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text2text-generation | transformers |
# t5-v1_1-base-dutch-english-cased-1024
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
This **t5-v1.1** mode... | {"language": ["nl", "en"], "license": "apache-2.0", "tags": ["t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned"], "inference": false} | yhavinga/t5-v1_1-base-dutch-english-cased-1024 | null | [
"transformers",
"jax",
"t5",
"text2text-generation",
"seq2seq",
"nl",
"en",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683",
"2109.10686"
] | [
"nl",
"en"
] | TAGS
#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| t5-v1\_1-base-dutch-english-cased-1024
======================================
A T5 sequence to sequence model
pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4.
This t5-v1.1 model has 247M parameters.
It was pre-trained with masked language modeling (denoise token span corruption) o... | [] | [
"TAGS\n#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# t5-v1_1-base-dutch-english-cased
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
This **t5-v1.1** model has... | {"language": ["nl", "en"], "license": "apache-2.0", "tags": ["t5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned"], "inference": false} | yhavinga/t5-v1_1-base-dutch-english-cased | null | [
"transformers",
"jax",
"t5",
"text2text-generation",
"seq2seq",
"nl",
"en",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683",
"2109.10686"
] | [
"nl",
"en"
] | TAGS
#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| t5-v1\_1-base-dutch-english-cased
=================================
A T5 sequence to sequence model
pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4.
This t5-v1.1 model has 247M parameters.
It was pre-trained with masked language modeling (denoise token span corruption) objective o... | [] | [
"TAGS\n#transformers #jax #t5 #text2text-generation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #arxiv-1910.10683 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
fill-mask | transformers | hello
| {} | yhk04150/SBERT | null | [
"transformers",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<span style="font-size:larger;">**Clinical-Longformer**</span> is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-Longformer consistently out-performs ClinicalBERT across 10 baseline dataset for a... | {"language": "en", "tags": ["longformer", "clinical"]} | yikuan8/Clinical-Longformer | null | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"clinical",
"en",
"arxiv:2201.11838",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.11838"
] | [
"en"
] | TAGS
#transformers #pytorch #longformer #fill-mask #clinical #en #arxiv-2201.11838 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<span style="font-size:larger;">Clinical-Longformer</span> is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-Longformer consistently out-performs ClinicalBERT across 10 baseline dataset for at le... | [
"### Pre-training\nWe initialized Clinical-Longformer from the pre-trained weights of the base version of Longformer. The pre-training process was distributed in parallel to 6 32GB Tesla V100 GPUs. FP16 precision was enabled to accelerate training. We pre-trained Clinical-Longformer for 200,000 steps with batch siz... | [
"TAGS\n#transformers #pytorch #longformer #fill-mask #clinical #en #arxiv-2201.11838 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pre-training\nWe initialized Clinical-Longformer from the pre-trained weights of the base version of Longformer. The pre-training process was distributed... |
automatic-speech-recognition | transformers |
<!-- 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. -->
# temp
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "temp", "results": []}]} | ying-tina/temp | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| temp
====
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4645
* Wer: 0.3527
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-32-epochs30
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab-32-epochs30", "results": []}]} | ying-tina/wav2vec2-base-timit-demo-colab-32-epochs30 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab-32-epochs30
==========================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4615
* Wer: 0.3434
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_b... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-32-epochs50-earlystop
This model is a fine-tuned version of [facebook/wav2vec2-base](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab-32-epochs50-earlystop", "results": []}]} | ying-tina/wav2vec2-base-timit-demo-colab-32-epochs50-earlystop | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab-32-epochs50-earlystop
====================================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5208
* Wer: 0.3561
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_b... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-32
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | ying-tina/wav2vec2-base-timit-demo-colab-32 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab-32
=================================
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4488
* Wer: 0.3149
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-test
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab-test", "results": []}]} | ying-tina/wav2vec2-base-timit-demo-colab-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab-test
===================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4283
* Wer: 0.3356
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | ying-tina/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5127
* Wer: 0.3082
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
fill-mask | transformers | `FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3... | {} | yiyanghkust/finbert-pretrain | null | [
"transformers",
"pytorch",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| 'FinBERT' is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3... | [] | [
"TAGS\n#transformers #pytorch #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3... | {"language": "en", "tags": ["financial-sentiment-analysis", "sentiment-analysis"], "widget": [{"text": "growth is strong and we have plenty of liquidity"}]} | yiyanghkust/finbert-tone | null | [
"transformers",
"pytorch",
"tf",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #text-classification #financial-sentiment-analysis #sentiment-analysis #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
'FinBERT' is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3... | [
"# How to use \nYou can use this model with Transformers pipeline for sentiment analysis."
] | [
"TAGS\n#transformers #pytorch #tf #text-classification #financial-sentiment-analysis #sentiment-analysis #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# How to use \nYou can use this model with Transformers pipeline for sentiment analysis."
] |
text2text-generation | transformers |
## BART ELI5
Read the article at https://yjernite.github.io/lfqa.html and try the demo at https://huggingface.co/qa/
| {"language": "en", "license": "apache-2.0", "datasets": ["eli5"]} | yjernite/bart_eli5 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:eli5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-eli5 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## BART ELI5
Read the article at URL and try the demo at URL
| [
"## BART ELI5\n\nRead the article at URL and try the demo at URL"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-eli5 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## BART ELI5\n\nRead the article at URL and try the demo at URL"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-zh-de-tuned-Tatoeba-small
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-de](https://huggingface.co/Hels... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-zh-de-tuned-Tatoeba-small", "results": []}]} | ykliu1892/opus-mt-zh-de-tuned-Tatoeba-small | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-zh-de-tuned-Tatoeba-small
=================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-zh-de on a refined dataset of Tatoeba German - Chinese corpus URL
It achieves the following results on the evaluation set:
* Loss: 2.2703
* Bleu: 16.504
* Gen Len: 16.6531
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
text2text-generation | transformers |
<!-- 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. -->
# translation-en-pt-t5-Duolingo-Subtitles
This model is a fine-tuned version of [unicamp-dl/translation-en-pt-t5](https://huggingf... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "translation-en-pt-t5-Duolingo-Subtitles", "results": []}]} | ykliu1892/translation-en-pt-t5-Duolingo-Subtitles | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# translation-en-pt-t5-Duolingo-Subtitles
This model is a fine-tuned version of unicamp-dl/translation-en-pt-t5 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7469
- eval_bleu: 39.9403
- eval_gen_len: 8.98
- eval_runtime: 997.6641
- eval_samples_per_second: 150.351
- ... | [
"# translation-en-pt-t5-Duolingo-Subtitles\n\nThis model is a fine-tuned version of unicamp-dl/translation-en-pt-t5 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.7469\n- eval_bleu: 39.9403\n- eval_gen_len: 8.98\n- eval_runtime: 997.6641\n- eval_samples_per_second: ... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# translation-en-pt-t5-Duolingo-Subtitles\n\nThis model is a fine-tuned version of unicamp-dl/translation-en-pt-t5 on an unknown data... |
text2text-generation | transformers |
<!-- 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. -->
# translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles
This model was trained from scratch on an unknown... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles", "results": []}]} | ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training p... | [
"# translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles\n\nThis model was trained from scratch on an unknown... |
text2text-generation | transformers |
<!-- 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. -->
# translation-en-pt-t5-finetuned-Duolingo-Subtitles
This model is a fine-tuned version of [ykliu1892/translation-en-pt-t5-finetune... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "translation-en-pt-t5-finetuned-Duolingo-Subtitles", "results": []}]} | ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles | null | [
"transformers",
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"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# translation-en-pt-t5-finetuned-Duolingo-Subtitles
This model is a fine-tuned version of ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0932
- eval_bleu: 28.4269
- eval_gen_len: 8.816
- eval_runtime: 1404.59... | [
"# translation-en-pt-t5-finetuned-Duolingo-Subtitles\n\nThis model is a fine-tuned version of ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0932\n- eval_bleu: 28.4269\n- eval_gen_len: 8.816\n- eval_runtime... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# translation-en-pt-t5-finetuned-Duolingo-Subtitles\n\nThis model is a fine-tuned version of ykliu1892/translation-en-pt-t5-finetuned... |
text2text-generation | transformers |
<!-- 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. -->
# translation-en-pt-t5-finetuned-Duolingo
This model was trained from scratch on an unknown dataset.
It achieves the following res... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "translation-en-pt-t5-finetuned-Duolingo", "results": []}]} | ykliu1892/translation-en-pt-t5-finetuned-Duolingo | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| translation-en-pt-t5-finetuned-Duolingo
=======================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7362
* Bleu: 39.4725
* Gen Len: 9.002
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-generation | transformers |
<!-- 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. -->
# fintune-ja-chatbot
This model is a fine-tuned version of [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2... | {"language": ["finetuned_from"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "fintune-ja-chatbot", "results": []}]} | ylh1013/fintune-ja-chatbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"finetuned_from"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# fintune-ja-chatbot
This model is a fine-tuned version of rinna/japanese-gpt2-medium on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpar... | [
"# fintune-ja-chatbot\n\nThis model is a fine-tuned version of rinna/japanese-gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proced... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# fintune-ja-chatbot\n\nThis model is a fine-tuned version of rinna/japanese-gpt2-medium on an unknown dataset.",
"## Model... |
text-generation | transformers |
<!-- 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. -->
# ja_chatbot
This model is a fine-tuned version of [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium)... | {"language": ["finetuned_from"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "ja_chatbot", "results": []}]} | ylh1013/ja_chatbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"finetuned_from"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ja_chatbot
This model is a fine-tuned version of rinna/japanese-gpt2-medium on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# ja_chatbot\n\nThis model is a fine-tuned version of rinna/japanese-gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ja_chatbot\n\nThis model is a fine-tuned version of rinna/japanese-gpt2-medium on an unknown dataset.",
"## Model description\n\nMor... |
text2text-generation | transformers | Note: no filter | {} | yliu337/sliding_window_token_both_ctx | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Note: no filter | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | https://www.geogebra.org/m/cwcveget
https://www.geogebra.org/m/b8dzxk6z
https://www.geogebra.org/m/nqanttum
https://www.geogebra.org/m/pd3g8a4u
https://www.geogebra.org/m/jw8324jz
https://www.geogebra.org/m/wjbpvz5q
https://www.geogebra.org/m/qm3g3ma6
https://www.geogebra.org/m/sdajgph8
https://www.geogebra.org/m/e3ghh... | {} | yluisfern/FDR | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
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URL | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | This is a strong pre-trained RoBERTa-Large NLI model.
The training data is a combination of well-known NLI datasets: [`SNLI`](https://nlp.stanford.edu/projects/snli/), [`MNLI`](https://cims.nyu.edu/~sbowman/multinli/), [`FEVER-NLI`](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever... | {"license": "mit", "datasets": ["snli", "anli", "multi_nli", "multi_nli_mismatch", "fever"]} | ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is a strong pre-trained RoBERTa-Large NLI model.
The training data is a combination of well-known NLI datasets: 'SNLI', 'MNLI', 'FEVER-NLI', 'ANLI (R1, R2, R3)'.
Other pre-trained NLI models including 'RoBERTa', 'ALBert', 'BART', 'ELECTRA', 'XLNet' are also available.
Trained by Yixin Nie, original source.... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null | # Test | {} | yo/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Test | [
"# Test"
] | [
"TAGS\n#region-us \n",
"# Test"
] |
text-generation | transformers |
<!-- 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. -->
# gpt2-bash-history-baseline
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It a... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-bash-history-baseline", "results": []}]} | yoavgur/gpt2-bash-history-baseline | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-bash-history-baseline
==========================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0349
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text-generation | transformers |
<!-- 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. -->
# gpt2-bash-history-baseline2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-bash-history-baseline2", "results": []}]} | yoavgur/gpt2-bash-history-baseline2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-bash-history-baseline2
===========================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6480
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
sentence-similarity | sentence-transformers |
## Usage
```
from sentence_transformers import SentenceTransformer, models
embedding_model = models.Transformer("yobi/klue-roberta-base-sts")
pooling_model = models.Pooling(
embedding_model.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
)
model = SentenceTransformer(modules=[embedding_model, ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | yobi/klue-roberta-base-sts | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
## Usage
| [
"## Usage"
] | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"## Usage"
] |
text-generation | transformers |
# japanese-gpt-1b
This repository provides a 1.3B-parameter Japanese GPT model. The model was trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# How to use the model
*NOTE:* Use `T5Tokenizer` to initiate the tokenizer.
~~~~
import torch
from transformers import T5Tok... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt", "text-generation", "lm", "nlp"], "datasets": ["cc100", "wikipedia"], "thumbnail": "https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png", "widget": [{"text": "\u897f\u7530\u5e7e\u591a\u90ce\u306f\u3001"}]} | yohida/yoshida_gpt | null | [
"transformers",
"gpt2",
"text-generation",
"ja",
"japanese",
"gpt",
"lm",
"nlp",
"dataset:cc100",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #gpt2 #text-generation #ja #japanese #gpt #lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# japanese-gpt-1b
!rinna-icon
This repository provides a 1.3B-parameter Japanese GPT model. The model was trained by rinna Co., Ltd.
# How to use the model
*NOTE:* Use 'T5Tokenizer' to initiate the tokenizer.
~~~~
import torch
from transformers import T5Tokenizer, AutoModelForCausalLM
tokenizer = T5... | [
"# japanese-gpt-1b\r\n\r\n!rinna-icon\r\n\r\nThis repository provides a 1.3B-parameter Japanese GPT model. The model was trained by rinna Co., Ltd.",
"# How to use the model\r\n\r\n*NOTE:* Use 'T5Tokenizer' to initiate the tokenizer.\r\n\r\n~~~~\r\nimport torch\r\nfrom transformers import T5Tokenizer, AutoModelFo... | [
"TAGS\n#transformers #gpt2 #text-generation #ja #japanese #gpt #lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# japanese-gpt-1b\r\n\r\n!rinna-icon\r\n\r\nThis repository provides a 1.3B-parameter Japanese GPT model. T... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | yokonav/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9177
* Mae: 0.4756
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-generation | transformers | # GPT-2 finetuned on German Dataset
### Tokenizer
We first trained a tokenizer on OSCAR's `unshuffled_original_de` German data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-ko/experiments/ex... | {"language": ["de"], "license": "mit", "tags": ["text-generation"], "datasets": ["oscar"], "widget": [{"text": "Mein Name ist Anna. Ich komme aus \u00d6sterreich und "}]} | yongzx/gpt2-finetuned-oscar-de | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"de",
"dataset:oscar",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #text-generation #de #dataset-oscar #license-mit #endpoints_compatible #text-generation-inference #region-us
| # GPT-2 finetuned on German Dataset
### Tokenizer
We first trained a tokenizer on OSCAR's 'unshuffled_original_de' German data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the Python file for the training.
### Model
We finetuned the 'wte' and 'wpe' layers of GPT-2 (while freezi... | [
"# GPT-2 finetuned on German Dataset",
"### Tokenizer\nWe first trained a tokenizer on OSCAR's 'unshuffled_original_de' German data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the Python file for the training.",
"### Model\nWe finetuned the 'wte' and 'wpe' layers of GP... | [
"TAGS\n#transformers #pytorch #gpt2 #feature-extraction #text-generation #de #dataset-oscar #license-mit #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-2 finetuned on German Dataset",
"### Tokenizer\nWe first trained a tokenizer on OSCAR's 'unshuffled_original_de' German data subset by ... |
text-generation | transformers |
# GPT-2 finetuned on French Dataset
### Tokenizer
We use GPT-2 tokenizer.
### Model
We finetuned the `wte` and `wpe` layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's `unshuffled_original_fr` French data subset. We used [Huggingface's code](https://github.com/huggingface/transformers/blo... | {"language": ["fr"], "license": "mit", "tags": ["text-generation"], "datasets": ["oscar"], "widget": [{"text": "Je suis ravi de vous "}]} | yongzx/gpt2-finetuned-oscar-fr-ori-tok | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"fr",
"dataset:oscar",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #fr #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT-2 finetuned on French Dataset
### Tokenizer
We use GPT-2 tokenizer.
### Model
We finetuned the 'wte' and 'wpe' layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's 'unshuffled_original_fr' French data subset. We used Huggingface's code for fine-tuning the causal language model GPT-2, ... | [
"# GPT-2 finetuned on French Dataset",
"### Tokenizer\nWe use GPT-2 tokenizer.",
"### Model\nWe finetuned the 'wte' and 'wpe' layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's 'unshuffled_original_fr' French data subset. We used Huggingface's code for fine-tuning the causal language... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #fr #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-2 finetuned on French Dataset",
"### Tokenizer\nWe use GPT-2 tokenizer.",
"### Model\nWe finetuned the 'wte' and 'wpe' layers of GP... |
text-generation | transformers |
# GPT-2 finetuned on French Dataset
### Tokenizer
We first trained a tokenizer on OSCAR's `unshuffled_original_fr` French data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-fr/experiments/... | {"language": ["fr"], "license": "mit", "tags": ["text-generation"], "datasets": ["oscar"], "widget": [{"text": "Je suis ravi de vous "}]} | yongzx/gpt2-finetuned-oscar-fr | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"fr",
"dataset:oscar",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #text-generation #fr #dataset-oscar #license-mit #endpoints_compatible #text-generation-inference #region-us
|
# GPT-2 finetuned on French Dataset
### Tokenizer
We first trained a tokenizer on OSCAR's 'unshuffled_original_fr' French data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the Python file for the training.
### Model
We finetuned the 'wte' and 'wpe' layers of GPT-2 (while fre... | [
"# GPT-2 finetuned on French Dataset",
"### Tokenizer\nWe first trained a tokenizer on OSCAR's 'unshuffled_original_fr' French data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the Python file for the training.",
"### Model\nWe finetuned the 'wte' and 'wpe' layers of GP... | [
"TAGS\n#transformers #pytorch #gpt2 #feature-extraction #text-generation #fr #dataset-oscar #license-mit #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-2 finetuned on French Dataset",
"### Tokenizer\nWe first trained a tokenizer on OSCAR's 'unshuffled_original_fr' French data subset by ... |
text-generation | transformers | # GPT-2 finetuned on Korean Dataset
### Tokenizer
We first trained a tokenizer on OSCAR's `unshuffled_original_ko` Korean data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-ko/experiments/ex... | {"language": ["ko"], "license": "mit", "tags": ["text-generation"], "datasets": ["oscar"], "widget": [{"text": "\ubaa8\ub4e0\uc0ac\ub78c\uc740\uad50\uc721\uc744 "}]} | yongzx/gpt2-finetuned-oscar-ko | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"ko",
"dataset:oscar",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #text-generation #ko #dataset-oscar #license-mit #endpoints_compatible #text-generation-inference #region-us
| # GPT-2 finetuned on Korean Dataset
### Tokenizer
We first trained a tokenizer on OSCAR's 'unshuffled_original_ko' Korean data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the Python file for the training.
### Model
We finetuned the 'wte' and 'wpe' layers of GPT-2 (while freezi... | [
"# GPT-2 finetuned on Korean Dataset",
"### Tokenizer\nWe first trained a tokenizer on OSCAR's 'unshuffled_original_ko' Korean data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the Python file for the training.",
"### Model\nWe finetuned the 'wte' and 'wpe' layers of GP... | [
"TAGS\n#transformers #pytorch #gpt2 #feature-extraction #text-generation #ko #dataset-oscar #license-mit #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-2 finetuned on Korean Dataset",
"### Tokenizer\nWe first trained a tokenizer on OSCAR's 'unshuffled_original_ko' Korean data subset by ... |
fill-mask | transformers | # SciBERT Longformer
This is a Lonformer version of the [SciBERT uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model by Allen AI. The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider `max_seq_length` (4096 vs. 512) which is handy in the case of working with long text... | {"language": ["en"], "license": "mit"} | yorko/scibert_scivocab_uncased_long_4096 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # SciBERT Longformer
This is a Lonformer version of the SciBERT uncased model by Allen AI. The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider 'max_seq_length' (4096 vs. 512) which is handy in the case of working with long texts, e.g. scientific full texts.
The conversion to Longform... | [
"# SciBERT Longformer\n\nThis is a Lonformer version of the SciBERT uncased model by Allen AI. The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wider 'max_seq_length' (4096 vs. 512) which is handy in the case of working with long texts, e.g. scientific full texts. \n\nThe conversion to... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# SciBERT Longformer\n\nThis is a Lonformer version of the SciBERT uncased model by Allen AI. The model is slower than SciBERT (~2.5x in my benchmarks) but can allow for 8x wid... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 470512388
- CO2 Emissions (in grams): 256.38650494338367
## Validation Metrics
- Loss: 0.18712733685970306
- Accuracy: 0.9388
- Precision: 0.9300274402195218
- Recall: 0.949
- AUC: 0.98323192
- F1: 0.9394179370421698
## Usage
You can ... | {"language": "en", "tags": "autonlp", "datasets": ["yosemite/autonlp-data-imdb-sentiment-analysis-english"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 256.38650494338367} | yosemite/autonlp-imdb-sentiment-analysis-english-470512388 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:yosemite/autonlp-data-imdb-sentiment-analysis-english",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-yosemite/autonlp-data-imdb-sentiment-analysis-english #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 470512388
- CO2 Emissions (in grams): 256.38650494338367
## Validation Metrics
- Loss: 0.18712733685970306
- Accuracy: 0.9388
- Precision: 0.9300274402195218
- Recall: 0.949
- AUC: 0.98323192
- F1: 0.9394179370421698
## Usage
You can ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 470512388\n- CO2 Emissions (in grams): 256.38650494338367",
"## Validation Metrics\n\n- Loss: 0.18712733685970306\n- Accuracy: 0.9388\n- Precision: 0.9300274402195218\n- Recall: 0.949\n- AUC: 0.98323192\n- F1: 0.9394179370421698"... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-yosemite/autonlp-data-imdb-sentiment-analysis-english #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 470512388\n- CO2 E... |
text-classification | transformers |
`bert-base-uncased` fine-tuned on CoLA dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as thos... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "cola", "glue", "torchdistill"], "datasets": ["cola"], "metrics": ["matthew's correlation"]} | yoshitomo-matsubara/bert-base-uncased-cola | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cola",
"glue",
"torchdistill",
"en",
"dataset:cola",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #cola #glue #torchdistill #en #dataset-cola #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on CoLA dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, an... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #cola #glue #torchdistill #en #dataset-cola #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on CoLA dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb)... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "cola", "glue", "kd", "torchdistill"], "datasets": ["cola"], "metrics": ["matthew's correlation"]} | yoshitomo-matsubara/bert-base-uncased-cola_from_bert-large-uncased-cola | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cola",
"glue",
"kd",
"torchdistill",
"en",
"dataset:cola",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #cola #glue #kd #torchdistill #en #dataset-cola #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on CoLA dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #cola #glue #kd #torchdistill #en #dataset-cola #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on MNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as thos... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "mnli", "ax", "glue", "torchdistill"], "datasets": ["mnli", "ax"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-mnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"mnli",
"ax",
"glue",
"torchdistill",
"en",
"dataset:mnli",
"dataset:ax",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #mnli #ax #glue #torchdistill #en #dataset-mnli #dataset-ax #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on MNLI dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, an... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #mnli #ax #glue #torchdistill #en #dataset-mnli #dataset-ax #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on MNLI dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb)... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "mnli", "ax", "glue", "kd", "torchdistill"], "datasets": ["mnli", "ax"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-mnli_from_bert-large-uncased-mnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"mnli",
"ax",
"glue",
"kd",
"torchdistill",
"en",
"dataset:mnli",
"dataset:ax",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #mnli #ax #glue #kd #torchdistill #en #dataset-mnli #dataset-ax #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on MNLI dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #mnli #ax #glue #kd #torchdistill #en #dataset-mnli #dataset-ax #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on MRPC dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as thos... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "mrpc", "glue", "torchdistill"], "datasets": ["mrpc"], "metrics": ["f1", "accuracy"]} | yoshitomo-matsubara/bert-base-uncased-mrpc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"mrpc",
"glue",
"torchdistill",
"en",
"dataset:mrpc",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #mrpc #glue #torchdistill #en #dataset-mrpc #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on MRPC dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, an... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #mrpc #glue #torchdistill #en #dataset-mrpc #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on MRPC dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb)... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "mrpc", "glue", "kd", "torchdistill"], "datasets": ["mrpc"], "metrics": ["f1", "accuracy"]} | yoshitomo-matsubara/bert-base-uncased-mrpc_from_bert-large-uncased-mrpc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"mrpc",
"glue",
"kd",
"torchdistill",
"en",
"dataset:mrpc",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #mrpc #glue #kd #torchdistill #en #dataset-mrpc #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on MRPC dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #mrpc #glue #kd #torchdistill #en #dataset-mrpc #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on QNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as thos... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "qnli", "glue", "torchdistill"], "datasets": ["qnli"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-qnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"qnli",
"glue",
"torchdistill",
"en",
"dataset:qnli",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #qnli #glue #torchdistill #en #dataset-qnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on QNLI dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, an... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #qnli #glue #torchdistill #en #dataset-qnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on QNLI dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb)... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "qnli", "glue", "kd", "torchdistill"], "datasets": ["qnli"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-qnli_from_bert-large-uncased-qnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"qnli",
"glue",
"kd",
"torchdistill",
"en",
"dataset:qnli",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #qnli #glue #kd #torchdistill #en #dataset-qnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on QNLI dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #qnli #glue #kd #torchdistill #en #dataset-qnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on QQP dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as those... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "qqp", "glue", "torchdistill"], "datasets": ["qqp"], "metrics": ["f1", "accuracy"]} | yoshitomo-matsubara/bert-base-uncased-qqp | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"qqp",
"glue",
"torchdistill",
"en",
"dataset:qqp",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #qqp #glue #torchdistill #en #dataset-qqp #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on QQP dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #qqp #glue #torchdistill #en #dataset-qqp #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on QQP dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) ... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "qqp", "glue", "kd", "torchdistill"], "datasets": ["qqp"], "metrics": ["f1", "accuracy"]} | yoshitomo-matsubara/bert-base-uncased-qqp_from_bert-large-uncased-qqp | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"qqp",
"glue",
"kd",
"torchdistill",
"en",
"dataset:qqp",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #qqp #glue #kd #torchdistill #en #dataset-qqp #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on QQP dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE s... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #qqp #glue #kd #torchdistill #en #dataset-qqp #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on RTE dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as those... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "rte", "glue", "torchdistill"], "datasets": ["rte"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-rte | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"rte",
"glue",
"torchdistill",
"en",
"dataset:rte",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #rte #glue #torchdistill #en #dataset-rte #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on RTE dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #rte #glue #torchdistill #en #dataset-rte #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on RTE dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) ... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "rte", "glue", "kd", "torchdistill"], "datasets": ["rte"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-rte_from_bert-large-uncased-rte | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"rte",
"glue",
"kd",
"torchdistill",
"en",
"dataset:rte",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #rte #glue #kd #torchdistill #en #dataset-rte #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on RTE dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE s... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #rte #glue #kd #torchdistill #en #dataset-rte #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on SST-2 dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as tho... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "sst2", "glue", "torchdistill"], "datasets": ["sst2"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-sst2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sst2",
"glue",
"torchdistill",
"en",
"dataset:sst2",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #sst2 #glue #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on SST-2 dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, a... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #sst2 #glue #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on SST-2 dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "sst2", "glue", "kd", "torchdistill"], "datasets": ["sst2"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-sst2_from_bert-large-uncased-sst2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sst2",
"glue",
"kd",
"torchdistill",
"en",
"dataset:sst2",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #sst2 #glue #kd #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on SST-2 dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #sst2 #glue #kd #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on STS-B dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as tho... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "stsb", "glue", "torchdistill"], "datasets": ["stsb"], "metrics": ["pearson correlation", "spearman correlation"]} | yoshitomo-matsubara/bert-base-uncased-stsb | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"stsb",
"glue",
"torchdistill",
"en",
"dataset:stsb",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #stsb #glue #torchdistill #en #dataset-stsb #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on STS-B dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, a... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #stsb #glue #torchdistill #en #dataset-stsb #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on STS-B dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "stsb", "glue", "kd", "torchdistill"], "datasets": ["stsb"], "metrics": ["pearson correlation", "spearman correlation"]} | yoshitomo-matsubara/bert-base-uncased-stsb_from_bert-large-uncased-stsb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"stsb",
"glue",
"kd",
"torchdistill",
"en",
"dataset:stsb",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #stsb #glue #kd #torchdistill #en #dataset-stsb #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on STS-B dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #stsb #glue #kd #torchdistill #en #dataset-stsb #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on WNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as thos... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "wnli", "glue", "torchdistill"], "datasets": ["wnli"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-wnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"wnli",
"glue",
"torchdistill",
"en",
"dataset:wnli",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #wnli #glue #torchdistill #en #dataset-wnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on WNLI dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, an... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #wnli #glue #torchdistill #en #dataset-wnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-base-uncased` fine-tuned on WNLI dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb)... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "wnli", "glue", "kd", "torchdistill"], "datasets": ["wnli"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"wnli",
"glue",
"kd",
"torchdistill",
"en",
"dataset:wnli",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #wnli #glue #kd #torchdistill #en #dataset-wnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-base-uncased' fine-tuned on WNLI dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.
The training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #wnli #glue #kd #torchdistill #en #dataset-wnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on CoLA dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as tho... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "cola", "glue", "torchdistill"], "datasets": ["cola"], "metrics": ["matthew's correlation"]} | yoshitomo-matsubara/bert-large-uncased-cola | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cola",
"glue",
"torchdistill",
"en",
"dataset:cola",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #cola #glue #torchdistill #en #dataset-cola #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on CoLA dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, a... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #cola #glue #torchdistill #en #dataset-cola #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on MNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as tho... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "mnli", "ax", "glue", "torchdistill"], "datasets": ["mnli", "ax"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-large-uncased-mnli | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"mnli",
"ax",
"glue",
"torchdistill",
"en",
"dataset:mnli",
"dataset:ax",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #mnli #ax #glue #torchdistill #en #dataset-mnli #dataset-ax #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on MNLI dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, a... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #mnli #ax #glue #torchdistill #en #dataset-mnli #dataset-ax #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on MRPC dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as tho... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "mrpc", "glue", "torchdistill"], "datasets": ["mrpc"], "metrics": ["f1", "accuracy"]} | yoshitomo-matsubara/bert-large-uncased-mrpc | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"mrpc",
"glue",
"torchdistill",
"en",
"dataset:mrpc",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #mrpc #glue #torchdistill #en #dataset-mrpc #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on MRPC dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, a... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #mrpc #glue #torchdistill #en #dataset-mrpc #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on QNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as tho... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "qnli", "glue", "torchdistill"], "datasets": ["qnli"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-large-uncased-qnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"qnli",
"glue",
"torchdistill",
"en",
"dataset:qnli",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #qnli #glue #torchdistill #en #dataset-qnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on QNLI dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, a... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #qnli #glue #torchdistill #en #dataset-qnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on QQP dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as thos... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "qqp", "glue", "torchdistill"], "datasets": ["qqp"], "metrics": ["f1", "accuracy"]} | yoshitomo-matsubara/bert-large-uncased-qqp | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"qqp",
"glue",
"torchdistill",
"en",
"dataset:qqp",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #qqp #glue #torchdistill #en #dataset-qqp #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on QQP dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, an... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #qqp #glue #torchdistill #en #dataset-qqp #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on RTE dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as thos... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "rte", "glue", "torchdistill"], "datasets": ["rte"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-large-uncased-rte | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"rte",
"glue",
"torchdistill",
"en",
"dataset:rte",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #rte #glue #torchdistill #en #dataset-rte #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on RTE dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, an... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rte #glue #torchdistill #en #dataset-rte #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on SST-2 dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as th... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "sst2", "glue", "torchdistill"], "datasets": ["sst2"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-large-uncased-sst2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sst2",
"glue",
"torchdistill",
"en",
"dataset:sst2",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sst2 #glue #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on SST-2 dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sst2 #glue #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on STS-B dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as th... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "stsb", "glue", "torchdistill"], "datasets": ["stsb"], "metrics": ["pearson correlation", "spearman correlation"]} | yoshitomo-matsubara/bert-large-uncased-stsb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"stsb",
"glue",
"torchdistill",
"en",
"dataset:stsb",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #stsb #glue #torchdistill #en #dataset-stsb #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on STS-B dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #stsb #glue #torchdistill #en #dataset-stsb #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
`bert-large-uncased` fine-tuned on WNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as tho... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "wnli", "glue", "torchdistill"], "datasets": ["wnli"], "metrics": ["accuracy"]} | yoshitomo-matsubara/bert-large-uncased-wnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"wnli",
"glue",
"torchdistill",
"en",
"dataset:wnli",
"arxiv:2310.17644",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2310.17644"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #wnli #glue #torchdistill #en #dataset-wnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'bert-large-uncased' fine-tuned on WNLI dataset, using *torchdistill* and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, a... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #wnli #glue #torchdistill #en #dataset-wnli #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# AI-DAY-distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "AI-DAY-distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glu... | younes9/AI-DAY-distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| AI-DAY-distilbert-base-uncased-finetuned-cola
=============================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7236
* Matthews Correlation: 0.5382
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
question-answering | transformers |
<!-- 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 squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | youngjae/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of 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
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
fill-mask | transformers |
# ukr-roberta-base
## Pre-training corpora
Below is the list of corpora used along with the output of wc command (counting lines, words and characters). These corpora were concatenated and tokenized with HuggingFace Roberta Tokenizer.
| Tables | Lines | Words | Characters |
| ------------- |------... | {"language": ["uk"]} | youscan/ukr-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"uk",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #uk #autotrain_compatible #endpoints_compatible #region-us
| ukr-roberta-base
================
Pre-training corpora
--------------------
Below is the list of corpora used along with the output of wc command (counting lines, words and characters). These corpora were concatenated and tokenized with HuggingFace Roberta Tokenizer.
Pre-training details
--------------------
*... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #uk #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | <!--
* @Description:
* @Version:
* @Author: Hardy
* @Date: 2022-02-09 15:13:53
* @LastEditors: Hardy
* @LastEditTime: 2022-02-09 16:59:01
-->
<br />
<p align="center">
<h1 align="center">clip-product-title-chinese</h1>
</p>
## 基于有赞商品图片和标题语料训练的clip模型。
## Usage
使用模型前，请 git clone https://github.com/youzanai/... | {} | youzanai/clip-product-title-chinese | null | [
"transformers",
"pytorch",
"clip_chinese_model",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #clip_chinese_model #endpoints_compatible #region-us
|
<br />
<p align="center">
<h1 align="center">clip-product-title-chinese</h1>
</p>
## 基于有赞商品图片和标题语料训练的clip模型。
## Usage
使用模型前，请 git clone URL
| [
"## 基于有赞商品图片和标题语料训练的clip模型。",
"## Usage\n使用模型前，请 git clone URL"
] | [
"TAGS\n#transformers #pytorch #clip_chinese_model #endpoints_compatible #region-us \n",
"## 基于有赞商品图片和标题语料训练的clip模型。",
"## Usage\n使用模型前，请 git clone URL"
] |
null | transformers |
# MobileBERT 日本語事前学習済みモデル爆誕！！
AI関係の仕事をしている櫻本です。
2020年に発表されたBERTの発展型モデルの一つである「MobileBERT」の、日本語事前学習済みモデルを構築しました。
このページを見つけた方はかなりラッキーですから、ぜひ一度使ってみてください！！
BERTの推論速度の遅さを嘆いている方にお薦めです。
# 利用方法
既にtransformersでBERTを利用されている方向けの説明です。
トークナイザは東北大学さんのモデル(cl-tohoku/bert-large-japanese)からお借りしましたのでご指定ください。
後は、**BertFor**なんちゃ... | {"language": "ja", "license": "cc-by-sa-3.0", "tags": ["mobilebert"], "datasets": ["wikipedia"]} | ysakuramoto/mobilebert-ja | null | [
"transformers",
"pytorch",
"mobilebert",
"ja",
"dataset:wikipedia",
"arxiv:2004.02984",
"license:cc-by-sa-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02984"
] | [
"ja"
] | TAGS
#transformers #pytorch #mobilebert #ja #dataset-wikipedia #arxiv-2004.02984 #license-cc-by-sa-3.0 #endpoints_compatible #region-us
|
# MobileBERT 日本語事前学習済みモデル爆誕！！
AI関係の仕事をしている櫻本です。
2020年に発表されたBERTの発展型モデルの一つである「MobileBERT」の、日本語事前学習済みモデルを構築しました。
このページを見つけた方はかなりラッキーですから、ぜひ一度使ってみてください！！
BERTの推論速度の遅さを嘆いている方にお薦めです。
# 利用方法
既にtransformersでBERTを利用されている方向けの説明です。
トークナイザは東北大学さんのモデル(cl-tohoku/bert-large-japanese)からお借りしましたのでご指定ください。
後は、BertForなんちゃら～のク... | [
"# MobileBERT 日本語事前学習済みモデル爆誕！！\nAI関係の仕事をしている櫻本です。 \n2020年に発表されたBERTの発展型モデルの一つである「MobileBERT」の、日本語事前学習済みモデルを構築しました。 \nこのページを見つけた方はかなりラッキーですから、ぜひ一度使ってみてください！！ \nBERTの推論速度の遅さを嘆いている方にお薦めです。",
"# 利用方法\n既にtransformersでBERTを利用されている方向けの説明です。 \nトークナイザは東北大学さんのモデル(cl-tohoku/bert-large-japanese)からお借りしましたのでご指定ください。 \n後は、... | [
"TAGS\n#transformers #pytorch #mobilebert #ja #dataset-wikipedia #arxiv-2004.02984 #license-cc-by-sa-3.0 #endpoints_compatible #region-us \n",
"# MobileBERT 日本語事前学習済みモデル爆誕！！\nAI関係の仕事をしている櫻本です。 \n2020年に発表されたBERTの発展型モデルの一つである「MobileBERT」の、日本語事前学習済みモデルを構築しました。 \nこのページを見つけた方はかなりラッキーですから、ぜひ一度使ってみてください！！ \nBERTの推論速度... |
text2text-generation | transformers | # T5-base data to text model specialized for Finance NLG
__complete version__
----
## Usage (HuggingFace Transformers)
#### Call the model
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("yseop/FNP_T5_D2T_complete")
model = AutoModelForSeq2SeqL... | {} | yseop/FNP_T5_D2T_complete | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # T5-base data to text model specialized for Finance NLG
__complete version__
----
## Usage (HuggingFace Transformers)
#### Call the model
#### Choose a generation method
Created by: Yseop | Pioneer in Natural Language Generation (NLG) technology. Scaling human expertise through Natural Language Genera... | [
"# T5-base data to text model specialized for Finance NLG\n\n__complete version__\n\n----",
"## Usage (HuggingFace Transformers)",
"#### Call the model",
"#### Choose a generation method \n\n\n\n\n\n\n\n\n\nCreated by: Yseop | Pioneer in Natural Language Generation (NLG) technology. Scaling human expertise th... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5-base data to text model specialized for Finance NLG\n\n__complete version__\n\n----",
"## Usage (HuggingFace Transformers)",
"#### Call the model",
... |
text2text-generation | transformers | # T5-base data to text model specialized for Finance NLG
__simple version__
This model was trained on a limited number of indicators, values and dates
----
## Usage (HuggingFace Transformers)
#### Call the model
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokeni... | {} | yseop/FNP_T5_D2T_simple | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # T5-base data to text model specialized for Finance NLG
__simple version__
This model was trained on a limited number of indicators, values and dates
----
## Usage (HuggingFace Transformers)
#### Call the model
#### Choose a generation method
Created by: Yseop | Pioneer in Natural Language Genera... | [
"# T5-base data to text model specialized for Finance NLG\n\n __simple version__\n \n This model was trained on a limited number of indicators, values and dates\n\n----",
"## Usage (HuggingFace Transformers)",
"#### Call the model",
"#### Choose a generation method \n\n\n\n\n\n\n\n\n\n\nCreated by: Yseop | Pi... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5-base data to text model specialized for Finance NLG\n\n __simple version__\n \n This model was trained on a limited number of indicators, values and dates... |
text-classification | transformers |
<div style="clear: both;">
<div style="float: left; margin-right 1em;">
<h1><strong>FReE (Financial Relation Extraction)</strong></h1>
</div>
<div>
<h2><img src="https://pbs.twimg.com/profile_images/1333760924914753538/fQL4zLUw_400x400.png" alt="" width="25" height="25"></h2>
</div>
</div>
We presen... | {"tags": ["feature-extraction", "text-classification"], "inference": true, "pipeline_tag": "text-classification", "library": "pytorch"} | yseop/distilbert-base-financial-relation-extraction | null | [
"transformers",
"pytorch",
"feature-extraction",
"text-classification",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #feature-extraction #text-classification #endpoints_compatible #has_space #region-us
|
**FReE (Financial Relation Extraction)**
========================================
We present FReE, a DistilBERT base model fine-tuned on a custom financial dataset for financial relation type detection and classification.
Process
-------
Detecting the presence of a relationship between financial terms and ... | [] | [
"TAGS\n#transformers #pytorch #feature-extraction #text-classification #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
<div style="clear: both;">
<div style="float: left; margin-right 1em;">
<h1><strong>FinISH (Finance-Identifying Sroberta for Hypernyms)</strong></h1>
</div>
<div>
<h2><img src="https://pbs.twimg.com/profile_images/1333760924914753538/fQL4zLUw_400x400.png" alt="" width="25" height="25"></h2>
</div>
</... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "inference": false, "pipeline_tag": "sentence-similarity"} | yseop/roberta-base-finance-hypernym-identification | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2108.09485",
"arxiv:1908.10084",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.09485",
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-2108.09485 #arxiv-1908.10084 #has_space #region-us
|
<div style="clear: both;">
<div style="float: left; margin-right 1em;">
<h1><strong>FinISH (Finance-Identifying Sroberta for Hypernyms)</strong></h1>
</div>
<div>
<h2><img src="URL alt="" width="25" height="25"></h2>
</div>
</div>
We present FinISH, a SRoBERTa base model fine-tuned on the FIBO ont... | [
"## SRoBERTa Model Architecture\nSentence-RoBERTa (SRoBERTa) is a modification of the pretrained RoBERTa network that uses siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar p... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-2108.09485 #arxiv-1908.10084 #has_space #region-us \n",
"## SRoBERTa Model Architecture\nSentence-RoBERTa (SRoBERTa) is a modification of the pretrained RoBERTa network that uses siamese and triplet netwo... |
null | null | # Dummy model
This is just a dummy model. Copying bert-base-uncased model files over here. | {} | ysharma/new-model-dummy | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Dummy model
This is just a dummy model. Copying bert-base-uncased model files over here. | [
"# Dummy model \n\nThis is just a dummy model. Copying bert-base-uncased model files over here."
] | [
"TAGS\n#region-us \n",
"# Dummy model \n\nThis is just a dummy model. Copying bert-base-uncased model files over here."
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863
## Validation Metrics
- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
-... | {"language": "zh", "tags": "autonlp", "datasets": ["ysslang/autonlp-data-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 10.9230691350863} | ysslang/autonlp-test-459011902 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"zh",
"dataset:ysslang/autonlp-data-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-ysslang/autonlp-data-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863
## Validation Metrics
- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
-... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 459011902\n- CO2 Emissions (in grams): 10.9230691350863",
"## Validation Metrics\n\n- Loss: 0.7189690470695496\n- Accuracy: 0.7453263867606497\n- Macro F1: 0.630810193227066\n- Micro F1: 0.7453263867606497\n- Weighted F1: 0.... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-ysslang/autonlp-data-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 459011902\n- CO2 Emissions (in grams): 10... |
text2text-generation | transformers |
TBA | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | yuchenlin/BART0-base | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
TBA | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
A BART-large version of T0.
Please check https://inklab.usc.edu/ReCross/ for more details. | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | yuchenlin/BART0 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
A BART-large version of T0.
Please check URL for more details. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
TBA | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | yuchenlin/BART0_CSR | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
TBA | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
TBA | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | yuchenlin/BART0pp-base | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
TBA | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
TBA | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | yuchenlin/BART0pp | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
TBA | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-bigscience/P3 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | Fine-tune snips dataset for SLU task using pretrained ASR model with hubert feature
---
language:
- en
receipe: "https://github.com/espnet/espnet/tree/master/egs2/snips/asr1"
datasets:
- snips: smart-lights-en-close-field
metrics:
- F1 score: 91.7
--- | {} | yuekai/espnet-slu-snips | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Fine-tune snips dataset for SLU task using pretrained ASR model with hubert feature
---
language:
- en
receipe: "URL
datasets:
- snips: smart-lights-en-close-field
metrics:
- F1 score: 91.7
--- | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | yunsizhang/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2292
* Accuracy: 0.926
* F1: 0.9259
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
question-answering | transformers | # Turkish Question Answering Model : Question Answering
Inspired by savasy/bert-base-turkish-squad,
* Inspired model: https://huggingface.co/savasy/bert-base-turkish-squad
* BERT-base: https://huggingface.co/dbmdz/bert-base-turkish-uncased
* Dataset: Private QnA Chatbot Database
# Training Code
```
model_args = Que... | {"language": "tr"} | yunusemreemik/logo-qna-model | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #question-answering #tr #endpoints_compatible #region-us
| # Turkish Question Answering Model : Question Answering
Inspired by savasy/bert-base-turkish-squad,
* Inspired model: URL
* BERT-base: URL
* Dataset: Private QnA Chatbot Database
# Training Code
# Dataset Sample
# Example Usage
> Load Model
> Apply the model.
> Please dont forget the delete backslashes "\" befo... | [
"# Turkish Question Answering Model : Question Answering\n\nInspired by savasy/bert-base-turkish-squad, \n* Inspired model: URL\n* BERT-base: URL\n* Dataset: Private QnA Chatbot Database",
"# Training Code",
"# Dataset Sample",
"# Example Usage\n\n> Load Model\n\n> Apply the model.\n> Please dont forget the ... | [
"TAGS\n#transformers #pytorch #bert #question-answering #tr #endpoints_compatible #region-us \n",
"# Turkish Question Answering Model : Question Answering\n\nInspired by savasy/bert-base-turkish-squad, \n* Inspired model: URL\n* BERT-base: URL\n* Dataset: Private QnA Chatbot Database",
"# Training Code",
"# ... |
text-generation | transformers |
# George Costanza Model
| {"tags": ["conversational"]} | yusufmorsi/georgebot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# George Costanza Model
| [
"# George Costanza Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# George Costanza Model"
] |
summarization | transformers |
# Summarization
## Model description
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
## How to use
PyTorch model available
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrain... | {"language": "en", "tags": ["summarization"]} | yuvraj/summarizer-cnndm | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #autotrain_compatible #endpoints_compatible #region-us
|
# Summarization
## Model description
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
## How to use
PyTorch model available
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrain... | [
"# Summarization\n",
"## Model description\n\nBartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset\n",
"## How to use\n\nPyTorch model available\n\n'''python\nfrom transformers import AutoTokenizer, AutoModelWithLMHead, pipeline\n\ntokenizer = Aut... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# Summarization\n",
"## Model description\n\nBartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset\n",
"## How ... |
summarization | transformers |
## Model description
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
## How to use
PyTorch model available
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("yuvraj/... | {"language": "en", "tags": ["summarization", "extreme summarization"]} | yuvraj/xSumm | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"extreme summarization",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #extreme summarization #en #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
## How to use
PyTorch model available
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("yuvraj/... | [
"## Model description\n\nBartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article\n",
"## How to use\n\nPyTorch model available\n\n'''python\nfrom transformers import AutoTokenizer, AutoModelWithLMHead, pipeline\n\ntokenizer = AutoTokenizer.from_p... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #extreme summarization #en #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model description\n\nBartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article\n",
"#... |
text-to-speech | transformers |
# Coqui Model for TTS
```
pip install TTS
git clone https://huggingface.co/z-uo/glowtts-female-it
# predict one
tts --text "ciao pluto" --model_path "glowtts-female-it/best_model.pth.tar" --config_path "glowtts-female-it/config.json"
# predict server
tts-server --model_path "glowtts-female-it/best_model.pth.tar" --con... | {"language": ["it"], "tags": ["text-to-speech"], "datasets": ["z-uo/female-LJSpeech-italian"], "model-index": [{"name": "glowtts-male-it", "results": []}]} | z-uo/glowtts-female-it | null | [
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:z-uo/female-LJSpeech-italian",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #tensorboard #text-to-speech #it #dataset-z-uo/female-LJSpeech-italian #endpoints_compatible #region-us
|
# Coqui Model for TTS
More information about training script in this repo. | [
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] | [
"TAGS\n#transformers #tensorboard #text-to-speech #it #dataset-z-uo/female-LJSpeech-italian #endpoints_compatible #region-us \n",
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] |
text-to-speech | transformers |
# Coqui Model for TTS
```
pip install TTS
git clone https://huggingface.co/z-uo/glowtts-male-it
# predict one
server --text "ciao pluto" --model_path "glowtts-male-it/GOOD_best_model_3840.pth.tar" --config_path "glowtts-male-it/config.json"
# predict server
tts-server --model_path "glowtts-male-it/GOOD_best_model_3840... | {"language": ["it"], "tags": ["text-to-speech"], "datasets": ["z-uo/male-LJSpeech-italian"], "model-index": [{"name": "glowtts-male-it", "results": []}]} | z-uo/glowtts-male-it | null | [
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:z-uo/male-LJSpeech-italian",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #tensorboard #text-to-speech #it #dataset-z-uo/male-LJSpeech-italian #endpoints_compatible #region-us
|
# Coqui Model for TTS
More information about training script in this repo. | [
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] | [
"TAGS\n#transformers #tensorboard #text-to-speech #it #dataset-z-uo/male-LJSpeech-italian #endpoints_compatible #region-us \n",
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] |
text2text-generation | transformers |
# Question and Answer with Italian T5
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB).
To use add a question + context in the same string for example:
`... | {"language": ["it"], "tags": ["text2text_generation", "question_answering"], "datasets": ["z-uo/squad-it"], "model-index": [{"name": "it5-squadv1-it", "results": []}]} | z-uo/it5-squadv1-it | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2text_generation",
"question_answering",
"it",
"dataset:z-uo/squad-it",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #text2text_generation #question_answering #it #dataset-z-uo/squad-it #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Question and Answer with Italian T5
This model is a fine-tuned version of gsarti/it5-base on Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB).
To use add a question + context in the same string for example:
The train achieves the following results/params:
- epoch: 2.0
- train_loss: 0.1064
- train_sa... | [
"# Question and Answer with Italian T5\n\nThis model is a fine-tuned version of gsarti/it5-base on Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB).\n\nTo use add a question + context in the same string for example:\n\n\nThe train achieves the following results/params:\n - epoch: 2.0\n - train_loss: 0.106... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #text2text_generation #question_answering #it #dataset-z-uo/squad-it #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Question and Answer with Italian T5\n\nThis model is a fine-tuned version of gsarti/it5-... |
text-to-speech | transformers |
# Coqui Model for TTS
```
pip install TTS
git clone https://huggingface.co/z-uo/vits-female-it
# predict one
tts --text "ciao pluto" --model_path "vits-female-it/best_model.pth.tar" --config_path "vits-female-it/config.json"
# predict server
tts-server --model_path "vits-female-it/best_model.pth.tar" --config_path "vi... | {"language": ["it"], "tags": ["text-to-speech"], "datasets": ["z-uo/female-LJSpeech-italian"], "model-index": [{"name": "vits-female-it", "results": []}]} | z-uo/vits-female-it | null | [
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:z-uo/female-LJSpeech-italian",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #tensorboard #text-to-speech #it #dataset-z-uo/female-LJSpeech-italian #endpoints_compatible #region-us
|
# Coqui Model for TTS
More information about training script in this repo. | [
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] | [
"TAGS\n#transformers #tensorboard #text-to-speech #it #dataset-z-uo/female-LJSpeech-italian #endpoints_compatible #region-us \n",
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] |
text-to-speech | transformers |
# Coqui Model for TTS
```
pip install TTS
git clone https://huggingface.co/z-uo/vits-male-it
# predict one
tts --text "ciao pluto" --model_path "vits-male-it/best_model.pth.tar" --config_path "vits-male-it/config.json"
# predict server
tts-server --model_path "vits-male-it/best_model.pth.tar" --config_path "vits-male-... | {"language": ["it"], "tags": ["text-to-speech"], "datasets": ["z-uo/female-LJSpeech-italian"], "model-index": [{"name": "vits-male-it", "results": []}]} | z-uo/vits-male-it | null | [
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:z-uo/female-LJSpeech-italian",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #tensorboard #text-to-speech #it #dataset-z-uo/female-LJSpeech-italian #endpoints_compatible #region-us
|
# Coqui Model for TTS
More information about training script in this repo. | [
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] | [
"TAGS\n#transformers #tensorboard #text-to-speech #it #dataset-z-uo/female-LJSpeech-italian #endpoints_compatible #region-us \n",
"# Coqui Model for TTS\n\nMore information about training script in this repo."
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | z3c1f4/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7400
* Matthews Correlation: 0.5321
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | null | # CascadeNet-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and limi... | {} | zaccharieramzi/CascadeNet-OASIS | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # CascadeNet-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see URL
This section is WIP.
## Intended uses and limitations
This model can be used to reco... | [
"# CascadeNet-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended uses and limitations\nThis... | [
"TAGS\n#region-us \n",
"# CascadeNet-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended u... |
null | null | # CascadeNet-fastmri
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses an... | {} | zaccharieramzi/CascadeNet-fastmri | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| CascadeNet-fastmri
==================
---
tags:
* TensorFlow
* MRI reconstruction
* MRI
datasets:
* fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
Model description
-----------------
For more details, see URL
This section is WIP.
Intended... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # KIKI-net-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and limita... | {} | zaccharieramzi/KIKI-net-OASIS | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # KIKI-net-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see URL
This section is WIP.
## Intended uses and limitations
This model can be used to recons... | [
"# KIKI-net-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended uses and limitations\nThis m... | [
"TAGS\n#region-us \n",
"# KIKI-net-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended use... |
null | null | # KIKI-net-fastmri
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and ... | {} | zaccharieramzi/KIKI-net-fastmri | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| KIKI-net-fastmri
================
---
tags:
* TensorFlow
* MRI reconstruction
* MRI
datasets:
* fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
Model description
-----------------
For more details, see URL
This section is WIP.
Intended use... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # NCPDNet-3D
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This is a non-Cartesian 3D MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal.inria.fr/hal-03188997.... | {} | zaccharieramzi/NCPDNet-3D | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # NCPDNet-3D
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This is a non-Cartesian 3D MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see URL
This section is WIP.
## Inten... | [
"# NCPDNet-3D\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis is a non-Cartesian 3D MRI reconstruction model for radial trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model description\nFor more details, see URL\nThis sec... | [
"TAGS\n#region-us \n",
"# NCPDNet-3D\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis is a non-Cartesian 3D MRI reconstruction model for radial trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model description\nFor more d... |
null | null | # NCPDNet-multicoil-radial
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian multicoil MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal... | {} | zaccharieramzi/NCPDNet-multicoil-radial | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # NCPDNet-multicoil-radial
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian multicoil MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see URL
This se... | [
"# NCPDNet-multicoil-radial\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian multicoil MRI reconstruction model for radial trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model description\nFor more det... | [
"TAGS\n#region-us \n",
"# NCPDNet-multicoil-radial\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian multicoil MRI reconstruction model for radial trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model ... |
null | null | # NCPDNet-multicoil-spiral
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian multicoil MRI reconstruction model for spiral trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal... | {} | zaccharieramzi/NCPDNet-multicoil-spiral | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # NCPDNet-multicoil-spiral
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian multicoil MRI reconstruction model for spiral trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see URL
This se... | [
"# NCPDNet-multicoil-spiral\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian multicoil MRI reconstruction model for spiral trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model description\nFor more det... | [
"TAGS\n#region-us \n",
"# NCPDNet-multicoil-spiral\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian multicoil MRI reconstruction model for spiral trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model ... |
null | null | # NCPDNet-singlecoil-radial
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal.inria.fr... | {} | zaccharieramzi/NCPDNet-singlecoil-radial | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # NCPDNet-singlecoil-radial
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see URL
This section is ... | [
"# NCPDNet-singlecoil-radial\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian MRI reconstruction model for radial trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model description\nFor more details, see... | [
"TAGS\n#region-us \n",
"# NCPDNet-singlecoil-radial\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian MRI reconstruction model for radial trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model descripti... |
null | null | # NCPDNet-singlecoil-spiral
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian MRI reconstruction model for spiral trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal.inria.fr... | {} | zaccharieramzi/NCPDNet-singlecoil-spiral | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # NCPDNet-singlecoil-spiral
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian MRI reconstruction model for spiral trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see URL
This section is ... | [
"# NCPDNet-singlecoil-spiral\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian MRI reconstruction model for spiral trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model description\nFor more details, see... | [
"TAGS\n#region-us \n",
"# NCPDNet-singlecoil-spiral\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- fastMRI\n---\n\nThis is a non-Cartesian MRI reconstruction model for spiral trajectories at acceleration factor 4.\nThe model uses 10 iterations and a small vanilla CNN.",
"## Model descripti... |
null | null | # PDNet-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and limitatio... | {} | zaccharieramzi/PDNet-OASIS | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # PDNet-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---
This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.
## Model description
For more details, see URL
This section is WIP.
## Intended uses and limitations
This model can be used to reconstru... | [
"# PDNet-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended uses and limitations\nThis mode... | [
"TAGS\n#region-us \n",
"# PDNet-OASIS\n---\ntags:\n- TensorFlow\n- MRI reconstruction\n- MRI\ndatasets:\n- OASIS\n---\n\nThis model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.",
"## Model description\nFor more details, see URL\nThis section is WIP.",
"## Intended uses a... |
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