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null | transformers |
# BioBERTurk- Turkish Biomedical Language Models
BioBERTurk on Huggingface repo:
BioBERTurkcased-(con)+(trM): BioBERTurkcased-(con)+(trM), was pretrained only on [Turkish biomedical text](https://huggingface.co/datasets/hazal/Turkish-Biomedical-corpus-trM) and applied the continual training approach, initializing we... | {"language": ["tr"]} | hazal/BioBERTurkcased-con-trM-trR | null | [
"transformers",
"pytorch",
"bert",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T09:05:11+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #tr #endpoints_compatible #region-us
|
# BioBERTurk- Turkish Biomedical Language Models
BioBERTurk on Huggingface repo:
BioBERTurkcased-(con)+(trM): BioBERTurkcased-(con)+(trM), was pretrained only on Turkish biomedical text and applied the continual training approach, initializing weights from available general Turkish BERTurk | [
"# BioBERTurk- Turkish Biomedical Language Models\n\nBioBERTurk on Huggingface repo:\n\nBioBERTurkcased-(con)+(trM): BioBERTurkcased-(con)+(trM), was pretrained only on Turkish biomedical text and applied the continual training approach, initializing weights from available general Turkish BERTurk"
] | [
"TAGS\n#transformers #pytorch #bert #tr #endpoints_compatible #region-us \n",
"# BioBERTurk- Turkish Biomedical Language Models\n\nBioBERTurk on Huggingface repo:\n\nBioBERTurkcased-(con)+(trM): BioBERTurkcased-(con)+(trM), was pretrained only on Turkish biomedical text and applied the continual training approach... |
token-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-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | RobertoMCA97/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T11:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1380
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# BioBert-PubMed200kRCT
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/b... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "SAMPLE 32,441 archived appendix samples fixed in formalin and embedded in paraffin and tested for the presence of abnormal prion protein (PrP)."}], "base_model": "dmis-lab/biobert-base-cased-v1.1", "model-index": [{"name": "BioBert-PubMe... | pritamdeka/BioBert-PubMed200kRCT | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-base-cased-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T12:38:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us
| BioBert-PubMed200kRCT
=====================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on the PubMed200kRCT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2832
* Accuracy: 0.8934
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 2.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
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"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | dennishauser/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T13:57:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2128
* Accuracy: 0.7597
* F1: 0.6574
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 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | DrishtiSharma/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T14:04:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0817
* Accuracy: 0.9844
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #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: 3e-05\n* train\\_batch\\_... |
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_supervised_SARC_3epochs_withcontext
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None d... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2_supervised_SARC_3epochs_withcontext", "results": []}]} | ScandinavianMrT/gpt2_supervised_SARC_3epochs_withcontext | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T14:16:33+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2\_supervised\_SARC\_3epochs\_withcontext
============================================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0949
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #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* train\\_batc... |
null | null | # StyleSwin
- Repo: https://github.com/microsoft/StyleSwin
- https://drive.google.com/file/d/1OjYZ1zEWGNdiv0RFKv7KhXRmYko72LjO/view?usp=sharing
- https://drive.google.com/file/d/1HF0wFNuz1WFrqGEbPhOXjL4QrY05Zu_m/view?usp=sharing
- https://drive.google.com/file/d/1YtIJOgLFfkaMI_KL2gBQNABFb1cwOzvM/view?usp=s... | {} | public-data/StyleSwin | null | [
"region:us",
"has_space"
] | null | 2022-03-15T14:29:57+00:00 | [] | [] | TAGS
#region-us #has_space
| # StyleSwin
- Repo: URL
- URL
- URL
- URL
- URL
- URL
| [
"# StyleSwin\n\n- Repo: URL\n - URL\n - URL\n - URL\n - URL\n - URL"
] | [
"TAGS\n#region-us #has_space \n",
"# StyleSwin\n\n- Repo: URL\n - URL\n - URL\n - URL\n - URL\n - 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. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | abinternet143/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T14:32:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-xsum
This model is a fine-tuned version of t5-small on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
... |
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. -->
# wangchanberta-th-QA
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co... | {"language": ["th"], "datasets": ["thaiqa_squad"]} | Thanakrit/wangchanberta-th-QA | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"th",
"dataset:thaiqa_squad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T14:34:26+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #camembert #question-answering #th #dataset-thaiqa_squad #endpoints_compatible #region-us
|
# wangchanberta-th-QA
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the thaiqa_squad dataset.
language:
- th
Code for fine-tune Model github | [
"# wangchanberta-th-QA\n\nThis model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the thaiqa_squad dataset.\n\n\nlanguage:\n- th\n\nCode for fine-tune Model github"
] | [
"TAGS\n#transformers #pytorch #camembert #question-answering #th #dataset-thaiqa_squad #endpoints_compatible #region-us \n",
"# wangchanberta-th-QA\n\nThis model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the thaiqa_squad dataset.\n\n\nlanguage:\n- th\n\nCode for fine-tune Model g... |
fill-mask | transformers | # MiniLM v2
Microsoft's MiniLM v2 L6 H384 distilled from RoBERTa-Large \
Found [here](https://github.com/microsoft/unilm/tree/master/minilm) | {} | torbenal/MiniLMv2-L6-H384-RoBERTa-Large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T15:01:00+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MiniLM v2
Microsoft's MiniLM v2 L6 H384 distilled from RoBERTa-Large \
Found here | [
"# MiniLM v2\nMicrosoft's MiniLM v2 L6 H384 distilled from RoBERTa-Large \\\nFound here"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniLM v2\nMicrosoft's MiniLM v2 L6 H384 distilled from RoBERTa-Large \\\nFound here"
] |
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-large-xls-r-300m-slowenian-with-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-slowenian-with-lm", "results": []}]} | mfleck/wav2vec2-large-xls-r-300m-slowenian-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T15:01:45+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-slowenian-with-lm
===========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3935
* Wer: 0.3480
Model description
-----------------
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* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"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... |
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. -->
# poem-gen-t5-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-t5-small", "results": []}]} | DrishtiSharma/poem-gen-t5-small | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T15:08:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| poem-gen-t5-small
=================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1066
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #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... |
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["tanslation", "generated_from_trainer"], "datasets": ["kde4"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": []}]} | spasis/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"tanslation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T15:14:38+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #tanslation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #tanslation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.",... |
token-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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Neulvo/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T15:26:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0793
* Precision: 0.9358
* Recall: 0.9510
* F1: 0.9433
* Accuracy: 0.9862
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# negfir/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [negfir/uncased_L-12_H-128_A-2](https://huggingfac... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "negfir/distilbert-base-uncased-finetuned-cola", "results": []}]} | negfir/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T15:29:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| negfir/distilbert-base-uncased-finetuned-cola
=============================================
This model is a fine-tuned version of negfir/uncased\_L-12\_H-128\_A-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6077
* Validation Loss: 0.6185
* Train Matthews Correlati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'c... |
fill-mask | 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-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | lijingxin/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T15:32:40+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2540
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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* num\\_epochs: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmpny35efxx
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmpny35efxx", "results": []}]} | smartiros/BERT_for_sentiment_5k_2pcs_sampled_airlines_tweets | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T16:26:59+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| tmpny35efxx
===========
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1996
* Train Accuracy: 0.9348
* Validation Loss: 0.8523
* Validation Accuracy: 0.7633
* Epoch: 1
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
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. -->
# Homophobia-Transphobia-v2-mBERT-EDA
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "Homophobia-Transphobia-v2-mBERT-EDA", "results": []}]} | bitsanlp/Homophobia-Transphobia-v2-mBERT-EDA | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T16:43:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Homophobia-Transphobia-v2-mBERT-EDA
===================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5401
* Accuracy: 0.9317
* F1: 0.4498
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: 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
text-generation | transformers |
## HingGPT
HingGPT is a Hindi-English code-mixed GPT model trained on roman text. It is a GPT2 model trained on L3Cube-HingCorpus.
<br>
[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398... | {"language": ["hi", "en", "multilingual"], "license": "cc-by-4.0", "tags": ["hi", "en", "codemix"], "datasets": ["L3Cube-HingCorpus"]} | l3cube-pune/hing-gpt | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"hi",
"en",
"codemix",
"multilingual",
"dataset:L3Cube-HingCorpus",
"arxiv:2204.08398",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T17:32:29+00:00 | [
"2204.08398"
] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## HingGPT
HingGPT is a Hindi-English code-mixed GPT model trained on roman text. It is a GPT2 model trained on L3Cube-HingCorpus.
<br>
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
Other models from HingBERT family: <br>
<a href="URL HingBERT </a> <br... | [
"## HingGPT\nHingGPT is a Hindi-English code-mixed GPT model trained on roman text. It is a GPT2 model trained on L3Cube-HingCorpus.\n<br>\n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\nOther models from HingBERT family: <br>\n<a href=\"URL Hing... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## HingGPT\nHingGPT is a Hindi-English code-mixed GPT ... |
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. -->
# Clasificacion_sentimientos
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/rob... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Clasificacion_sentimientos", "results": []}]} | alexhf90/Clasificacion_sentimientos | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:31:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Clasificacion\_sentimientos
===========================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3399
* Accuracy: 0.9428
Model description
-----------------
Se entrena un modelo que es capaz de clasi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-002 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:34:23+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-004 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:34:54+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-006 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:35:27+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-008 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:36:02+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-016 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:36:39+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-032 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:37:18+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-040 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:38:02+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
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-all
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-case... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "bert-all", "results": []}]} | krinal214/bert-all | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:38:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-tydiqa #license-apache-2.0 #endpoints_compatible #region-us
| bert-all
========
This model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5985
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-tydiqa #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: 2e-05\n* train\\_batch\\_size: ... |
image-classification | transformers |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/regnet-x-064 | null | [
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:38:56+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #tf #regnet #image-classification #vision #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first ... |
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... | RaghuramKol/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 | 2022-03-15T19:47:42+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: 0.2218
* Accuracy: 0.927
* F1: 0.9272
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... |
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-rnd-no-adapter | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:50:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8384
* Wer: 0.1367
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-rnd-2-layer-no-adapter | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T19:50:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8365
* Wer: 0.2812
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1318831968352612352/blMp... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/theshiftnews/1647377809961/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/theshiftnews | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T20:56:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
The Shift News
@theshiftnews
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1442160889596026883/gq6j... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/maltatoday-netnewsmalta-one_news_malta/1647379141053/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/maltatoday-netnewsmalta-one_news_malta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T21:18:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
ONE news & NETnews & MaltaToday
@maltatoday-netnewsmalta-one\_news\_malta
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1333858206012084227/XP6E... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/independentmlt-maltatoday-thetimesofmalta/1647381547913/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/independentmlt-maltatoday-thetimesofmalta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T21:42:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
MaltaToday & Times of Malta & The Malta Independent
@independentmlt-maltatoday-thetimesofmalta
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how th... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# predict-perception-xlmr-blame-assassin
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-rober... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-blame-assassin", "results": []}]} | responsibility-framing/predict-perception-xlmr-blame-assassin | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:28:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-blame-assassin
======================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4439
* Rmse: 0.9571
* Rmse Blame::a L'assassino: 0.9571
* Mae: 0.7260
* Mae Blame::a L'assass... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
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. -->
# predict-perception-xlmr-blame-victim
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-blame-victim", "results": []}]} | responsibility-framing/predict-perception-xlmr-blame-victim | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:33:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-blame-victim
====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1098
* Rmse: 0.6801
* Rmse Blame::a La vittima: 0.6801
* Mae: 0.5617
* Mae Blame::a La vittima: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
text-classification | transformers | # MARS Encoder for Multi-Agent Response Selection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class and is the model used in the paper [One Agent To Rule Them All: Towards Multi-agent Conversational AI](htt... | {"license": "cc"} | claritylab/MARS-Encoder | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:36:03+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #license-cc #autotrain_compatible #endpoints_compatible #region-us
| # MARS Encoder for Multi-Agent Response Selection
This model was trained using SentenceTransformers Cross-Encoder class and is the model used in the paper One Agent To Rule Them All: Towards Multi-agent Conversational AI.
## Training Data
This model was trained on the BBAI dataset. The model will predict a score betwe... | [
"# MARS Encoder for Multi-Agent Response Selection\nThis model was trained using SentenceTransformers Cross-Encoder class and is the model used in the paper One Agent To Rule Them All: Towards Multi-agent Conversational AI.",
"## Training Data\nThis model was trained on the BBAI dataset. The model will predict a ... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"# MARS Encoder for Multi-Agent Response Selection\nThis model was trained using SentenceTransformers Cross-Encoder class and is the model used in the paper One Agent To... |
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. -->
# predict-perception-xlmr-blame-object
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-blame-object", "results": []}]} | responsibility-framing/predict-perception-xlmr-blame-object | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:38:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-blame-object
====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7219
* Rmse: 0.6215
* Rmse Blame::a Un oggetto: 0.6215
* Mae: 0.4130
* Mae Blame::a Un oggetto: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
token-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. -->
# biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_EN
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_EN", "results": []}]} | StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_EN | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:41:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2-finetuned-ner-CRAFT\_Augmented\_EN
==========================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the CRAFT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2299
* Precision: 0.8122
* Recall: 0.8... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
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. -->
# predict-perception-xlmr-blame-concept
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-blame-concept", "results": []}]} | responsibility-framing/predict-perception-xlmr-blame-concept | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:43:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-blame-concept
=====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9414
* Rmse: 0.7875
* Rmse Blame::a Un concetto astratto o un'emozione: 0.7875
* Mae: 0.6165
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
token-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. -->
# biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_ES
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_ES", "results": []}]} | StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-CRAFT_Augmented_ES | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:44:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2-finetuned-ner-CRAFT\_Augmented\_ES
==========================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the CRAFT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2251
* Precision: 0.8276
* Recall: 0.8... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
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. -->
# predict-perception-xlmr-blame-none
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-blame-none", "results": []}]} | responsibility-framing/predict-perception-xlmr-blame-none | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:48:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-blame-none
==================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8941
* Rmse: 1.1259
* Rmse Blame::a Nessuno: 1.1259
* Mae: 0.8559
* Mae Blame::a Nessuno: 0.8559
* R2... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
token-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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | kSaluja/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:50:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1555
* Precision: 0.9681
* Recall: 0.9670
* F1: 0.9675
* Accuracy: 0.9687
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
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. -->
# predict-perception-xlmr-cause-human
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-cause-human", "results": []}]} | responsibility-framing/predict-perception-xlmr-cause-human | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:53:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-cause-human
===================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7632
* Rmse: 1.2675
* Rmse Cause::a Causata da un essere umano: 1.2675
* Mae: 0.9299
* Mae Cause::a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
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. -->
# predict-perception-xlmr-cause-object
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-cause-object", "results": []}]} | responsibility-framing/predict-perception-xlmr-cause-object | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T22:58:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-cause-object
====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3069
* Rmse: 0.8927
* Rmse Cause::a Causata da un oggetto (es. una pistola): 0.8927
* Mae: 0.5854... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
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. -->
# predict-perception-xlmr-focus-assassin
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-rober... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-focus-assassin", "results": []}]} | responsibility-framing/predict-perception-xlmr-focus-assassin | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:08:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-focus-assassin
======================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3264
* Rmse: 0.9437
* Rmse Focus::a Sull'assassino: 0.9437
* Mae: 0.7093
* Mae Focus::a Sull'... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
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. -->
# predict-perception-xlmr-focus-victim
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-focus-victim", "results": []}]} | responsibility-framing/predict-perception-xlmr-focus-victim | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:13:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-focus-victim
====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2546
* Rmse: 0.6301
* Rmse Focus::a Sulla vittima: 0.6301
* Mae: 0.5441
* Mae Focus::a Sulla vitt... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
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-3lang
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-ca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "bert-3lang", "results": []}]} | krinal214/bert-3lang | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:17:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-tydiqa #license-apache-2.0 #endpoints_compatible #region-us
| bert-3lang
==========
This model is a fine-tuned version of bert-base-multilingual-cased on the tydiqa dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6422
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inf... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-tydiqa #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: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# predict-perception-xlmr-focus-object
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-focus-object", "results": []}]} | responsibility-framing/predict-perception-xlmr-focus-object | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:19:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-focus-object
====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1927
* Rmse: 0.5495
* Rmse Focus::a Su un oggetto: 0.5495
* Mae: 0.4174
* Mae Focus::a Su un ogge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
token-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. -->
# roberta-finetuned-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an un... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-finetuned-ner", "results": []}]} | kSaluja/roberta-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:20:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-finetuned-ner
=====================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1322
* Precision: 0.9772
* Recall: 0.9782
* F1: 0.9777
* Accuracy: 0.9767
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
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. -->
# predict-perception-xlmr-focus-concept
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-focus-concept", "results": []}]} | responsibility-framing/predict-perception-xlmr-focus-concept | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:23:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-focus-concept
=====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8296
* Rmse: 1.0302
* Rmse Focus::a Su un concetto astratto o un'emozione: 1.0302
* Mae: 0.7515... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
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. -->
# predict-perception-xlmr-cause-concept
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-cause-concept", "results": []}]} | responsibility-framing/predict-perception-xlmr-cause-concept | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:31:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-cause-concept
=====================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3933
* Rmse: 0.5992
* Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.5992
* Mae... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_review... | golivaresm/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:34:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2328
* Accuracy: 0.9313
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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #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\... |
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. -->
# predict-perception-xlmr-cause-none
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-xlmr-cause-none", "results": []}]} | responsibility-framing/predict-perception-xlmr-cause-none | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:39:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-xlmr-cause-none
==================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8639
* Rmse: 1.3661
* Rmse Cause::a Spontanea, priva di un agente scatenante: 1.3661
* Mae: 1.0795
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AnnaR/literature_summarizer
This model is a fine-tuned version of [sshleifer/distilbart-xsum-1-1](https://huggingface.co/sshleifer/dis... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AnnaR/literature_summarizer", "results": []}]} | AnnaR/literature_summarizer | null | [
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T23:47:38+00:00 | [] | [] | TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AnnaR/literature\_summarizer
============================
This model is a fine-tuned version of sshleifer/distilbart-xsum-1-1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.2180
* Validation Loss: 4.7198
* Epoch: 10
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 5300, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #bart #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
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_common_voice_accents_3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_common_voice_accents_3", "results": []}]} | willcai/wav2vec2_common_voice_accents_3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T00:25:23+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_common\_voice\_accents\_3
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0042
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 32\n*... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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: 0.0003\n* train\\_batch\... |
token-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. -->
# roberta-finetuned-ner-without-data-sort
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robe... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-finetuned-ner-without-data-sort", "results": []}]} | kSaluja/roberta-finetuned-ner-without-data-sort | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T00:41:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-finetuned-ner-without-data-sort
=======================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0420
* Precision: 0.9914
* Recall: 0.9909
* F1: 0.9912
* Accuracy: 0.9920
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-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-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | aytugkaya/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T01:55:14+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1474
* F1: 0.8651
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
fill-mask | 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. -->
# MiniLMv2-L6-H768-distilled-from-RoBERTa-Large-finetuned-wikitext103
This model is a fine-tuned version of [nreimers/MiniLMv2-L6-... | {"tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "MiniLMv2-L6-H768-distilled-from-RoBERTa-Large-finetuned-wikitext103", "results": []}]} | saghar/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:wikitext",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T03:59:15+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L6-H768-distilled-from-RoBERTa-Large-finetuned-wikitext103
===================================================================
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large on the wikitext dataset.
It achieves the following results on the evaluation set:
* Los... | [
"### 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: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #region-us \n",
"### 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\\_ba... |
text2text-generation | transformers |
# poem-gen-spanish-t5-small
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the [Spanish Poetry Dataset](https://www.kaggle.com/andreamorgar/spanish-poetry-dataset/version/1) dataset.
The model was created during the [First Spanish Ha... | {"language": "es", "license": "mit", "tags": ["generated_from_trainer"], "base_model": "flax-community/spanish-t5-small", "model-index": [{"name": "poem-gen-spanish-t5-small", "results": []}]} | hackathon-pln-es/poem-gen-spanish-t5-small | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"es",
"base_model:flax-community/spanish-t5-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T04:55:33+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #es #base_model-flax-community/spanish-t5-small #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| poem-gen-spanish-t5-small
=========================
This model is a fine-tuned version of flax-community/spanish-t5-small on the Spanish Poetry Dataset dataset.
The model was created during the First Spanish Hackathon organized by Somos NLP.
The team who participated was composed by:
* 🇨🇺 Alberto Carmona Bart... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTraining and evaluation data\n----------------------------\n\n\nThe original dataset has the columns 'author', 'content' and 'title'.\nFor each poem we generate new examples:\n\n\n* content: *line\\_i* , generate... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #es #base_model-flax-community/spanish-t5-small #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model direc... |
fill-mask | 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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | Neulvo/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T05:13:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4717
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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. -->
# roberta-base-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/d... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "roberta-base-squad2-finetuned-squad", "results": []}]} | deepakvk/roberta-base-squad2-finetuned-squad | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T06:09:49+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# roberta-base-squad2-finetuned-squad
This model is a fine-tuned version of deepset/roberta-base-squad2 on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# roberta-base-squad2-finetuned-squad\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on the squad_v2 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# roberta-base-squad2-finetuned-squad\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on the squad_v2 dataset.",
"## Model descripti... |
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_prefinetune_SARC_1epoch_withcontext
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None d... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2_prefinetune_SARC_1epoch_withcontext", "results": []}]} | ScandinavianMrT/gpt2_prefinetune_SARC_1epoch_withcontext | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T06:24:23+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2\_prefinetune\_SARC\_1epoch\_withcontext
============================================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7899
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #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* train\\_batc... |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": []}]} | libalabala/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T07:09:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the k... |
text-generation | transformers |
## MahaGPT
MahaGPT is a Marathi GPT2 model. It is a GPT2 model pre-trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.or... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaCorpus"]} | l3cube-pune/marathi-gpt | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"mr",
"dataset:L3Cube-MahaCorpus",
"arxiv:2202.01159",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T07:54:31+00:00 | [
"2202.01159"
] | [
"mr"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## MahaGPT
MahaGPT is a Marathi GPT2 model. It is a GPT2 model pre-trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets.
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
| [
"## MahaGPT\nMahaGPT is a Marathi GPT2 model. It is a GPT2 model pre-trained on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. \n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #mr #dataset-L3Cube-MahaCorpus #arxiv-2202.01159 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## MahaGPT\nMahaGPT is a Marathi GPT2 model. It is a GPT2 model pre-trained on L3Cube-MahaCo... |
text-classification | transformers |
# twitter_sexismo-finetuned-exist2021
This model is a fine-tuned version of [pysentimiento/robertuito-hate-speech](https://huggingface.co/pysentimiento/robertuito-hate-speech) on the EXIST dataset and MeTwo: Machismo and Sexism Twitter Identification dataset https://github.com/franciscorodriguez92/MeTwo.
It achieves ... | {"license": "apache-2.0", "tags": [], "datasets": ["EXIST Dataset", "MeTwo Machismo and Sexism Twitter Identification dataset"], "metrics": ["accuracy"], "widget": [{"text": "manejas muy bien para ser mujer"}, {"text": "En temas pol\u00edticos hombres y mujeres son iguales"}, {"text": "Los ipad son unos equipos electr\... | hackathon-pln-es/twitter_sexismo-finetuned-exist2021-metwo | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T08:03:19+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| twitter\_sexismo-finetuned-exist2021
====================================
This model is a fine-tuned version of pysentimiento/robertuito-hate-speech on the EXIST dataset and MeTwo: Machismo and Sexism Twitter Identification dataset URL
It achieves the following results on the evaluation set:
* Loss: 0.54
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* my\\_learning\\_rate = 5E-5\n* my\\_adam\\_epsilon = 1E-8\n* my\\_number\\_of\\_epochs = 8\n* my\\_warmup = 3\n* my\\_mini\\_batch\\_size = 32\n* optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_sched... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* my\\_learning\\_rate = 5E-5\n* my\\_adam\\_epsilon = 1E-8\n* my\... |
reinforcement-learning | transformers |
# Decision Transformer model trained on expert trajectories sampled from the Gym HalfCheetah environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on expert trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are required to ... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-halfcheetah-expert | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T08:19:45+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #has_space #region-us
|
# Decision Transformer model trained on expert trajectories sampled from the Gym HalfCheetah environment
This is a trained Decision Transformer model trained on expert trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are required to use this model:
mean = [ -0.0448914... | [
"# Decision Transformer model trained on expert trajectories sampled from the Gym HalfCheetah environment\nThis is a trained Decision Transformer model trained on expert trajectories sampled from the Gym HalfCheetah environment.\n\nThe following normlization coeficients are required to use this model:\n\nmean = [ -... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #has_space #region-us \n",
"# Decision Transformer model trained on expert trajectories sampled from the... |
reinforcement-learning | transformers | # Decision Transformer model trained on medium trajectories sampled from the Gym HalfCheetah environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are required to u... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-halfcheetah-medium | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T08:19:56+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us
| # Decision Transformer model trained on medium trajectories sampled from the Gym HalfCheetah environment
This is a trained Decision Transformer model trained on medium trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are required to use this model:
mean = [-0.06845774,... | [
"# Decision Transformer model trained on medium trajectories sampled from the Gym HalfCheetah environment\nThis is a trained Decision Transformer model trained on medium trajectories sampled from the Gym HalfCheetah environment.\n\nThe following normlization coeficients are required to use this model:\n\nmean = [-0... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us \n",
"# Decision Transformer model trained on medium trajectories sampled from the Gym HalfCh... |
reinforcement-learning | transformers | # Decision Transformer model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-halfcheetah-medium-replay | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T08:20:08+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us
| # Decision Transformer model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment
This is a trained Decision Transformer model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are required to use this model:
mean =... | [
"# Decision Transformer model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment\nThis is a trained Decision Transformer model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment.\n\nThe following normlization coeficients are required to use this model:... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us \n",
"# Decision Transformer model trained on medium-replay trajectories sampled from the Gym... |
reinforcement-learning | transformers | # Decision Transformer model trained on expert trajectories sampled from the Gym Hopper environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on expert trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required to use this m... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-hopper-expert | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T08:20:20+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #has_space #region-us
| # Decision Transformer model trained on expert trajectories sampled from the Gym Hopper environment
This is a trained Decision Transformer model trained on expert trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required to use this model:
mean = [ 1.3490015, -0.11208... | [
"# Decision Transformer model trained on expert trajectories sampled from the Gym Hopper environment\nThis is a trained Decision Transformer model trained on expert trajectories sampled from the Gym Hopper environment.\n\nThe following normlization coefficients are required to use this model:\n\nmean = [ 1.3490015,... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #has_space #region-us \n",
"# Decision Transformer model trained on expert trajectories sampled from the... |
reinforcement-learning | transformers | # Decision Transformer model trained on medium trajectories sampled from the Gym Hopper environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required to use this m... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-hopper-medium | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T08:20:31+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #has_space #region-us
| # Decision Transformer model trained on medium trajectories sampled from the Gym Hopper environment
This is a trained Decision Transformer model trained on medium trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required to use this model:
mean = [ 1.311279, -0.0846952... | [
"# Decision Transformer model trained on medium trajectories sampled from the Gym Hopper environment\nThis is a trained Decision Transformer model trained on medium trajectories sampled from the Gym Hopper environment.\n\nThe following normlization coefficients are required to use this model:\n\nmean = [ 1.311279, ... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #has_space #region-us \n",
"# Decision Transformer model trained on medium trajectories sampled from the... |
reinforcement-learning | transformers | # Decision Transformer model trained on medium-replay trajectories sampled from the Gym Hopper environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium-replay trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-hopper-medium-replay | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T08:20:43+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us
| # Decision Transformer model trained on medium-replay trajectories sampled from the Gym Hopper environment
This is a trained Decision Transformer model trained on medium-replay trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required to use this model:
mean = [ 1.2305... | [
"# Decision Transformer model trained on medium-replay trajectories sampled from the Gym Hopper environment\nThis is a trained Decision Transformer model trained on medium-replay trajectories sampled from the Gym Hopper environment.\n\nThe following normlization coefficients are required to use this model:\n\nmean ... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us \n",
"# Decision Transformer model trained on medium-replay trajectories sampled from the Gym... |
reinforcement-learning | transformers | # Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on expert trajectories sampled from the Gym Walker2d environment.
The following normlization coeficients are required to use thi... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-walker2d-expert | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T08:20:54+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us
| # Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment
This is a trained Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment.
The following normlization coeficients are required to use this model:
mean = [ 1.2384834e+00, 1... | [
"# Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment\nThis is a trained Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment.\n\nThe following normlization coeficients are required to use this model:\n\nmean = [ 1.23848... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us \n",
"# Decision Transformer model trained on expert trajectories sampled from the Gym Walker... |
reinforcement-learning | transformers | # Decision Transformer model trained on medium trajectories sampled from the Gym Walker2d environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium trajectories sampled from the Gym Walker2d environment.
The following normlization coeficients are required to use thi... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-walker2d-medium | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T08:21:05+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us
| # Decision Transformer model trained on medium trajectories sampled from the Gym Walker2d environment
This is a trained Decision Transformer model trained on medium trajectories sampled from the Gym Walker2d environment.
The following normlization coeficients are required to use this model:
mean = [ 1.218966, 0.14163... | [
"# Decision Transformer model trained on medium trajectories sampled from the Gym Walker2d environment\nThis is a trained Decision Transformer model trained on medium trajectories sampled from the Gym Walker2d environment.\n\nThe following normlization coeficients are required to use this model:\n\nmean = [ 1.21896... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us \n",
"# Decision Transformer model trained on medium trajectories sampled from the Gym Walker... |
reinforcement-learning | transformers | # Decision Transformer model trained on medium-replay trajectories sampled from the Gym Walker2d environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium-replay trajectories sampled from the Gym Walker2d environment.
The following normlization coeficients are requi... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "decision-transformer", "gym-continous-control"], "pipeline_tag": "reinforcement-learning"} | edbeeching/decision-transformer-gym-walker2d-medium-replay | null | [
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T08:21:17+00:00 | [
"2106.01345"
] | [] | TAGS
#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us
| # Decision Transformer model trained on medium-replay trajectories sampled from the Gym Walker2d environment
This is a trained Decision Transformer model trained on medium-replay trajectories sampled from the Gym Walker2d environment.
The following normlization coeficients are required to use this model:
mean = [1.20... | [
"# Decision Transformer model trained on medium-replay trajectories sampled from the Gym Walker2d environment\nThis is a trained Decision Transformer model trained on medium-replay trajectories sampled from the Gym Walker2d environment.\n\nThe following normlization coeficients are required to use this model:\n\nme... | [
"TAGS\n#transformers #pytorch #decision_transformer #feature-extraction #deep-reinforcement-learning #reinforcement-learning #decision-transformer #gym-continous-control #arxiv-2106.01345 #endpoints_compatible #region-us \n",
"# Decision Transformer model trained on medium-replay trajectories sampled from the Gym... |
text-classification | sentence-transformers |
# Cross-Encoder for MS Marco
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/REA... | {"language": "en", "license": "mit", "tags": ["sentence-transformers"], "pipeline_tag": "text-classification"} | navteca/ms-marco-MiniLM-L-6-v2 | null | [
"sentence-transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"license:mit",
"region:us"
] | null | 2022-03-16T09:26:53+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #jax #bert #text-classification #en #license-mit #region-us
| Cross-Encoder for MS Marco
==========================
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is availab... | [] | [
"TAGS\n#sentence-transformers #pytorch #jax #bert #text-classification #en #license-mit #region-us \n"
] |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall)
... | {"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-xsmall"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | navteca/nli-deberta-v3-xsmall | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-xsmall",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T09:37:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-xsmall #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to th... | [
"# Cross-Encoder for Natural Language Inference\n\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores correspo... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-xsmall #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for Natural Language Inference\n\nThis model was trained using... |
fill-mask | transformers |
# Roberta-eus cc100 base cased
This is a RoBERTa model for Basque model presented in [Does corpus quality really matter for low-resource languages?](https://arxiv.org/abs/2203.08111). There are several models for Basque using the RoBERTa architecture, using different corpora:
- roberta-eus-euscrawl-base-cased: Basqu... | {"language": "eu", "license": "cc-by-nc-4.0", "tags": ["basque", "roberta"]} | ixa-ehu/roberta-eus-cc100-base-cased | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"basque",
"eu",
"arxiv:2203.08111",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T09:47:37+00:00 | [
"2203.08111"
] | [
"eu"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Roberta-eus cc100 base cased
============================
This is a RoBERTa model for Basque model presented in Does corpus quality really matter for low-resource languages?. There are several models for Basque using the RoBERTa architecture, using different corpora:
* roberta-eus-euscrawl-base-cased: Basque RoBERT... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Roberta-eus Euscrawl base cased
This is a RoBERTa model for Basque model presented in [Does corpus quality really matter for low-resource languages?](https://arxiv.org/abs/2203.08111). There are several models for Basque using the RoBERTa architecture, which are pre-trained using different corpora:
- roberta-eus-e... | {"language": "eu", "license": "cc-by-nc-4.0", "tags": ["basque", "roberta"]} | ixa-ehu/roberta-eus-euscrawl-base-cased | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"basque",
"eu",
"arxiv:2203.08111",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T09:54:43+00:00 | [
"2203.08111"
] | [
"eu"
] | TAGS
#transformers #pytorch #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Roberta-eus Euscrawl base cased
===============================
This is a RoBERTa model for Basque model presented in Does corpus quality really matter for low-resource languages?. There are several models for Basque using the RoBERTa architecture, which are pre-trained using different corpora:
* roberta-eus-euscra... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Roberta-eus Euscrawl large cased
This is a RoBERTa model for Basque model presented in [Does corpus quality really matter for low-resource languages?](https://arxiv.org/abs/2203.08111). There are several models for Basque using the RoBERTa architecture, using different corpora:
- roberta-eus-euscrawl-base-cased: B... | {"language": "eu", "license": "cc-by-nc-4.0", "tags": ["basque", "roberta"]} | ixa-ehu/roberta-eus-euscrawl-large-cased | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"basque",
"eu",
"arxiv:2203.08111",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T09:55:25+00:00 | [
"2203.08111"
] | [
"eu"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Roberta-eus Euscrawl large cased
================================
This is a RoBERTa model for Basque model presented in Does corpus quality really matter for low-resource languages?. There are several models for Basque using the RoBERTa architecture, using different corpora:
* roberta-eus-euscrawl-base-cased: Basqu... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# Roberta-eus mc4 base cased
This is a RoBERTa model for Basque model presented in [Does corpus quality really matter for low-resource languages?](https://arxiv.org/abs/2203.08111). There are several models for Basque using the RoBERTa architecture, using different corpora:
- roberta-eus-euscrawl-base-cased: Basque ... | {"language": "eu", "license": "cc-by-nc-4.0", "tags": ["basque", "roberta"]} | ixa-ehu/roberta-eus-mc4-base-cased | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"basque",
"eu",
"arxiv:2203.08111",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T09:56:03+00:00 | [
"2203.08111"
] | [
"eu"
] | TAGS
#transformers #pytorch #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Roberta-eus mc4 base cased
==========================
This is a RoBERTa model for Basque model presented in Does corpus quality really matter for low-resource languages?. There are several models for Basque using the RoBERTa architecture, using different corpora:
* roberta-eus-euscrawl-base-cased: Basque RoBERTa mo... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #basque #eu #arxiv-2203.08111 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | ThomasSimonini/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-03-16T10:07:11+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
automatic-speech-recognition | transformers | ASR for urdu language.
Dataset used is common voice and also some self collected data. | {} | sraza/wav2vec2-large-xls-r-300m-ur-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T10:22:41+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ASR for urdu language.
Dataset used is common voice and also some self collected data. | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | Neulvo/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T10:57:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8564
- Bleu: 52.8938
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8564\n- Bleu: 52.8938",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
null | null | hello fact
| {} | shaozk/hello | null | [
"region:us"
] | null | 2022-03-16T11:07:28+00:00 | [] | [] | TAGS
#region-us
| hello fact
| [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers |
# MultiIndicWikiBioUnified
MultiIndicWikiBioUnified is a multilingual, sequence-to-sequence pre-trained model, a [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint fine-tuned on the 9 languages of [IndicWikiBio](https://huggingface.co/datasets/ai4bharat/IndicWikiBio) dataset. For fine-tuning details,
... | {"language": ["as", "bn", "hi", "kn", "ml", "or", "pa", "ta", "te"], "tags": ["wikibio", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicWikiBio"], "licenses": ["cc-by-nc-4.0"], "widget": ["<TAG> name </TAG> \u0928\u0935\u0924\u0947\u091c \u092d\u093e\u0930\u0924\u0940 <TAG> image </TAG> NavtejBharati ... | ai4bharat/MultiIndicWikiBioUnified | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"wikibio",
"multilingual",
"nlp",
"indicnlp",
"as",
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"hi",
"kn",
"ml",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicWikiBio",
"arxiv:2203.05437",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
... | null | 2022-03-16T11:35:33+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"hi",
"kn",
"ml",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #wikibio #multilingual #nlp #indicnlp #as #bn #hi #kn #ml #or #pa #ta #te #dataset-ai4bharat/IndicWikiBio #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #region-us
| MultiIndicWikiBioUnified
========================
MultiIndicWikiBioUnified is a multilingual, sequence-to-sequence pre-trained model, a IndicBART checkpoint fine-tuned on the 9 languages of IndicWikiBio dataset. For fine-tuning details,
see the paper. You can use MultiIndicWikiBio to build biography generation applic... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #wikibio #multilingual #nlp #indicnlp #as #bn #hi #kn #ml #or #pa #ta #te #dataset-ai4bharat/IndicWikiBio #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# MultiIndicWikiBioSS
MultiIndicWikiBioSS is a multilingual, sequence-to-sequence pre-trained model, a [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint fine-tuned on the 9 languages of [IndicWikiBio](https://huggingface.co/datasets/ai4bharat/IndicWikiBio) dataset. For fine-tuning details,
see th... | {"language": ["as", "bn", "hi", "kn", "ml", "or", "pa", "ta", "te"], "tags": ["wikibio", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicWikiBio"], "licenses": ["cc-by-nc-4.0"], "widget": [{"text": "<TAG> name </TAG> \u0930\u093e\u092e \u0928\u0930\u0947\u0936 \u092a\u093e\u0902\u0921\u0947\u092f <TAG>... | ai4bharat/MultiIndicWikiBioSS | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"wikibio",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"hi",
"kn",
"ml",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicWikiBio",
"arxiv:2203.05437",
"autotrain_compatible",
"endpoints_compatible",
"has_space",... | null | 2022-03-16T11:36:23+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"hi",
"kn",
"ml",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #wikibio #multilingual #nlp #indicnlp #as #bn #hi #kn #ml #or #pa #ta #te #dataset-ai4bharat/IndicWikiBio #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #has_space #region-us
| MultiIndicWikiBioSS
===================
MultiIndicWikiBioSS is a multilingual, sequence-to-sequence pre-trained model, a IndicBARTSS checkpoint fine-tuned on the 9 languages of IndicWikiBio dataset. For fine-tuning details,
see the paper. You can use MultiIndicWikiBioSS to build biography generation applications for ... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #wikibio #multilingual #nlp #indicnlp #as #bn #hi #kn #ml #or #pa #ta #te #dataset-ai4bharat/IndicWikiBio #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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. -->
# zero_last
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "zero_last", "results": []}]} | krinal214/zero_shot | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-16T11:37:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us
| zero\_last
==========
This model is a fine-tuned version of bert-base-multilingual-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9190
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More info... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
text2text-generation | transformers |
# MultiIndicQuestionGenerationUnified
MultiIndicQuestionGenerationUnified is a multilingual, sequence-to-sequence pre-trained model, a [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint fine-tuned on the 11 languages of [IndicQuestionGeneration](https://huggingface.co/datasets/ai4bharat/IndicQuestionG... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "tags": ["question-generation", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicQuestionGeneration", "squad"], "licenses": ["cc-by-nc-4.0"]} | ai4bharat/MultiIndicQuestionGenerationUnified | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question-generation",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicQuestionGeneration",
"dataset:squad",
"arxiv:2203.05437",
"autot... | null | 2022-03-16T11:37:13+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #question-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicQuestionGeneration #dataset-squad #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #region-us
| MultiIndicQuestionGenerationUnified
===================================
MultiIndicQuestionGenerationUnified is a multilingual, sequence-to-sequence pre-trained model, a IndicBART checkpoint fine-tuned on the 11 languages of IndicQuestionGeneration dataset. For fine-tuning details,
see the paper. You can use MultiIndi... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #question-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicQuestionGeneration #dataset-squad #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# MultiIndicQuestionGenerationSS
MultiIndicQuestionGenerationSS is a multilingual, sequence-to-sequence pre-trained model, a [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint fine-tuned on the 11 languages of [IndicQuestionGeneration](https://huggingface.co/datasets/ai4bharat/IndicQuestionGenerat... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "tags": ["question-generation", "multilingual", "nlp", "indicnlp"], "datasets": ["ai4bharat/IndicQuestionGeneration", "squad"], "licenses": ["cc-by-nc-4.0"]} | ai4bharat/MultiIndicQuestionGenerationSS | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question-generation",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicQuestionGeneration",
"dataset:squad",
"arxiv:2203.05437",
"autot... | null | 2022-03-16T11:37:46+00:00 | [
"2203.05437"
] | [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #question-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicQuestionGeneration #dataset-squad #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #has_space #region-us
| MultiIndicQuestionGenerationSS
==============================
MultiIndicQuestionGenerationSS is a multilingual, sequence-to-sequence pre-trained model, a IndicBARTSS checkpoint fine-tuned on the 11 languages of IndicQuestionGeneration dataset. For fine-tuning details,
see the paper. You can use MultiIndicQuestionGene... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #question-generation #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-ai4bharat/IndicQuestionGeneration #dataset-squad #arxiv-2203.05437 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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. -->
# poem-gen-t5-small_v1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-t5-small_v1", "results": []}]} | DrishtiSharma/poem-gen-t5-small_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T11:37:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| poem-gen-t5-small\_v1
=====================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7290
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #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... |
audio-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. -->
# xtreme_s_xlsr_minds14_upd
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_xlsr_minds14_upd", "results": []}]} | anton-l/xtreme_s_xlsr_minds14_upd | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T11:48:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
|
# xtreme_s_xlsr_minds14_upd
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - MINDS14.FR-FR dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6303
- F1: 0.0223
- Accuracy: 0.0833
## Model description
More information needed
## Intended uses & li... | [
"# xtreme_s_xlsr_minds14_upd\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - MINDS14.FR-FR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.6303\n- F1: 0.0223\n- Accuracy: 0.0833",
"## Model description\n\nMore information needed",
"## ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"# xtreme_s_xlsr_minds14_upd\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/... |
token-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. -->
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-funsd-test", "results": []}]} | mazenalasali/layoutlmv2-finetuned-funsd-test | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T11:54:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased 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",
... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown data... |
token-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-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | RobertoMCA97/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:03:40+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1667
* F1: 0.8582
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\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-xl-ft-with-non-challenging-0.8
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknow... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-with-non-challenging-0.8", "results": []}]} | newtonkwan/gpt2-xl-ft-with-non-challenging-0.8 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-16T12:05:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-ft-with-non-challenging-0.8
===================================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.3121
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 2022\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-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: 0.0005\n* train\\_bat... |
question-answering | transformers | A MacBERTh model fine-tuned on SQuAD_v2. Hopefully, this will allow the model to perform well on QA tasks on historical texts.
Finetune parameters:
```
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=3e-5,
per_device_train_ba... | {"license": "afl-3.0"} | Nadav/MacSQuAD | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:14:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #license-afl-3.0 #endpoints_compatible #region-us
| A MacBERTh model fine-tuned on SQuAD_v2. Hopefully, this will allow the model to perform well on QA tasks on historical texts.
Finetune parameters:
Evaluation metrics on the validation set of SQuAD_v2:
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #license-afl-3.0 #endpoints_compatible #region-us \n"
] |
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. -->
# xlm-all-final
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tydiqa da... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "xlm-all-final", "results": []}]} | krinal214/xlm-all | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:19:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us
| xlm-all-final
=============
This model is a fine-tuned version of xlm-roberta-base on the tydiqa dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6038
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: 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
token-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-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | RobertoMCA97/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:25:09+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2651
* F1: 0.8355
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
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. -->
# xlm-eng-beng-tel
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tydiqa... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "xlm-eng-beng-tel", "results": []}]} | krinal214/xlm-3lang | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:tydiqa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-16T12:40:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us
| xlm-eng-beng-tel
================
This model is a fine-tuned version of xlm-roberta-base on the tydiqa dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7303
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inf... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-tydiqa #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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