tokenizers 0.10.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-xnli | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
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"DistilBertForSequenceClassification"
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}
} | 31 | 2022-02-12T09:34:19Z | ---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# TODO: Fill this model card
|
Chaddmckay/Cdm | [] | null | {
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} | 0 | null | ---
language: es
thumbnail: https://i.imgur.com/jgBdimh.png
---
# BETO (Spanish BERT) + Spanish SQuAD2.0
This model is provided by [BETO team](https://github.com/dccuchile/beto) and fine-tuned on [SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve) for **Q&A** downstream task.
## Details of the language model('dccuchile/bert-base-spanish-wwm-cased')
Language model ([**'dccuchile/bert-base-spanish-wwm-cased'**](https://github.com/dccuchile/beto/blob/master/README.md)):
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with [Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) as well as other (not BERT-based) models.
## Details of the downstream task (Q&A) - Dataset
[SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve)
| Dataset | # Q&A |
| ---------------------- | ----- |
| SQuAD2.0 Train | 130 K |
| SQuAD2.0-es-v2.0 | 111 K |
| SQuAD2.0 Dev | 12 K |
| SQuAD-es-v2.0-small Dev| 69 K |
## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
export SQUAD_DIR=path/to/nl_squad
python transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
--do_train \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train_nl-v2.0.json \
--predict_file $SQUAD_DIR/dev_nl-v2.0.json \
--per_gpu_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2.0 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/model_output \
--save_steps 5000 \
--threads 4 \
--version_2_with_negative
```
## Results:
| Metric | # Value |
| ---------------------- | ----- |
| **Exact** | **76.50**50 |
| **F1** | **86.07**81 |
```json
{
"exact": 76.50501430594491,
"f1": 86.07818773108252,
"total": 69202,
"HasAns_exact": 67.93020719738277,
"HasAns_f1": 82.37912207996466,
"HasAns_total": 45850,
"NoAns_exact": 93.34104145255225,
"NoAns_f1": 93.34104145255225,
"NoAns_total": 23352,
"best_exact": 76.51223953064941,
"best_exact_thresh": 0.0,
"best_f1": 86.08541295578848,
"best_f1_thresh": 0.0
}
```
### Model in action (in a Colab Notebook)
<details>
1. Set the context and ask some questions:
2. Run predictions:
</details>
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
ClaudeYang/awesome_fb_model | [
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
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}
} | 26 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- type: accuracy
value: 0.9150649826102873
name: Accuracy
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: qnli
split: validation
metrics:
- type: accuracy
value: 0.914881933003844
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDY2NmRlOTEyMzkwMjc5MjVjZDY3MTczMmM2ZTEyZTFiMTk1YmJiYjkxYmYyYTAzNDlhOTU5OTMzZjhhMjkyMSIsInZlcnNpb24iOjF9.aoHEeaQLKI4uwmTgp8Lo9zRoParcSlyDiXZiRrWTqZJIMHgwKgQg52zvYYrZ9HMjjIvWjdW9G_s_DfxqBoekDA
- type: precision
value: 0.9195906432748538
name: Precision
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGMyMjUyNTliOWZjMzkzM2Y3YWU0ODhiNDcyOTAwZjYyZjRiNGQ5NTgyODM4Y2VjNGRlYzNkNTViNmJhNzM0ZSIsInZlcnNpb24iOjF9.fJdQ7M46RGvp_uXk9jvBpl0RFAIGTRAtk8bRQGjNn_uy5weBm6tENL-OclZHwG4uU6LviGTdXmAwn5Ba37hNBw
- type: recall
value: 0.9112640347700108
name: Recall
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Y2ZmIyZTMzMzM1MTc1OWQ0YWI2ZjU2MzQ5NGU1M2FjNDRiOWViM2NkNWU2M2UzZjljMDJjNmUzZTQ1YWM2MiIsInZlcnNpb24iOjF9.6kVxEkJ-Fojy9HgMevsHovimj3IYp97WO2991zQOFN8nEpPc0hThFk5kMRotS-jPSLFh0mS2PVhQ5x3HIo17Ag
- type: auc
value: 0.9718281171793548
name: AUC
verified: true
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value: 0.9154084045843187
name: F1
verified: true
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value: 0.21421395242214203
name: loss
verified: true
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---
<!-- 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. -->
# DeBERTa-v3-small fine-tuned on QNLI
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2143
- Accuracy: 0.9151
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2823 | 1.0 | 6547 | 0.2143 | 0.9151 |
| 0.1996 | 2.0 | 13094 | 0.2760 | 0.9103 |
| 0.1327 | 3.0 | 19641 | 0.3293 | 0.9169 |
| 0.0811 | 4.0 | 26188 | 0.4278 | 0.9193 |
| 0.05 | 5.0 | 32735 | 0.5110 | 0.9176 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CleveGreen/FieldClassifier_v2_gpt | [
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} | 26 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- type: accuracy
value: 0.9403669724770642
name: Accuracy
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
- type: accuracy
value: 0.9403669724770642
name: Accuracy
verified: true
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- type: precision
value: 0.9375
name: Precision
verified: true
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- type: recall
value: 0.9459459459459459
name: Recall
verified: true
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- type: auc
value: 0.9804217184474193
name: AUC
verified: true
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- type: f1
value: 0.9417040358744394
name: F1
verified: true
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value: 0.21338027715682983
name: loss
verified: true
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---
<!-- 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. -->
# DeBERTa v3 (small) fine-tuned on SST2
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2134
- Accuracy: 0.9404
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.176 | 1.0 | 4210 | 0.2134 | 0.9404 |
| 0.1254 | 2.0 | 8420 | 0.2362 | 0.9415 |
| 0.0957 | 3.0 | 12630 | 0.3187 | 0.9335 |
| 0.0673 | 4.0 | 16840 | 0.3039 | 0.9266 |
| 0.0457 | 5.0 | 21050 | 0.3521 | 0.9312 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
CleveGreen/JobClassifier | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 31 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: deberta-v3-snall-goemotions
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. -->
# deberta-v3-snall-goemotions
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5638
- F1: 0.4241
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.614 | 1.0 | 3082 | 1.5577 | 0.3663 |
| 1.4338 | 2.0 | 6164 | 1.5580 | 0.4084 |
| 1.2936 | 3.0 | 9246 | 1.5006 | 0.4179 |
| 1.1531 | 4.0 | 12328 | 1.5348 | 0.4276 |
| 1.0536 | 5.0 | 15410 | 1.5638 | 0.4241 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
CodeNinja1126/bert-p-encoder | [
"pytorch"
] | null | {
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} | 3 | null | ---
language: en
tags:
- wsb
- tweets
widget:
- text: "Come on guys this is"
---
# distilGPT-2 fine-tuned on Kaggle WSB Reddit posts dataset |
CogComp/roberta-temporal-predictor | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.00436",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 14 | null | ---
language: es
thumbnail: https://i.imgur.com/uxAvBfh.png
tags:
- Spanish
- Electra
datasets:
-large_spanish_corpus
---
## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
**Electricidad-base-discriminator** (uncased) is a ```base``` Electra like model (discriminator in this case) trained on a [Large Spanish Corpus](https://github.com/josecannete/spanish-corpora) (aka BETO's corpus)
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
## Model details ⚙
|Name| # Value|
|-----|--------|
|Layers| 12 |
|Hidden | 768 |
|Params| 110M |
## Evaluation metrics (for discriminator) 🧾
|Metric | # Score |
|-------|---------|
|Accuracy| 0.985|
|Precision| 0.726|
|AUC | 0.922|
## Fast example of usage 🚀
```python
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-base-discriminator")
tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-base-discriminator")
sentence = "El rápido zorro marrón salta sobre el perro perezoso"
fake_sentence = "El rápido zorro marrón amar sobre el perro perezoso"
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
[print("%7s" % prediction, end="") for prediction in predictions.tolist()]
# Output:
'''
el rapido zorro marro ##n amar sobre el perro pere ##zoso 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0[None, None, None, None, None, None, None, None, None, None, None, None, None
'''
```
As you can see there are **1s** in the places where the model detected a fake token. So, it works! 🎉
### Some models fine-tuned on a downstream task 🛠️
[Question Answering](https://huggingface.co/mrm8488/electricidad-base-finetuned-squadv1-es)
[POS](https://huggingface.co/mrm8488/electricidad-base-finetuned-pos)
[NER](https://huggingface.co/mrm8488/electricidad-base-finetuned-ner)
### Spanish LM model comparison 📊
| Dataset | Metric | RoBERTa-b | RoBERTa-l | BETO | mBERT | BERTIN | Electricidad-b |
|-------------|----------|-----------|-----------|--------|--------|--------|---------|
| UD-POS | F1 | 0.9907 | 0.9901 | 0.9900 | 0.9886 | 0.9904 | 0.9818 |
| Conll-NER | F1 | 0.8851 | 0.8772 | 0.8759 | 0.8691 | 0.8627 | 0.7954 |
| Capitel-POS | F1 | 0.9846 | 0.9851 | 0.9836 | 0.9839 | 0.9826 | 0.9816 |
| Capitel-NER | F1 | 0.8959 | 0.8998 | 0.8771 | 0.8810 | 0.8741 | 0.8035 |
| STS | Combined | 0.8423 | 0.8420 | 0.8216 | 0.8249 | 0.7822 | 0.8065 |
| MLDoc | Accuracy | 0.9595 | 0.9600 | 0.9650 | 0.9560 | 0.9673 | 0.9490 |
| PAWS-X | F1 | 0.9035 | 0.9000 | 0.8915 | 0.9020 | 0.8820 | **0.9045** |
| XNLI | Accuracy | 0.8016 | 0.7958 | 0.8130 | 0.7876 | 0.7864 | 0.7878 |
## Acknowledgments
I thank [🤗/transformers team](https://github.com/huggingface/transformers) for allowing me to train the model (specially to [Julien Chaumond](https://twitter.com/julien_c)).
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{mromero2020electricidad-base-discriminator,
title={Spanish Electra by Manuel Romero},
author={Romero, Manuel},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/mrm8488/electricidad-base-discriminator/}},
year={2020}
}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
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} | 0 | null | ---
lang: 'es'
widget:
- text: "TUMOR DE COMPORTAMIENTO INCIERTO O DESCONOCIDO DEL HNGADO, DE LA VESNCULA BILIAR Y DEL CONDUCTO BILIAR - DiagnNstico Principal - Z01.8 OTROS EXNMENES ESPECIALES ESPECIFICADOS"
---
# Electricidad (base) fine-tuned medical diagnostics |
Contrastive-Tension/BERT-Distil-CT-STSb | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
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} | 1 | null | ---
language: en
thumbnail:
widget:
- text: "HuggingFace Cake:"
---
|
Contrastive-Tension/BERT-Distil-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
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}
}
} | 9 | null | ---
language: en
thumbnail:
widget:
- text: "HuggingFace Cake:"
---
|
Contrastive-Tension/BERT-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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}
} | 7 | null | # GPT2-IMDB-neg (LM + RL) 🎞😡✍
All credits to [@lvwerra](https://twitter.com/lvwerra)
## What is it?
A small GPT2 (`lvwerra/gpt2-imdb`) language model fine-tuned to produce **negative** movie reviews based the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). The model is trained with rewards from a BERT sentiment classifier (`lvwerra/gpt2-imdb`) via **PPO**.
## Why?
I wanted to reproduce the experiment [lvwerra/gpt2-imdb-pos](https://huggingface.co/lvwerra/gpt2-imdb-pos) but for generating **negative** movie reviews.
## Training setting
The model was trained for `100` optimisation steps with a batch size of `256` which corresponds to `25600` training samples. The full experiment setup (for positive samples) in [trl repo](https://lvwerra.github.io/trl/04-gpt2-sentiment-ppo-training/).
## Examples
A few examples of the model response to a query before and after optimisation:
| query | response (before) | response (after) | rewards (before) | rewards (after) |
|-------|-------------------|------------------|------------------|-----------------|
|This movie is a fine | attempt as far as live action is concerned, n...|example of how bad Hollywood in theatrics pla...| 2.118391 | -3.31625|
|I have watched 3 episodes |with this guy and he is such a talented actor...| but the show is just plain awful and there ne...| 2.681171| -4.512792|
|We know that firefighters and| police officers are forced to become populari...| other chains have going to get this disaster ...| 1.367811| -3.34017|
## Training logs and metrics <img src="https://gblobscdn.gitbook.com/spaces%2F-Lqya5RvLedGEWPhtkjU%2Favatar.png?alt=media" width="25" height="25">
Watch the whole training logs and metrics on [W&B](https://app.wandb.ai/mrm8488/gpt2-sentiment-negative?workspace=user-mrm8488)
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
Coolhand/Sentiment | [] | null | {
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} | 0 | null | ---
language: es
tags:
- Long documents
- LongFormer
- QA
- Q&A
datasets:
- BSC-TeMU/SQAC
---
# Spanish Longformer fine-tuned on **SQAC** for Spanish **QA** 📖❓
[longformer-base-4096-spanish](https://huggingface.co/mrm8488/longformer-base-4096-spanish) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task.
## Details of the model 🧠
[longformer-base-4096-spanish](https://huggingface.co/mrm8488/longformer-base-4096-spanish) is a BERT-like model started from the RoBERTa checkpoint (**BERTIN** in this case) and pre-trained for *MLM* on long documents (from BETO's `all_wikis`). It supports sequences of length up to **4,096**!
## Details of the dataset 📚
This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence](https://creativecommons.org/licenses/by/4.0/legalcode).
This dataset can be used to build extractive-QA.
## Evaluation Metrics 📈
TBA
## Fast Usage with HF `pipeline` 🧪
```py
from transformers import pipeline
qa_pipe = pipeline("question-answering", model='mrm8488/longformer-base-4096-spanish-finetuned-squad')
context = '''
Hace aproximadamente un año, Hugging Face, una startup de procesamiento de lenguaje natural con sede en Brooklyn, Nueva York, lanzó BigScience, un proyecto internacional con más de 900 investigadores que está diseñado para comprender mejor y mejorar la calidad de los grandes modelos de lenguaje natural. Los modelos de lenguaje grande (LLM), algoritmos que pueden reconocer, predecir y generar lenguaje sobre la base de conjuntos de datos basados en texto, han captado la atención de empresarios y entusiastas de la tecnología por igual. Pero el costoso hardware requerido para desarrollar LLM los ha mantenido en gran medida fuera del alcance de los investigadores sin los recursos de compañías como OpenAI y DeepMind detrás de ellos.
Inspirándose en organizaciones como la Organización Europea para la Investigación Nuclear (también conocida como CERN) y el Gran Colisionador de Hadrones, el objetivo de BigScience es crear LLM y grandes conjuntos de datos de texto que eventualmente serán de código abierto para la IA más amplia. comunidad. Los modelos serán entrenados en la supercomputadora Jean Zay ubicada cerca de París, Francia, que se encuentra entre las máquinas más poderosas del mundo.
'''
question = "¿Cuál es el objetivo de BigScience?"
qa_pipe({'context':context, 'question': question})
# It outpus
```
```js
{'answer': 'comprender mejor y mejorar la calidad de los grandes modelos de lenguaje natural.',
'end': 305,
'score': 0.9999799728393555,
'start': 224}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Narrativa](https://www.narrativa.com/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
CopymySkill/DialoGPT-medium-atakan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 7 | null | ---
language:
- es
license: mit
widget:
- text: "Manuel Romero ha creado con el equipo de BERTIN un modelo que procesa documentos <mask> largos."
tags:
- Long documents
- longformer
- bertin
- spanish
datasets:
- spanish_large_corpus
---
# longformer-base-4096-spanish
## [Longformer](https://arxiv.org/abs/2004.05150) is a Transformer model for long documents.
`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint (**BERTIN** in this case) and pre-trained for *MLM* on long documents (from BETO's `all_wikis`). It supports sequences of length up to 4,096!
**Longformer** uses a combination of a sliding window (*local*) attention and *global* attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.
This model was made following the research done by [Iz Beltagy and Matthew E. Peters and Arman Cohan](https://arxiv.org/abs/2004.05150).
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{mromero2022longformer-base-4096-spanish,
title={Spanish LongFormer by Manuel Romero},
author={Romero, Manuel},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/mrm8488/longformer-base-4096-spanish}},
year={2022}
}
``` |
CouchCat/ma_ner_v7_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] | token-classification | {
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}
}
} | 13 | null | ---
tags:
- translation
language:
- es
- en
datasets:
- opus100
---
### mbart-large-es-en
This is mbart-large-cc25, finetuned on opus100 for Spanish to English translation.
It scores BLEU **28.25** on validation dataset
It scores BLEU **28.28** on test
dataset |
Coverage/sakurajimamai | [] | null | {
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}
} | 0 | 2020-07-29T12:41:23Z | ---
language: en
tags:
- mobilebert
- pos
license: mit
---
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2 | [] | null | {
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}
} | 0 | null | ---
language: es
tags:
- GPT-2
datasets:
- large_spanish_corpus
widgets:
- text: "Érase un vez un"
license: mit
---
# Spanish GPT-2 trained on [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus)
This is a Spanish GPT-2 model trained from scratch on the [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus) aka BETO's corpus with [Flax](https://github.com/google/flax)
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Dataset
The dataset is about 20 GB. 95% of the data was used for training and the rest 5% for validation.
## Metrics (on evaluation dataset)
- Loss: 2.413
- Perplexity: 11.36
## Team members
- Manuel Romero ([mrm8488](https://huggingface.co/mrm8488))
- María Grandury ([mariagrandury](https://huggingface.co/))
- Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps))
- Daniel Vera ([daveni](https://huggingface.co/daveni))
- Sri Lakshmi ([srisweet](https://huggingface.co/srisweet))
- José Posada ([jdposa](https://huggingface.co/jdposa))
- Santiago Hincapie ([shpotes](https://huggingface.co/shpotes))
- Jorge ([jorgealro](https://huggingface.co/jorgealro))
## Useful links
- [Community Week timeline](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104#summary-timeline-calendar-6)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Community Week thread](https://discuss.huggingface.co/t/pretrain-gpt2-from-scratch-in-spanish/7086/8) |
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2 | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 1 | null | ---
language: es
tags:
- QA
- Q&A
datasets:
- BSC-TeMU/SQAC
widget:
- text: "question: ¿Cuál es el nombre que se le da a la unidad morfológica y funcional de los seres vivos? context: La célula (del latín cellula, diminutivo de cella, ‘celda’) es la unidad morfológica y funcional de todo ser vivo. De hecho, la célula es el elemento de menor tamaño que puede considerarse vivo.\u200b De este modo, puede clasificarse a los organismos vivos según el número de células que posean: si solo tienen una, se les denomina unicelulares (como pueden ser los protozoos o las bacterias, organismos microscópicos); si poseen más, se les llama pluricelulares. En estos últimos el número de células es variable: de unos pocos cientos, como en algunos nematodos, a cientos de billones (1014), como en el caso del ser humano. Las células suelen poseer un tamaño de 10 µm y una masa de 1 ng, si bien existen células mucho mayores."
---
# Spanish T5 (small) fine-tuned on **SQAC** for Spanish **QA** 📖❓
[spanish-T5-small](https://huggingface.co/flax-community/spanish-t5-small) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task.
## Details of Spanish T5 (small)
T5 (small) like arch trained from scatch on [large_spanish_corpus](https://huggingface.co/datasets/large_spanish_corpus) for **HuggingFace/Flax/Jax Week**.
## Details of the dataset 📚
This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence](https://creativecommons.org/licenses/by/4.0/legalcode).
This dataset can be used to build extractive-QA.
## Results on test dataset 📝
| Metric | # Value |
| ------ | --------- |
| **BLEU** | **41.94** |
## Model in Action 🚀
```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
ckpt = 'mrm8488/spanish-t5-small-sqac-for-qa'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = T5ForConditionalGeneration.from_pretrained(ckpt).to(device)
def get_answer(question, context):
input_text = 'question: %s context: %s' % (question, context)
features = tokenizer([input_text ], padding='max_length', truncation=True, max_length=512, return_tensors='pt')
output = model.generate(input_ids=features['input_ids'].to(device),
attention_mask=features['attention_mask'].to(device))
return tokenizer.decode(output[0], skip_special_tokens=True)
context = '''
La ex codirectora del grupo de investigación de IA ética de Google, Margaret Mitchell,
quien fue despedida en febrero después de una controversia sobre un artículo crítico del que fue coautora,
se unirá a HuggingFace para ayudar a que los algoritmos de IA sean más justos.
'''
question = '¿Qué hará Margaret Mitchell en HuggingFace?'
print(get_answer(context, question))
# ayudar a que los algoritmos de ia sean más justos
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Narrativa](https://www.narrativa.com/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
] | fill-mask | {
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} | 14 | null | ---
language: en
datasets:
- qasc
---
# T5-base fine-tuned on QASC
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QASC](https://allenai.org/data/qasc) for **QA** (via *sentence composition*) downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the dataset 📚
**Question Answering via Sentence Composition** (QASC) is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28). The **context** passed to the *encoder* is the combination of the 2 *facts* (`fact1` and `fact2`). The **question** is just the `formatted_question` field. The **answer** passed to the *decoder* is the`text` right answer instead of the `label` (A, B, C... See `choices` field). More details about the dataset format/fields [here](https://huggingface.co/nlp/viewer/?dataset=qasc)
## Metrics on validation set 📋
| Metric | Score |
|--------|-------|
|Accuracy (EM) | **97.73**|
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-qasc")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-qasc")
def get_response(question, context, max_length=64):
input_text = 'question: %s context: %s' % (question, context)
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'],
max_length=max_length)
return tokenizer.decode(output[0])
fact_1 = 'a watch is used for measuring time'
fact_2 = 'Times are measured in seconds.'
context = fact_1 + ' ' + fact_2
question = 'What can be used to measure seconds? (A) Watch (B) seconds (C) fluid (D) Ruler (E) goggles (F) glasses (G) Drill (H) Scale'
get_response(question, context)
# output: 'Watch'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
DJSammy/bert-base-swedish-uncased_BotXO-ai | [
"pytorch",
"transformers"
] | null | {
"architectures": null,
"model_type": null,
"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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},
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},
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},
"translation_en_to_ro": {
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}
}
} | 1 | 2020-11-02T23:29:18Z | ---
language: en
datasets:
- quarel
---
# T5-base fine-tuned on QuaRel
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QuaRel](https://allenai.org/data/quarel) for **QA** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the dataset 📚
**QuaRel**: *[A Dataset and Models for Answering Questions about Qualitative Relationships](https://www.semanticscholar.org/paper/QuaRel%3A-A-Dataset-and-Models-for-Answering-about-Tafjord-Clark/51004bc6461a572e1189a0e3b32b441155d760ce)*
Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of mapping questions into those models has formidable challenges. We present QuaRel, a dataset of diverse story questions involving qualitative relationships that characterize these challenges, and techniques that begin to address them. The dataset has 2771 questions relating 19 different types of quantities. For example, "Jenny observes that the robot vacuum cleaner moves slower on the living room carpet than on the bedroom carpet. Which carpet has more friction?" We contribute (1) a simple and flexible conceptual framework for representing these kinds of questions; (2) the QuaRel dataset, including logical forms, exemplifying the parsing challenges; and (3) two novel models for this task, built as extensions of type-constrained semantic parsing. The first of these models (called QuaSP+) significantly outperforms off-the-shelf tools on QuaRel. The second (QuaSP+Zero) demonstrates zero-shot capability, i.e., the ability to handle new qualitative relationships without requiring additional training data, something not possible with previous models. This work thus makes inroads into answering complex, qualitative questions that require reasoning, and scaling to new relationships at low cost
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28). The **context** passed to the *encoder* is the `logical_form_pretty` field (example: `qrel(speed, higher, ice) -> qrel(smoothness, higher, snow) ; qrel(smoothness, higher, ice`) . The **question** is just the `question` field. The **answer** passed to the *decoder* is obtained from `question`using the `answer_index` field. More details about the dataset format/fields [here](https://huggingface.co/nlp/viewer/?dataset=quarel)
## Metrics on validation set 📋
| Metric | Score |
|--------|-------|
|Accuracy (EM) | **67.98**|
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-quarel")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-quarel")
def get_response(question, context, max_length=32):
input_text = 'question: %s context: %s' % (question, context)
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'],
max_length=max_length)
return tokenizer.decode(output[0])
question = 'As the train left the station it crossed the bridge and being farther away it looked (A) larger (B) smaller'
context = 'qrel(distance, higher, Train on a bridge) -> qrel(apparentSize, higher, Train on a bridge) ; qrel(apparentSize, lower, Train on a bridge)'
get_response(question, context)
# output: 'smaller'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"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,
"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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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 29 | 2020-06-04T19:37:28Z | ---
language: en
tags:
- news
- summary
---
# T5-base fine-tuned fo News Summarization 📖✏️🧾
All credits to [Abhishek Kumar Mishra](https://github.com/abhimishra91)
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [News Summary](https://www.kaggle.com/sunnysai12345/news-summary) dataset for **summarization** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (Summarization) - Dataset 📚
[News Summary](https://www.kaggle.com/sunnysai12345/news-summary)
The dataset consists of **4515 examples** and contains Author_name, Headlines, Url of Article, Short text, Complete Article. I gathered the summarized news from Inshorts and only scraped the news articles from Hindu, Indian times and Guardian. Time period ranges from febrauary to august 2017.
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this Colab Notebook](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb) created by [Abhishek Kumar Mishra](https://github.com/abhimishra91), so all credits to him!
I also trained the model for more epochs (6).
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
def summarize(text, max_length=150):
input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)
generated_ids = model.generate(input_ids=input_ids, num_beams=2, max_length=max_length, repetition_penalty=2.5, length_penalty=1.0, early_stopping=True)
preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
return preds[0]
```
Given the following article from **NYT** (2020/06/09) with title *George Floyd’s death energized a movement. He will be buried in Houston today*:
After the sound and the fury, weeks of demonstrations and anguished calls for racial justice, the man whose death gave rise to an international movement, and whose last words — “I can’t breathe” — have been a rallying cry, will be laid to rest on Tuesday at a private funeral in Houston.George Floyd, who was 46, will then be buried in a grave next to his mother’s.The service, scheduled to begin at 11 a.m. at the Fountain of Praise church, comes after five days of public memorials in Minneapolis, North Carolina and Houston and two weeks after a Minneapolis police officer was caught on video pressing his knee into Mr. Floyd’s neck for nearly nine minutes before Mr. Floyd died. That officer, Derek Chauvin, has been charged with second-degree murder and second-degree manslaughter. His bail was set at $1.25 million in a court appearance on Monday. The outpouring of anger and outrage after Mr. Floyd’s death — and the speed at which protests spread from tense, chaotic demonstrations in the city where he died to an international movement from Rome to Rio de Janeiro — has reflected the depth of frustration borne of years of watching black people die at the hands of the police or vigilantes while calls for change went unmet.
```
summarize('After the sound and the fury, weeks of demonstrations and anguished calls for racial justice, the man whose death gave rise to an international movement, and whose last words — “I can’t breathe” — have been a rallying cry, will be laid to rest on Tuesday at a private funeral in Houston.George Floyd, who was 46, will then be buried in a grave next to his mother’s.The service, scheduled to begin at 11 a.m. at the Fountain of Praise church, comes after five days of public memorials in Minneapolis, North Carolina and Houston and two weeks after a Minneapolis police officer was caught on video pressing his knee into Mr. Floyd’s neck for nearly nine minutes before Mr. Floyd died. That officer, Derek Chauvin, has been charged with second-degree murder and second-degree manslaughter. His bail was set at $1.25 million in a court appearance on Monday. The outpouring of anger and outrage after Mr. Floyd’s death — and the speed at which protests spread from tense, chaotic demonstrations in the city where he died to an international movement from Rome to Rio de Janeiro — has reflected the depth of frustration borne of years of watching black people die at the hands of the police or vigilantes while calls for change went unmet.', 80)
```
We would obtain:
At a private funeral in Houston. Floyd, who was 46 years old when his death occurred, will be buried next to the grave of his mother. A Minnesota police officer was caught on video pressing his knee into Mr's neck for nearly nine minutes before his death. The officer has been charged with second-degree manslaughter and $1.2 million bail is set at
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
DTAI-KULeuven/robbertje-1-gb-merged | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"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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"prefix": null
},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 1 | 2020-07-18T11:18:43Z | ---
language: en
datasets:
- wikisql
---
# T5-base fine-tuned on WikiSQL for SQL to English translation
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **SQL** to **English** **translation** task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the Dataset 📚
Dataset ID: ```wikisql``` from [Huggingface/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| wikisql | train | 56355 |
| wikisql | valid | 14436 |
How to load it from [nlp](https://github.com/huggingface/nlp)
```python
train_dataset = nlp.load_dataset('wikisql', split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('wikisql', split=nlp.Split.VALIDATION)
```
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL-sql-to-en")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL-sql-to-en")
def get_explanation(query):
input_text = "translate Sql to English: %s </s>" % query
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'])
return tokenizer.decode(output[0])
query = "SELECT COUNT Params form model where location=HF-Hub"
get_explanation(query)
# output: 'How many parameters form model for HF-hub?'
```
Play with it in a Colab:
<img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg">
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
DTAI-KULeuven/robbertje-1-gb-non-shuffled | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"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": {
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},
"translation_en_to_ro": {
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}
}
} | 53 | 2020-07-14T16:34:40Z | ---
language: en
datasets:
- wikisql
widget:
- text: >-
translate English to SQL: How many models were finetuned using BERT as base
model?
license: apache-2.0
---
# T5-base fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **English** to **SQL** **translation**.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the Dataset 📚
Dataset ID: ```wikisql``` from [Huggingface/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| wikisql | train | 56355 |
| wikisql | valid | 14436 |
How to load it from [nlp](https://github.com/huggingface/nlp)
```python
train_dataset = nlp.load_dataset('wikisql', split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('wikisql', split=nlp.Split.VALIDATION)
```
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
def get_sql(query):
input_text = "translate English to SQL: %s </s>" % query
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'])
return tokenizer.decode(output[0])
query = "How many models were finetuned using BERT as base model?"
get_sql(query)
# output: 'SELECT COUNT Model fine tuned FROM table WHERE Base model = BERT'
```
Other examples from validation dataset:
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain |
alexandrainst/da-hatespeech-detection-small | [
"pytorch",
"electra",
"text-classification",
"da",
"transformers",
"license:cc-by-4.0"
] | text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 1,506 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- tex2log
- log2tex
- foc
widget:
- text: "translate to nl: all x1.(_explanation(x1) -> -_equal(x1))"
- text: "translate to fol: All chains are bad."
model-index:
- name: t5-small-text2log
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. -->
# T5 (small) fine-tuned on Text2Log
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an Text2Log dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0313
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.0749 | 1.0 | 21661 | 0.0509 |
| 0.0564 | 2.0 | 43322 | 0.0396 |
| 0.0494 | 3.0 | 64983 | 0.0353 |
| 0.0425 | 4.0 | 86644 | 0.0332 |
| 0.04 | 5.0 | 108305 | 0.0320 |
| 0.0381 | 6.0 | 129966 | 0.0313 |
### Usage:
```py
from transformers import AutoTokenizer, T5ForConditionalGeneration
MODEL_CKPT = "mrm8488/t5-small-finetuned-text2log"
model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT).to(device)
tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)
def translate(text):
inputs = tokenizer(text, padding="longest", max_length=64, return_tensors="pt")
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask, early_stopping=False, max_length=64)
return tokenizer.decode(output[0], skip_special_tokens=True)
prompt_nl_to_fol = "translate to fol: "
prompt_fol_to_nl = "translate to nl: "
example_1 = "Every killer leaves something."
example_2 = "all x1.(_woman(x1) -> exists x2.(_emotion(x2) & _experience(x1,x2)))"
print(translate(prompt_nl_to_fol + example_1)) # all x1.(_killer(x1) -> exists x2._leave(x1,x2))
print(translate(prompt_fol_to_nl + example_2)) # Every woman experiences emotions.
```
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
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} | 0 | 2020-07-16T16:36:18Z | ---
language: en
datasets:
- wikisql
---
# T5-small fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **English** to **SQL** **translation**.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the Dataset 📚
Dataset ID: ```wikisql``` from [Huggingface/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| wikisql | train | 56355 |
| wikisql | valid | 14436 |
How to load it from [nlp](https://github.com/huggingface/nlp)
```python
train_dataset = nlp.load_dataset('wikisql', split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('wikisql', split=nlp.Split.VALIDATION)
```
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him!
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-wikiSQL")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-small-finetuned-wikiSQL")
def get_sql(query):
input_text = "translate English to SQL: %s </s>" % query
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'])
return tokenizer.decode(output[0])
query = "How many millions of params there are in HF-hub?"
get_sql(query)
# output: 'SELECT COUNT Params FROM table WHERE Location = HF-hub'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 1,907 | null | ---
language: it
---
# UmBERTo Wikipedia Uncased + italian SQuAD v1 📚 🧐 ❓
[UmBERTo-Wikipedia-Uncased](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1) fine-tuned on [Italian SQUAD v1 dataset](https://github.com/crux82/squad-it) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking.
UmBERTo-Wikipedia-Uncased Training is trained on a relative small corpus (~7GB) extracted from Wikipedia-ITA.
## Details of the downstream task (Q&A) - Dataset 📚
[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) [Rajpurkar et al. 2016] is a large scale dataset for training of question answering systems on factoid questions. It contains more than 100,000 question-answer pairs about passages from 536 articles chosen from various domains of Wikipedia.
**SQuAD-it** is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path 'Musixmatch/umberto-wikipedia-uncased-v1' \
--do_eval \
--do_train \
--do_lower_case \
--train_file '/content/dataset/SQuAD_it-train.json' \
--predict_file '/content/dataset/SQuAD_it-test.json' \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/drive/My\ Drive/umberto-uncased-finetuned-squadv1-it \
--overwrite_output_dir \
--save_steps 1000
```
With 10 epochs the model overfits the train dataset so I evaluated the different checkpoints created during training (every 1000 steps) and chose the best (In this case the one created at 17000 steps).
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **60.50** |
| **F1** | **72.41** |
```json
{
'exact': 60.50729399395453,
'f1': 72.4141113348361,
'total': 7609,
'HasAns_exact': 60.50729399395453,
'HasAns_f1': 72.4141113348361,
'HasAns_total': 7609,
'best_exact': 60.50729399395453,
'best_exact_thresh': 0.0,
'best_f1': 72.4141113348361,
'best_f1_thresh': 0.0
}
```
## Comparison ⚖️
| Model | EM | F1 score |
| -------------------------------------------------------------------------------------------------------------------------------- | --------- | --------- |
| [DrQA-it trained on SQuAD-it ](https://github.com/crux82/squad-it/blob/master/README.md#evaluating-a-neural-model-over-squad-it) | 56.1 | 65.9 |
| This one |60.50 |72.41 |
| [bert-italian-finedtuned-squadv1-it-alfa](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa) |**62.51** |**74.16** | | **62.51** | **74.16** |
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it')
QnA_pipeline({
'context': 'Marco Aurelio era un imperatore romano che praticava lo stoicismo come filosofia di vita .',
'question': 'Quale filosofia seguì Marco Aurelio ?'
})
# Output:
{'answer': 'stoicismo', 'end': 65, 'score': 0.9477770241566028, 'start': 56}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 7 | null | ---
tags:
- image-classification
- pytorch
- medical
- colon
metrics:
- accuracy: 0.93
---
# Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification
## Demo
### Drag the following images to the widget to test the model
- 
- 
- 
- 
## Training
You can find the code [here](https://github.com/qanastek/HugsVision/blob/main/recipes/kvasir_v2/binary_classification/Kvasir_v2_Image_Classifier.ipynb)
## Metrics
```
precision recall f1-score support
dyed-lifted-polyps 0.95 0.93 0.94 60
dyed-resection-margins 0.97 0.95 0.96 64
esophagitis 0.93 0.79 0.85 67
normal-cecum 1.00 0.98 0.99 54
normal-pylorus 0.95 1.00 0.97 57
normal-z-line 0.82 0.93 0.87 67
polyps 0.92 0.92 0.92 52
ulcerative-colitis 0.93 0.95 0.94 59
accuracy 0.93 480
macro avg 0.93 0.93 0.93 480
weighted avg 0.93 0.93 0.93 480
```
## How to use
```py
from transformers import ViTFeatureExtractor, ViTForImageClassification
from hugsvision.inference.VisionClassifierInference import VisionClassifierInference
path = "mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy"
classifier = VisionClassifierInference(
feature_extractor = ViTFeatureExtractor.from_pretrained(path),
model = ViTForImageClassification.from_pretrained(path),
)
img = "Your image path"
label = classifier.predict(img_path=img)
print("Predicted class:", label)
```
> Disclaimer: This model was trained for research only
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain |
DanBot/TCRsynth | [] | null | {
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} | 0 | null | ---
language: eo
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Esperanto Manuel Romero
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice eo
type: common_voice
args: eo
metrics:
- name: Test WER
type: wer
value: 15.86
---
# Wav2Vec2-Large-XLSR-53-esperanto
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "eo", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-esperanto")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Ukrainian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "eo", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-esperanto")
model.to("cuda")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 15.86 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found ??? |
DanL/scientific-challenges-and-directions | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:DanL/scientific-challenges-and-directions-dataset",
"arxiv:2108.13751",
"transformers",
"generated_from_trainer"
] | text-classification | {
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"BertForSequenceClassification"
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} | 134 | 2021-03-23T08:14:22Z | ---
language: eu
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Euskera Manuel Romero
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice eu
type: common_voice
args: eu
metrics:
- name: Test WER
type: wer
value: 24.03
---
# Wav2Vec2-Large-XLSR-53-euskera
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Euskera using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "eu", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Euskera test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "eu", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-euskera")
model.to("cuda")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 24.03 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found ???
|
Danbi/distilroberta-base-finetuned-wikitext2 | [] | null | {
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} | 0 | 2021-03-23T17:58:46Z | ---
language: uk
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Ukrainian Manuel Romero
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice uk
type: common_voice
args: uk
metrics:
- name: Test WER
type: wer
value: 41.82
---
# Wav2Vec2-Large-XLSR-53-ukrainian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "uk", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Ukrainian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "uk", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 41.82 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found ???
|
DarkestSky/distilbert-base-uncased-finetuned-ner | [] | null | {
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} | 0 | 2022-01-20T07:41:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- name: Loss
type: loss
value: 1.073493242263794
verified: true
- name: Exact Match
type: exact_match
value: 80.0853
verified: true
- name: F1
type: f1
value: 87.606
verified: true
---
<!-- 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-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Darren/darren | [
"pytorch"
] | null | {
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} | 0 | 2022-01-19T09:49:57Z | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 50.20410659441166
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Bleu: 50.2041
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DarshanDeshpande/marathi-distilbert | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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"DistilBertForMaskedLM"
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} | 14 | 2021-10-05T05:29:28Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {mrp/simcse-model-distil-m-bert}
This is a [sentence-transformers](https://www.SBERT.net) by using m-Distil-BERT as the baseline model 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 -->
We use SimCSE [here](https://arxiv.org/pdf/2104.08821.pdf) and training the model with Thai Wikipedia [here](https://github.com/PyThaiNLP/ThaiWiki-clean/releases/tag/20210620?fbclid=IwAR1YcmZkb-xd1ibTWCJOcu98_FQ5x3ioZaGW1ME-VHy9fAQLhEr5tXTJygA)
## 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 = ["ฉันนะคือคนรักชาติยังไงละ!", "พวกสามกีบล้มเจ้า!"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
``` |
DataikuNLP/paraphrase-MiniLM-L6-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
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} | 25 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ms29315/distilbert-base-uncased-finetuned-cola
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. -->
# ms29315/distilbert-base-uncased-finetuned-cola
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: 0.3100
- Validation Loss: 0.5090
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3100 | 0.5090 | 0 |
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.18.0
- Tokenizers 0.10.3
|
DataikuNLP/paraphrase-albert-small-v2 | [
"pytorch",
"albert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
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} | 628 | 2021-08-18T09:24:23Z | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5**
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
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} | 1,517 | 2021-08-18T14:19:02Z | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5**
|
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} | 0 | 2021-08-18T05:45:48Z | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5** |
Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 15 | 2021-11-19T15:11:01Z | ---
language:
- en
tags:
- sentence classification
- vossian antonomasia
license: "apache-2.0"
datasets:
- custom
widget:
- text: Bijan wants Jordan to be the Elizabeth Taylor of men's fragrances.
metrics:
- f1
- precision
- recall
---
## English Vossian Antonomasia Sentence Classifier
This page presents a fine-tuned [BERT-base-cased](https://huggingface.co/bert-base-cased) language model for classifying sentences that include Vossian Antonomasia.
The label "VA" corresponds to the occurrence of a Vossian Antonomasia in the sentence.
### Dataset
The dataset is a labeled Vossian Antonomasia dataset that evolved from [Schwab et al. 2019](https://www.aclweb.org/anthology/D19-1647.pdf) and was updated in [Schwab et al. 2022](https://doi.org/10.3389/frai.2022.868249).
### Results
F1 score: 0.974
For more results, please have a look at [our paper](https://doi.org/10.3389/frai.2022.868249).
---
### Cite
Please cite the following paper when using this model.
```
@article{schwab2022rodney,
title={“The Rodney Dangerfield of Stylistic Devices”: End-to-End Detection and Extraction of Vossian Antonomasia Using Neural Networks},
author={Schwab, Michel and J{\"a}schke, Robert and Fischer, Frank},
journal={Frontiers in Artificial Intelligence},
volume={5},
year={2022},
publisher={Frontiers Media SA}
}
```
---
### Interested in more?
Visit our [Website](http://vossanto.weltliteratur.net/) for more research on Vossian Antonomasia, including interactive visualizations for exploration. |
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 27 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-base-finetuned-arxiv
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: arxiv
metrics:
- name: Rouge1
type: rouge
value: 13.6917
---
<!-- 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. -->
# bart-base-finetuned-arxiv
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2912
- Rouge1: 13.6917
- Rouge2: 5.9564
- Rougel: 11.1734
- Rougelsum: 12.6817
- Gen Len: 19.9992
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6027 | 1.0 | 6345 | 2.4504 | 13.3687 | 5.603 | 10.8671 | 12.3297 | 20.0 |
| 2.4807 | 2.0 | 12690 | 2.3561 | 13.6207 | 5.855 | 11.1073 | 12.594 | 20.0 |
| 2.4041 | 3.0 | 19035 | 2.3035 | 13.6222 | 5.8863 | 11.1173 | 12.5984 | 20.0 |
| 2.3716 | 4.0 | 25380 | 2.2912 | 13.6917 | 5.9564 | 11.1734 | 12.6817 | 19.9992 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
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},
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}
}
} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-copa-kb-27
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-uncased-copa-kb-27
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6114
- Accuracy: 0.7100
## 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: 10
- eval_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6534 | 0.7400 |
| No log | 2.0 | 80 | 0.6114 | 0.7100 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Davlan/xlm-roberta-base-finetuned-igbo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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}
} | 68 | null | ---
language: multilingual
tags:
- mudes
license: apache-2.0
---
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in [this paper](https://arxiv.org/abs/2102.09665). We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5). Our participation in the task is detailed in [this paper](https://arxiv.org/abs/2104.04630).
## Usage
You can use this model when you have [MUDES](https://github.com/TharinduDR/MUDES) installed:
```bash
pip install mudes
```
Then you can use the model like this:
```python
from mudes.app.mudes_app import MUDESApp
app = MUDESApp("multilingual-base", use_cuda=False)
print(app.predict_toxic_spans("You motherfucking cunt", spans=True))
```
## System Demonstration
An experimental demonstration interface called MUDES-UI has been released on [GitHub](https://github.com/TharinduDR/MUDES-UI) and can be checked out in [here](http://rgcl.wlv.ac.uk/mudes/).
## Citing & Authors
If you find this model helpful, feel free to cite our publications
```bibtex
@inproceedings{ranasinghemudes,
title={{MUDES: Multilingual Detection of Offensive Spans}},
author={Tharindu Ranasinghe and Marcos Zampieri},
booktitle={Proceedings of NAACL},
year={2021}
}
```
```bibtex
@inproceedings{ranasinghe2021semeval,
title={{WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans}},
author = {Ranasinghe, Tharindu and Sarkar, Diptanu and Zampieri, Marcos and Ororbia, Alex},
booktitle={Proceedings of SemEval},
year={2021}
}
``` |
Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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} | 61 | null | # MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in text. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5).
## Usage
You can use this model when you have [MUDES](https://github.com/TharinduDR/MUDES) installed:
```bash
pip install mudes
```
Then you can use the model like this:
```python
from mudes.app.mudes_app import MUDESApp
app = MUDESApp("multilingual-large", use_cuda=False)
print(app.predict_toxic_spans("You motherfucking cunt", spans=True))
```
## System Demonstration
An experimental demonstration interface called MUDES-UI has been released on [GitHub](https://github.com/TharinduDR/MUDES-UI) and can be checked out in [here](http://rgcl.wlv.ac.uk/mudes/).
## Citing & Authors
If you find this model helpful, feel free to cite our publication
```bash
@inproceedings{ranasinghemudes,
title={{MUDES: Multilingual Detection of Offensive Spans}},
author={Tharindu Ranasinghe and Marcos Zampieri},
booktitle={Proceedings of NAACL},
year={2021}
}
```
```bash
@inproceedings{ranasinghe2021semeval,
title={{WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans}},
author = {Ranasinghe, Tharindu and Sarkar, Diptanu and Zampieri, Marcos and Ororbia, Alex},
booktitle={Proceedings of SemEval},
year={2021}
}
``` |
Davlan/xlm-roberta-base-finetuned-luo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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},
"translation_en_to_fr": {
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}
} | 5 | 2021-11-22T20:42:37Z | # The fastai models - PETS
This model is based on Lesson 1 of [fastai](https://course.fast.ai) and of [Walk with fastai](https://walkwithfastai.com/Pets)
## Dataset Used
This model was created with the [Oxford Pets](https://docs.fast.ai/data.external.html#Image-Classification-datasets) dataset in the fastai framework
## Model Training
The model was trained as a binary classifier, for cats or dogs
## How to use:
First, ensure that `huggingface_hub` is installed:
```bash
pip(3) install huggingface_hub
```
Next, download this model repo:
```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="muellerzr/fastai-pets-resnet-34")
```
Then install the correct fastai version:
```bash
cd fastai-pets-resnet34
pip(3) install -r requirements.txt
```
**NOTE: This is extremely important, as fastai versions are aggressively pinned based on training environment**
And finally load in the fastai `Learner` and predict
```python
from fastai.learner import load_learner
learn = load_learner('model.pth')
pred = learn.predict('myImage.jpg')
```
Versions of model used were taken with [dependency_checker](https://muellerzr.github.io/dependency_checker)
|
Davlan/xlm-roberta-base-finetuned-shona | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"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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}
} | 5 | 2021-09-12T16:39:53Z | ---
tags:
- conversational
---
# The Office - Pam DialoGPT Model |
Davlan/xlm-roberta-base-finetuned-somali | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
} | 8 | 2021-10-05T18:48:00Z | ---
language:
- tg
widget:
- text: "Пойтахти <mask> Душанбе"
- text: "<mask> ба ин сайти шумо медароям."
- text: "Номи ман Акрам <mask>"
tags:
- generated_from_trainer
model_index:
- name: TajBERTo
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
# TajBERTo: RoBERTa-like Language model trained on Tajik
## First ever Tajik NLP model 🔥
# Dataset:
# This model was trained on filtered and merged version of Leipzig Corpora https://wortschatz.unileipzig.de/en/download/Tajik
## Intended use
# You can use the raw model for masked text generation or fine-tune it to a downstream task.
## Example pipeline
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="muhtasham/TajBERTo",
tokenizer="muhtasham/TajBERTo"
)
fill_mask("Пойтахти <mask> Душанбе")
# This is the beginning of a beautiful <mask>.
{'score': 0.1952248513698578,
'sequence': 'Пойтахти шаҳри Душанбе',
'token': 710,
'token_str': ' шаҳри'},
{'score': 0.029092855751514435,
'sequence': 'Пойтахти дар Душанбе',
'token': 310,
'token_str': ' дар'},
{'score': 0.020065447315573692,
'sequence': 'Пойтахти Душанбе Душанбе',
'token': 717,
'token_str': ' Душанбе'},
{'score': 0.016725927591323853,
'sequence': 'Пойтахти Тоҷикистон Душанбе',
'token': 424,
'token_str': ' Тоҷикистон'},
{'score': 0.011400512419641018,
'sequence': 'Пойтахти аз Душанбе',
'token': 335,
'token_str': ' аз'}
```
|
Davlan/xlm-roberta-base-finetuned-wolof | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
} | 3 | 2021-10-22T11:59:53Z | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 203.30658367993382
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595545
- CO2 Emissions (in grams): 203.30658367993382
## Validation Metrics
- Loss: 0.30214861035346985
- MSE: 0.30214861035346985
- MAE: 0.25911855697631836
- R2: 0.8455587614373526
- RMSE: 0.5496804714202881
- Explained Variance: 0.8476610779762268
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/muhtasham/autonlp-Doctor_DE-24595545
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/autonlp-Doctor_DE-24595545", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("muhtasham/autonlp-Doctor_DE-24595545", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
Davlan/xlm-roberta-base-finetuned-xhosa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 12 | null | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 210.5957437893554
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595546
- CO2 Emissions (in grams): 210.5957437893554
## Validation Metrics
- Loss: 0.3092539310455322
- MSE: 0.30925390124320984
- MAE: 0.25015318393707275
- R2: 0.841926941198094
- RMSE: 0.5561060309410095
- Explained Variance: 0.8427215218544006
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/muhtasham/autonlp-Doctor_DE-24595546
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/autonlp-Doctor_DE-24595546", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("muhtasham/autonlp-Doctor_DE-24595546", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
Davlan/xlm-roberta-base-finetuned-zulu | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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}
}
} | 3 | null | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 183.88911013564527
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595548
- CO2 Emissions (in grams): 183.88911013564527
## Validation Metrics
- Loss: 0.3050823509693146
- MSE: 0.3050823509693146
- MAE: 0.2664000689983368
- R2: 0.844059188176304
- RMSE: 0.5523425936698914
- Explained Variance: 0.8472161293029785
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/muhtasham/autonlp-Doctor_DE-24595548
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/autonlp-Doctor_DE-24595548", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("muhtasham/autonlp-Doctor_DE-24595548", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
Davlan/xlm-roberta-large-masakhaner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"XLMRobertaForTokenClassification"
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} | 1,449 | 2022-02-13T05:04:41Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-base-v2_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9398776667163956
---
<!-- 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. -->
# albert-base-v2_mnli_bc
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2952
- Accuracy: 0.9399
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2159 | 1.0 | 16363 | 0.2268 | 0.9248 |
| 0.1817 | 2.0 | 32726 | 0.2335 | 0.9347 |
| 0.0863 | 3.0 | 49089 | 0.3014 | 0.9401 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Dawit/DialogGPT-small-ironman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 7 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9583768461882739
---
<!-- 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. -->
# roberta-base_mnli_bc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2125
- Accuracy: 0.9584
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2015 | 1.0 | 16363 | 0.1820 | 0.9470 |
| 0.1463 | 2.0 | 32726 | 0.1909 | 0.9559 |
| 0.0768 | 3.0 | 49089 | 0.2117 | 0.9585 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
] | text-classification | {
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} | 37 | null | ---
language: "id"
license: "mit"
datasets:
- Squad
- XQuad
- Tydiqa
widget:
- text: "I love you"
---
## Prefix use
Use prefix "question: {question} context: {context}" before input to generate the question answering
e.g
"question: siapa nama saya ? context: nama saya andi. saya tinggal di jakarta. istri saya bernama raisa"
for generate question prefix
generate questions: nama saya andi. saya tinggal di jakarta. istri saya bernama raisa
## Training data
Squad
XQuad
Tydiqa |
DeadBeast/roberta-base-pretrained-mr-2 | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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} | 5 | null | ---
tags:
- translation
language: "id"
license: "mit"
datasets:
- OPUS
- CC-aligned
widget:
- text: "I love you"
---
## MT5-Large-Translate-en-id
## Prefix use
Use prefix "translate:" before input to generate the translation
e.g
"translate: i love you"
## Training data
Opus (Open Subtittle and Wikimatrix)
CCaligned (en-id sentence pair)
|
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} | 0 | null | ---
language: id
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
- xlsr-fine-tuning-week
license: apache-2.0
---
## Evaluation on Common Voice ID Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "munggok/xlsr_indonesia"
device = "cuda"
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]' # noqa: W605
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
processor = Wav2Vec2Processor.from_pretrained(model_name)
ds = load_dataset("common_voice", "id", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
ds = ds.map(map_to_array)
def map_to_pred(batch):
features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)
batch["target"] = batch["sentence"]
return batch
result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys()))
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
**Result**: 25.7 % |
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} | 0 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mohsenalam/autonlp-data-billsum-summarization
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 5691253
## Validation Metrics
- Loss: 1.4430530071258545
- Rouge1: 23.9565
- Rouge2: 19.1897
- RougeL: 23.1191
- RougeLsum: 23.3308
- Gen Len: 20.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/mohsenalam/autonlp-billsum-summarization-5691253
``` |
DecafNosebleed/scarabot-model | [
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 6 | null | `bert-base-cased` trained for spelling correction. See [neuspell](https://github.com/neuspell/neuspell) repository for more details about training and evaluating the model. |
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} | 0 | null | ---
tags:
- conversational
---
# Rick DialoGPT Model |
Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 7 | null | ---
tags:
- conversational
---
# SpongeBob DialoGPT Model |
Declan/CNN_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: roberta-base-finetuned-squad2
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. -->
# roberta-base-finetuned-squad2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9325
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.88 | 1.0 | 8160 | 0.8129 |
| 0.6643 | 2.0 | 16320 | 0.8567 |
| 0.5096 | 3.0 | 24480 | 0.9325 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
Declan/ChicagoTribune_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
model-index:
- name: reformer-clm
---
## reformer-clm
This casual language model was trained from scratch on CNN/Dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7783
## 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: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.8321 | 1.0 | 18412 | 3.8074 |
| 3.4965 | 2.0 | 36824 | 3.4223 |
| 3.1927 | 3.0 | 55236 | 3.0815 |
| 3.046 | 4.0 | 73648 | 2.9270 |
| 2.9781 | 5.0 | 92060 | 2.8515 |
| 2.9398 | 6.0 | 110472 | 2.8082 |
| 2.9293 | 7.0 | 128884 | 2.7904 |
| 2.9212 | 8.0 | 147296 | 2.7817 |
| 2.9169 | 9.0 | 165708 | 2.7787 |
| 2.9197 | 10.0 | 184120 | 2.7783 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.9.0
- Datasets 1.2.1
- Tokenizers 0.10.3
|
Declan/FoxNews_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | 2021-02-13T11:20:58Z | ---
language: zh
widget:
- text: "今天是下雨天"
- text: "走向森林"
---
# EasternFantasyNoval
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 1.2GiB
- **Language**: Chinese
|
Declan/FoxNews_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
language: zh
widget:
- text: "今天是下雨天"
- text: "走向森林"
---
<h1 align="center">
CPM
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
[repo: CPM-Generate](https://github.com/TsinghuaAI/CPM-Generate)
The One Thing You Need to Know is this model is not uploaded by official, the conver script is [here](https://github.com/mymusise/CPM-TF2Transformer/blob/main/transfor_CMP.ipynb)
# Overview
- **Language model**: CPM
- **Model size**: 2.6B parameters
- **Language**: Chinese
# How to use
How to use this model directly from the 🤗/transformers library:
```python
from transformers import XLNetTokenizer, TFGPT2LMHeadModel
import jieba
# add spicel process
class XLNetTokenizer(XLNetTokenizer):
translator = str.maketrans(" \n", "\u2582\u2583")
def _tokenize(self, text, *args, **kwargs):
text = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)]
text = " ".join(text)
return super()._tokenize(text, *args, **kwargs)
def _decode(self, *args, **kwargs):
text = super()._decode(*args, **kwargs)
text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n')
return text
tokenizer = XLNetTokenizer.from_pretrained('mymusise/CPM-GPT2-FP16')
model = TFGPT2LMHeadModel.from_pretrained("mymusise/CPM-GPT2-FP16")
```
How to generate text
```python
from transformers import TextGenerationPipeline
text_generater = TextGenerationPipeline(model, tokenizer)
texts = [
'今天天气不错',
'天下武功, 唯快不',
"""
我们在火星上发现了大量的神奇物种。有神奇的海星兽，身上是粉色的，有5条腿；有胆小的猫猫兽，橘色，有4条腿；有令人恐惧的蜈蚣兽，全身漆黑，36条腿；有纯洁的天使兽，全身洁白无瑕，有3条腿；有贪吃的汪汪兽，银色的毛发，有5条腿；有蛋蛋兽，紫色，8条腿。
请根据上文，列出一个表格，包含物种名、颜色、腿数量。
|物种名|颜色|腿数量|
|亚古兽|金黄|2|
|海星兽|粉色|5|
|猫猫兽|橘色|4|
|蜈蚣兽|漆黑|36|
"""
]
for text in texts:
token_len = len(tokenizer._tokenize(text))
print(text_generater(text, max_length=token_len + 15, top_k=1, use_cache=True, prefix='')[0]['generated_text'])
print(text_generater(text, max_length=token_len + 15, do_sample=True, top_k=5)[0]['generated_text'])
```
You can try it on [colab](https://colab.research.google.com/github/mymusise/CPM-TF2Transformer/blob/main/demo-fp16.ipynb)
<a href="https://colab.research.google.com/github/mymusise/CPM-TF2Transformer/blob/main/demo-fp16.ipynb">
<img alt="Build" src="https://colab.research.google.com/assets/colab-badge.svg">
</a>
|
Declan/HuffPost_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | # {MODEL_NAME}
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉
## Model
This is a finetuned version of [mys/bert-base-turkish-cased-nli-mean](https://huggingface.co/) for FAQ retrieval, which is itself a finetuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) for NLI. It maps questions & answers to 768 dimensional vectors to be used for FAQ-style chatbots and answer retrieval in question-answering pipelines. It was trained on the Turkish subset of [clips/mqa](https://huggingface.co/datasets/clips/mqa) dataset after some cleaning/ filtering and with a Multiple Negatives Symmetric Ranking loss. Before finetuning, I added two special tokens to the tokenizer (i.e., `<Q>` for questions and `<A>` for answers) and resized the model embeddings, so you need to prepend the relevant tokens to the sequences before feeding them into the model. Please have a look at [my accompanying repo](https://github.com/monatis/trfaq) to see how it was finetuned and how it can be used in inference. The following code snippet is an excerpt from the inference at the repo.
## Usage
```python
questions = [
"Merhaba",
"Nasılsın?",
"Bireysel araç kiralama yapıyor musunuz?",
"Kurumsal araç kiralama yapıyor musunuz?"
]
answers = [
"Merhaba, size nasıl yardımcı olabilirim?",
"İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?",
"Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?",
"Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?"
]
questions = ["<Q>" + q for q in questions]
answers = ["<A>" + a for a in answers]
def answer_faq(model, tokenizer, questions, answers, return_similarities=False):
q_len = len(questions)
tokens = tokenizer(questions + answers, padding=True, return_tensors='tf')
embs = model(**tokens)[0]
attention_masks = tf.cast(tokens['attention_mask'], tf.float32)
sample_length = tf.reduce_sum(attention_masks, axis=-1, keepdims=True)
masked_embs = embs * tf.expand_dims(attention_masks, axis=-1)
masked_embs = tf.reduce_sum(masked_embs, axis=1) / tf.cast(sample_length, tf.float32)
a = tf.math.l2_normalize(masked_embs[:q_len, :], axis=1)
b = tf.math.l2_normalize(masked_embs[q_len:, :], axis=1)
similarities = tf.matmul(a, b, transpose_b=True)
scores = tf.nn.softmax(similarities)
results = list(zip(answers, scores.numpy().squeeze().tolist()))
sorted_results = sorted(results, key=lambda x: x[1], reverse=True)
sorted_results = [{"answer": answer.replace("<A>", ""), "score": f"{score:.4f}"} for answer, score in sorted_results]
return sorted_results
for question in questions:
results = answer_faq(model, tokenizer, [question], answers)
print(question.replace("<Q>", ""))
print(results)
print("---------------------")
```
And the output is:
```shell
Merhaba
[{'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.2931'}, {'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2751'}, {'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2200'}, {'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.2118'}]
---------------------
Nasılsın?
[{'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2808'}, {'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.2623'}, {'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2320'}, {'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.2249'}]
---------------------
Bireysel araç kiralama yapıyor musunuz?
[{'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2861'}, {'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.2768'}, {'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2215'}, {'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.2156'}]
---------------------
Kurumsal araç kiralama yapıyor musunuz?
[{'answer': 'Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?', 'score': '0.3060'}, {'answer': 'Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?', 'score': '0.2929'}, {'answer': 'İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?', 'score': '0.2066'}, {'answer': 'Merhaba, size nasıl yardımcı olabilirim?', 'score': '0.1945'}]
---------------------
``` |
DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] | text-generation | {
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}
} | 340 | null | scibert_scivocab_uncased submission for SDU21 Task 1 AI
|
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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}
} | 1,463 | null | scibert_scivocab_uncased_ft MLM pretrained on SDU21 Task 1 + 2
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] | feature-extraction | {
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}
} | 1,614 | null | scibert_scivocab_uncased_ft_mlm MLM pretrained on SDU21 Task 1 + 2
|
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | {
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}
} | 140 | null | scibert_scivocab_uncased_tv submission for SDU21 Task 1 AI
|
DeepPavlov/marianmt-tatoeba-enru | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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} | 1 | null | This model is further trained on top of scibert-base using masked language modeling loss (MLM). The corpus is roughly abstracts from 270,000 earth science-based publications.
The tokenizer used is AutoTokenizer, which is trained on the same corpus.
Stay tuned for further downstream task tests and updates to the model.
in the works
- MLM + NSP task loss
- Add more data sources for training
- Test using downstream tasks
|
DeltaHub/adapter_t5-3b_qnli | [
"pytorch",
"transformers"
] | null | {
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} | 3 | null | ---
library_name: keras
---
## 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 14 | 2021-06-23T23:58:49Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: baked-goods
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.875
---
# baked-goods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cake
#### cookie
#### pie
 |
Deniskin/essays_small_2000 | [] | null | {
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} | 0 | 2021-06-29T19:47:25Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: baseball-stadium-foods
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9107142686843872
---
# baseball-stadium-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cotton candy
#### hamburger
#### hot dog
#### nachos
#### popcorn
 |
Deniskin/gpt3_medium | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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} | 52 | null | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- ag_news
- pytorch
license: mit
datasets:
- ag_news
metrics:
- accuracy
---
# bert-base-uncased-ag-news
## Model description
`bert-base-uncased` finetuned on the AG News dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 4 T4 GPUs, 4 epochs. [The code can be found here](https://github.com/nateraw/hf-text-classification)
#### Limitations and bias
- Not the best model...
## Training data
Data came from HuggingFace's `datasets` package. The data can be viewed [on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=ag_news).
## Training procedure
...
## Eval results
... |
Denny29/DialoGPT-medium-asunayuuki | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | null | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- accuracy
---
# bert-base-uncased-emotion
## Model description
`bert-base-uncased` finetuned on the emotion dataset using PyTorch Lightning. Sequence length 128, learning rate 2e-5, batch size 32, 2 GPUs, 4 epochs.
For more details, please see, [the emotion dataset on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion).
#### Limitations and bias
- Not the best model, but it works in a pinch I guess...
- Code not available as I just hacked this together.
- [Follow me on github](https://github.com/nateraw) to get notified when code is made available.
## Training data
Data came from HuggingFace's `datasets` package. The data can be viewed [on nlp viewer](https://huggingface.co/nlp/viewer/?dataset=emotion).
## Training procedure
...
## Eval results
val_acc - 0.931 (useless, as this should be precision/recall/f1)
The score was calculated using PyTorch Lightning metrics.
|
DeskDown/MarianMixFT_en-id | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: codecarbon-text-classification
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. -->
# codecarbon-text-classification
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the imdb 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: 5e-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: 4
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
DeskDown/MarianMixFT_en-ja | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 9 | null | ---
library_name: pytorch
tags:
- dcgan
---
# cryptopunks-gan
A DCGAN trained to generate novel Cryptopunks.
Check out the code by Teddy Koker [here](https://github.com/teddykoker/cryptopunks-gan).
## Generated Punks
Here are some punks generated by this model:
## Usage
You can try it out yourself, or you can play with the [demo](https://huggingface.co/spaces/nateraw/cryptopunks-generator).
To use it yourself - make sure you have `torch`, `torchvision`, and `huggingface_hub` installed. Then, run the following to generate a grid of 64 random punks:
```python
import torch
from huggingface_hub import hf_hub_download
from torch import nn
from torchvision.utils import save_image
class Generator(nn.Module):
def __init__(self, nc=4, nz=100, ngf=64):
super(Generator, self).__init__()
self.network = nn.Sequential(
nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh(),
)
def forward(self, input):
output = self.network(input)
return output
model = Generator()
weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')
model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
out = model(torch.randn(64, 100, 1, 1))
save_image(out, "punks.png", normalize=True)
```
|
DeskDown/MarianMix_en-ja-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 1 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: denver-nyc-paris
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8657407164573669
---
# denver-nyc-paris
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### denver
#### new york city
#### paris
 |
DeskDown/MarianMix_en-zh-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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}
}
} | 3 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: doggos-lol
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9166666865348816
---
# doggos-lol
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### bernese mountain dog
#### husky
#### saint bernard
 |
DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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}
}
} | 5 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: donut-or-bagel
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
# donut-or-bagel
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### bagel
#### donut
 |
Devid/DialoGPT-small-Miku | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 10 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ex-for-evan
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9791666865348816
---
# ex-for-evan
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cheetah
#### elephant
#### giraffe
#### rhino
 |
Devmapall/paraphrase-quora | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
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"prefix": "summarize: "
},
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"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
- pytorch
datasets:
- food101
metrics:
- accuracy
model-index:
- name: food101_outputs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food-101
type: food101
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8912871287128713
---
<!-- 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. -->
# nateraw/food
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the nateraw/food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4501
- Accuracy: 0.8913
## 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: 128
- eval_batch_size: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8271 | 1.0 | 592 | 0.6070 | 0.8562 |
| 0.4376 | 2.0 | 1184 | 0.4947 | 0.8691 |
| 0.2089 | 3.0 | 1776 | 0.4876 | 0.8747 |
| 0.0882 | 4.0 | 2368 | 0.4639 | 0.8857 |
| 0.0452 | 5.0 | 2960 | 0.4501 | 0.8913 |
### Framework versions
- Transformers 4.9.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.9.1.dev0
- Tokenizers 0.10.3
|
Devrim/prism-default | [
"license:mit"
] | null | {
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} | 0 | null | ---
tags:
- text-classification
library_name: generic
---
# Model Card for `guesslang`
|
DevsIA/imagenes | [] | null | {
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} | 0 | null | ---
tags:
- object-detection
- pytorch
---
# hot-dog
Ignore me...I'm broken. |
DiegoBalam12/institute_classification | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- huggingpics
- generated_from_trainer
---
<!-- 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. -->
# huggingpics-package-demo-2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3761
- Acc: 0.9403
## 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: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0328 | 1.0 | 24 | 0.9442 | 0.7463 |
| 0.8742 | 2.0 | 48 | 0.7099 | 0.9403 |
| 0.6451 | 3.0 | 72 | 0.5050 | 0.9403 |
| 0.508 | 4.0 | 96 | 0.3761 | 0.9403 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
|
Digakive/Hsgshs | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- huggingpics
- generated_from_trainer
model-index:
- name: huggingpics-package-demo
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. -->
# huggingpics-package-demo
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3761
- Acc: 0.9403
## 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: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0328 | 1.0 | 24 | 0.9442 | 0.7463 |
| 0.8742 | 2.0 | 48 | 0.7099 | 0.9403 |
| 0.6451 | 3.0 | 72 | 0.5050 | 0.9403 |
| 0.508 | 4.0 | 96 | 0.3761 | 0.9403 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
|
DingleyMaillotUrgell/homer-bot | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] | conversational | {
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} | 12 | null | ---
license: apache-2.0
tags:
- image-classification
- keras
library_name: keras
---
# Test |
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
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} | 0 | null | ---
library_name: keras
---
## 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
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