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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. -->
# bart-large-cnn-weaksup-10k-NOpad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/fa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-weaksup-10k-NOpad-early", "results": []}]} | cammy/bart-large-cnn-weaksup-10k-NOpad-early | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T05:40:28+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-weaksup-10k-NOpad-early
======================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7883
* Rouge1: 26.9755
* Rouge2: 12.4975
* Rougel: 21.0743
* Rougelsum: 23.9303
* Gen ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
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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. -->
# bart-large-cnn-100-lit-evalMA-NOpad
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/faceb... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-100-lit-evalMA-NOpad", "results": []}]} | cammy/bart-large-cnn-100-lit-evalMA-NOpad | null | [
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"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T06:26:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-100-lit-evalMA-NOpad
===================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1514
* Rouge1: 27.5985
* Rouge2: 11.3869
* Rougel: 20.9359
* Rougelsum: 24.7113
* Gen Len: 6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
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token-classification | transformers |
# bert-base-russian-upos
## Model Description
This is a BERT model pre-trained with [UD_Russian](https://universaldependencies.org/ru/) for POS-tagging and dependency-parsing, derived from [rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased). Every word is tagged by [UPOS](https://universaldepend... | {"language": ["ru"], "license": "cc-by-sa-4.0", "tags": ["russian", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/bert-base-russian-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"russian",
"pos",
"dependency-parsing",
"ru",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T07:07:10+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bert #token-classification #russian #pos #dependency-parsing #ru #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-russian-upos
## Model Description
This is a BERT model pre-trained with UD_Russian for POS-tagging and dependency-parsing, derived from rubert-base-cased. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-parser with ... | [
"# bert-base-russian-upos",
"## Model Description\n\nThis is a BERT model pre-trained with UD_Russian for POS-tagging and dependency-parsing, derived from rubert-base-cased. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-tagger and D... | [
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"# bert-base-russian-upos",
"## Model Description\n\nThis is a BERT model pre-trained with UD_Russian ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 633018323
- CO2 Emissions (in grams): 3.622203603306694
## Validation Metrics
- Loss: 0.681106686592102
- Accuracy: 0.709136109384711
- Macro F1: 0.6987186860138147
- Micro F1: 0.709136109384711
- Weighted F1: 0.7059639788836748
- ... | {"language": "en", "tags": "autonlp", "datasets": ["DrishtiSharma/autonlp-data-Text-Classification-Catalonia-Independence-AutoNLP"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.622203603306694} | DrishtiSharma/autonlp-Text-Classification-Catalonia-Independence-AutoNLP-633018323 | null | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T07:28:50+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 633018323
- CO2 Emissions (in grams): 3.622203603306694
## Validation Metrics
- Loss: 0.681106686592102
- Accuracy: 0.709136109384711
- Macro F1: 0.6987186860138147
- Micro F1: 0.709136109384711
- Weighted F1: 0.7059639788836748
- ... | [
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"## Validation Metrics\n\n- Loss: 0.681106686592102\n- Accuracy: 0.709136109384711\n- Macro F1: 0.6987186860138147\n- Micro F1: 0.709136109384711\n- Weighted F1: 0.7... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n-... |
token-classification | transformers |
## HingBERT-LID
HingBERT-LID is a Hindi-English code-mixed language identification BERT model. It is a HingBERT model fine-tuned on L3Cube-HingLID dataset.
<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:/... | {"language": ["hi", "en", "multilingual"], "license": "cc-by-4.0", "tags": ["hi", "en", "codemix"], "datasets": ["L3Cube-HingCorpus", "L3Cube-HingLID"]} | l3cube-pune/hing-bert-lid | null | [
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"pytorch",
"safetensors",
"bert",
"token-classification",
"hi",
"en",
"codemix",
"multilingual",
"dataset:L3Cube-HingCorpus",
"dataset:L3Cube-HingLID",
"arxiv:2204.08398",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T07:36:24+00:00 | [
"2204.08398"
] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #dataset-L3Cube-HingLID #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## HingBERT-LID
HingBERT-LID is a Hindi-English code-mixed language identification BERT model. It is a HingBERT model fine-tuned on L3Cube-HingLID dataset.
<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 hr... | [
"## HingBERT-LID\nHingBERT-LID is a Hindi-English code-mixed language identification BERT model. It is a HingBERT model fine-tuned on L3Cube-HingLID dataset.\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:... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #dataset-L3Cube-HingLID #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## HingBERT-LID\nHingBERT-LID is a Hindi-English code-mixed lan... |
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. -->
# bart-large-cnn-weaksup-100-NOpad-early1
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/f... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-weaksup-100-NOpad-early1", "results": []}]} | cammy/bart-large-cnn-weaksup-100-NOpad-early1 | null | [
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"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T09:39:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-weaksup-100-NOpad-early1
=======================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0768
* Rouge1: 28.7953
* Rouge2: 10.9535
* Rougel: 20.6447
* Rougelsum: 24.3516
* Ge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #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\\_size: 1\n* eval\\_... |
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. -->
# bart-large-cnn-weaksup-100-NOpad-early2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/f... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-weaksup-100-NOpad-early2", "results": []}]} | cammy/bart-large-cnn-weaksup-100-NOpad-early2 | null | [
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"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T09:45:23+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-weaksup-100-NOpad-early2
=======================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0768
* Rouge1: 28.6914
* Rouge2: 11.1481
* Rougel: 20.6967
* Rougelsum: 24.2834
* Ge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_... |
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. -->
# bart-large-cnn-1000-lit-evalMA-NOpad
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/face... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-1000-lit-evalMA-NOpad", "results": []}]} | cammy/bart-large-cnn-1000-lit-evalMA-NOpad | null | [
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"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T10:08:09+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-1000-lit-evalMA-NOpad
====================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9804
* Rouge1: 27.2698
* Rouge2: 11.8561
* Rougel: 20.5948
* Rougelsum: 23.5497
* Gen Len:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
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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. -->
# bart-large-cnn-100-lit-evalMA-NOpad2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/face... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-100-lit-evalMA-NOpad2", "results": []}]} | cammy/bart-large-cnn-100-lit-evalMA-NOpad2 | null | [
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"text2text-generation",
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T10:56:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-100-lit-evalMA-NOpad2
====================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2126
* Rouge1: 25.6196
* Rouge2: 7.2753
* Rougel: 18.0987
* Rougelsum: 20.8416
* Gen Len: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
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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. -->
# bart-large-cnn-10k-lit-evalMA-NOpad
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/faceb... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-10k-lit-evalMA-NOpad", "results": []}]} | cammy/bart-large-cnn-10k-lit-evalMA-NOpad | null | [
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"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T11:12:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-10k-lit-evalMA-NOpad
===================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9464
* Rouge1: 28.6721
* Rouge2: 13.8303
* Rougel: 22.458
* Rougelsum: 25.668
* Gen Len: 66.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_... |
question-answering | transformers | # GELECTRA-large-LegalQuAD
## Overview
**Language model:** GELECTRA-large
**Language:** German
**Downstream-task:** Extractive QA
**Training data:** German-legal-SQuAD
**Eval data:** German-legal-SQuAD testset
## Hyperparameters
```
batch_size = 10
n_epochs = 2
max_seq_len=256,
learning_rate=1e-5,
## Eval result... | {"language": ["de"], "tags": ["qa"], "widget": [{"text": "", "context": "", "example_title": "Extractive QA"}]} | Christoph911/GELECTRA-large-LegalQuAD | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"qa",
"de",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T11:14:35+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #electra #question-answering #qa #de #endpoints_compatible #region-us
| # GELECTRA-large-LegalQuAD
## Overview
Language model: GELECTRA-large
Language: German
Downstream-task: Extractive QA
Training data: German-legal-SQuAD
Eval data: German-legal-SQuAD testset
## Hyperparameters
'''
batch_size = 10
n_epochs = 2
max_seq_len=256,
learning_rate=1e-5,
## Eval results
Evaluated on Germa... | [
"# GELECTRA-large-LegalQuAD",
"## Overview\nLanguage model: GELECTRA-large \nLanguage: German\nDownstream-task: Extractive QA \nTraining data: German-legal-SQuAD \nEval data: German-legal-SQuAD testset",
"## Hyperparameters\n'''\nbatch_size = 10\nn_epochs = 2\nmax_seq_len=256,\nlearning_rate=1e-5,",
"## Ev... | [
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"# GELECTRA-large-LegalQuAD",
"## Overview\nLanguage model: GELECTRA-large \nLanguage: German\nDownstream-task: Extractive QA \nTraining data: German-legal-SQuAD \nEval data: German-legal-SQuAD testset",
... |
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. -->
# finetuned
This model is a fine-tuned version of [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned", "results": []}]} | Danik51002/finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-13T12:10:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# finetuned
This model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperp... | [
"# finetuned\n\nThis model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proc... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# finetuned\n\nThis model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.",
"## Model descript... |
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. -->
# komrc_train
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the korquad d... | {"tags": ["generated_from_trainer"], "datasets": ["korquad"], "model-index": [{"name": "komrc_train", "results": []}]} | Taekyoon/komrc_train | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:korquad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T12:22:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-korquad #endpoints_compatible #region-us
| komrc\_train
============
This model is a fine-tuned version of beomi/kcbert-base on the korquad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6544
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: 1234\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-korquad #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: 16\n* eval\\_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. -->
# distilbert-base-uncased-finetuned-sem
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["sem_eval2010_task8"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sem", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sem_eval2010_task8", "typ... | leonadase/distilbert-base-uncased-finetuned-sem | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:sem_eval2010_task8",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T13:14:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-sem_eval2010_task8 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sem
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the sem\_eval2010\_task8 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6704
* Accuracy: 0.8314
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-sem_eval2010_task8 #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... |
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-hindi
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-large-xls-r-300m-hindi", "results": []}]} | Devendr/wav2vec2-large-xls-r-300m-hindi | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T14:01:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice da... |
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/1498704685875744769/r3jT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mikepompeo/1647181695747/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mikepompeo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-13T14:27:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mike Pompeo
@mikepompeo
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"
] |
null | 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. -->
# test_mae_flysheet
This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on... | {"license": "apache-2.0", "tags": ["masked-auto-encoding", "generated_from_trainer"], "datasets": ["image_folder"], "base_model": "facebook/vit-mae-base", "model-index": [{"name": "test_mae_flysheet", "results": []}]} | davanstrien/test_mae_flysheet | null | [
"transformers",
"pytorch",
"tensorboard",
"vit_mae",
"pretraining",
"masked-auto-encoding",
"generated_from_trainer",
"dataset:image_folder",
"base_model:facebook/vit-mae-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T15:30:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #dataset-image_folder #base_model-facebook/vit-mae-base #license-apache-2.0 #endpoints_compatible #region-us
| test\_mae\_flysheet
===================
This model is a fine-tuned version of facebook/vit-mae-base on the davanstrien/flysheet dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2675
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3.75e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_... | [
"TAGS\n#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #dataset-image_folder #base_model-facebook/vit-mae-base #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# NewModel
This model is a fine-tuned version of [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "NewModel", "results": []}]} | Danik51002/NewModel | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-13T16:51:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NewModel
This model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpa... | [
"# NewModel\n\nThis model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proce... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model descripti... |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Sivakumar/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T17:08:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4101
Model description
-----------------
More information needed
Intended u... | [
"### 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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #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\... |
null | transformers | # Note
This model is training with 180k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
... | {"language": ["en"], "license": "mit", "tags": ["aspect-based-sentiment-analysis", "lcf-bert"], "datasets": ["laptop14 (w/ augmentation)", "restaurant14 (w/ augmentation)", "restaurant16 (w/ augmentation)", "ACL-Twitter (w/ augmentation)", "MAMS (w/ augmentation)", "Television (w/ augmentation)", "TShirt (w/ augmentati... | yangheng/deberta-v3-large-absa | null | [
"transformers",
"pytorch",
"deberta-v2",
"aspect-based-sentiment-analysis",
"lcf-bert",
"en",
"arxiv:2110.08604",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T17:17:55+00:00 | [
"2110.08604"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #aspect-based-sentiment-analysis #lcf-bert #en #arxiv-2110.08604 #license-mit #endpoints_compatible #region-us
| # Note
This model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
# DeBERTa for aspect-based sentiment analysi... | [
"# Note\r\nThis model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)",
"# DeBERTa for aspect-based sentimen... | [
"TAGS\n#transformers #pytorch #deberta-v2 #aspect-based-sentiment-analysis #lcf-bert #en #arxiv-2110.08604 #license-mit #endpoints_compatible #region-us \n",
"# Note\r\nThis model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model f... |
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-ft-with-non-challenging
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-ft-with-non-challenging", "results": []}]} | newtonkwan/gpt2-ft-with-non-challenging | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-13T17:49:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-ft-with-non-challenging
============================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9906
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: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_bat... |
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
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown da... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-ft-with-non-challenging", "results": []}]} | newtonkwan/gpt2-xl-ft-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-13T18:22:41+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
===============================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4872
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: 2020\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... |
text2text-generation | transformers |
# M2M100 12B (average of last 5 checkpoints)
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) ... | {"language": ["multilingual", "af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn",... | facebook/m2m100-12B-avg-5-ckpt | null | [
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"g... | null | 2022-03-13T18:25:42+00:00 | [
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"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
... | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #m2m100-12B #multilingual #af #am #ar #ast #az #ba #be #bg #bn #br #bs #ca #ceb #cs #cy #da #de #el #en #es #et #fa #ff #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #ht #hu #hy #id #ig #ilo #is #it #ja #jv #ka #kk #km #kn #ko #lb #lg #ln #lo #lt #lv #mg #mk #ml ... |
# M2M100 12B (average of last 5 checkpoints)
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this paper and first released in this repository.
The model that can directly translate between the 9,900 directions of 100 languages.
To tr... | [
"# M2M100 12B (average of last 5 checkpoints)\n\nM2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.\nIt was introduced in this paper and first released in this repository.\n\nThe model that can directly translate between the 9,900 directions of 100 languag... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #m2m100-12B #multilingual #af #am #ar #ast #az #ba #be #bg #bn #br #bs #ca #ceb #cs #cy #da #de #el #en #es #et #fa #ff #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #ht #hu #hy #id #ig #ilo #is #it #ja #jv #ka #kk #km #kn #ko #lb #lg #ln #lo #lt #lv #mg #m... |
text2text-generation | transformers |
# IteraTeR PEGASUS model
This model was obtained by fine-tuning [google/pegasus-large](https://huggingface.co/google/pegasus-large) on [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03... | {"datasets": ["IteraTeR_full_sent"]} | wanyu/IteraTeR-PEGASUS-Revision-Generator | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-13T18:55:49+00:00 | [
"2203.03802"
] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #dataset-IteraTeR_full_sent #arxiv-2203.03802 #autotrain_compatible #endpoints_compatible #has_space #region-us
| IteraTeR PEGASUS model
======================
This model was obtained by fine-tuning google/pegasus-large on IteraTeR-full-sent dataset.
Paper: Understanding Iterative Revision from Human-Written Text
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
Text Revision Task
... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #dataset-IteraTeR_full_sent #arxiv-2203.03802 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
# IteraTeR RoBERTa model
This model was obtained by fine-tuning [roberta-large](https://huggingface.co/roberta-large) on [IteraTeR-human-sent](https://huggingface.co/datasets/wanyu/IteraTeR_human_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
Au... | {"datasets": ["IteraTeR_full_sent"]} | wanyu/IteraTeR-ROBERTA-Intention-Classifier | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T19:10:06+00:00 | [
"2203.03802"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #dataset-IteraTeR_full_sent #arxiv-2203.03802 #autotrain_compatible #endpoints_compatible #region-us
| IteraTeR RoBERTa model
======================
This model was obtained by fine-tuning roberta-large on IteraTeR-human-sent dataset.
Paper: Understanding Iterative Revision from Human-Written Text
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
Edit Intention Prediction... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #dataset-IteraTeR_full_sent #arxiv-2203.03802 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# M2M100 12B (average of last 10 checkpoints)
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100)... | {"language": ["multilingual", "af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn",... | facebook/m2m100-12B-avg-10-ckpt | null | [
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"fy",
"g... | null | 2022-03-13T21:10:48+00:00 | [
"2010.11125"
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"hi",
"hr",
"ht",
"hu",
"hy",
... | TAGS
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# M2M100 12B (average of last 10 checkpoints)
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this paper and first released in this repository.
The model that can directly translate between the 9,900 directions of 100 languages.
To t... | [
"# M2M100 12B (average of last 10 checkpoints)\n\nM2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.\nIt was introduced in this paper and first released in this repository.\n\nThe model that can directly translate between the 9,900 directions of 100 langua... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #m2m100-12B #multilingual #af #am #ar #ast #az #ba #be #bg #bn #br #bs #ca #ceb #cs #cy #da #de #el #en #es #et #fa #ff #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #ht #hu #hy #id #ig #ilo #is #it #ja #jv #ka #kk #km #kn #ko #lb #lg #ln #lo #lt #lv #mg #m... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/roshansh_asr_base_sp_conformer_swbd`
This model was trained by roshansh-cmu using swbd recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout a04a98c98797b314f2425082bc40261757fd47de
pip install -e .
cd egs2/swbd/asr1
... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["swbd"]} | espnet/roshansh_asr_base_sp_conformer_swbd | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:swbd",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-13T21:11:41+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-swbd #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/roshansh\_asr\_base\_sp\_conformer\_swbd'
This model was trained by roshansh-cmu using swbd recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Mar 13 17:23:58 EDT 2022'
* python version: '3.8.12 (default, O... | [
"### 'espnet/roshansh\\_asr\\_base\\_sp\\_conformer\\_swbd'\n\n\nThis model was trained by roshansh-cmu using swbd recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Mar 13 17:23:58 EDT 2022'\n* python version: '3.8.12 (default, Oct 12 2021... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-swbd #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/roshansh\\_asr\\_base\\_sp\\_conformer\\_swbd'\n\n\nThis model was trained by roshansh-cmu using swbd recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\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. -->
# distilbert-base-uncased-finetuned-advers
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["adversarial_qa"], "model-index": [{"name": "distilbert-base-uncased-finetuned-advers", "results": []}]} | T-qualizer/distilbert-base-uncased-finetuned-advers | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:adversarial_qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T21:23:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-adversarial_qa #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-advers
========================================
This model is a fine-tuned version of distilbert-base-uncased on the adversarial\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6462
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 3000",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-adversarial_qa #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: 9e-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/493786234221641730/OFQm2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ayurastro/1647214031676/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ayurastro | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-13T23:26:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
AyurAstro®
@ayurastro
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-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... | wypoon/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 | 2022-03-14T00:27:34+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: 0.2243
* Accuracy: 0.919
* F1: 0.9193
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... |
text-generation | transformers |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | DB13067/Peterbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-14T01:44:26+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peter from Your Boyfriend Game.
| [
"# Peter from Your Boyfriend Game."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter from Your Boyfriend Game."
] |
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. -->
# efl-finetuned-cola
This model is a fine-tuned version of [nghuyong/ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) o... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "efl-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_... | kapilchauhan/efl-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T04:48:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| efl-finetuned-cola
==================
This model is a fine-tuned version of nghuyong/ernie-2.0-en on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4688
* Matthews Correlation: 0.6098
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #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: 3e-05\n* train\\_... |
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. -->
# bert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "met... | BAHIJA/bert-base-uncased-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T04:52:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-sst2
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2745
* Accuracy: 0.9346
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
text-generation | transformers | - This model forked from [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2).
- You can use this model in [Teachable-NLP](https://ainize.ai/teachable-nlp).
For more details: https://github.com/SKT-AI/KoGPT2
| {"language": "ko", "license": "cc-by-nc-sa-4.0", "tags": ["gpt2"]} | ComCom/skt_kogpt2-base-v2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-14T06:28:29+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| - This model forked from skt/kogpt2-base-v2.
- You can use this model in Teachable-NLP.
For more details: URL
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# distilbert-base-uncased-holtin-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-holtin-finetuned-squad", "results": []}]} | holtin/distilbert-base-uncased-holtin-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T07:57:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-holtin-finetuned-squad
==============================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8541
Model description
-----------------
More information neede... | [
"### 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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #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\... |
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... | robertou2/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 | 2022-03-14T08:34:50+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: 0.2368
* Accuracy: 0.9325
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\... |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | aaraki/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T08:42:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2248
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #distilbert #question-answering #generated_from_trainer #dataset-squad #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\\_s... |
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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | lijingxin/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T09:05:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7755
* Accuracy: 0.9161
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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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* lea... |
null | null | The growth of digitalization is reshaping businesses, industries, and individuals from all walks of life.
It is the age of conversational commerce, and Chatbot is paired with many O.T.T. apps in the automobile sector.
And Chatbots are rapidly showing to be a holistic answer for company communication procedures.
... | {} | yugasa/Chatbots-for-the-Automotive-Industry | null | [
"region:us"
] | null | 2022-03-14T09:18:00+00:00 | [] | [] | TAGS
#region-us
| The growth of digitalization is reshaping businesses, industries, and individuals from all walks of life.
It is the age of conversational commerce, and Chatbot is paired with many O.T.T. apps in the automobile sector.
And Chatbots are rapidly showing to be a holistic answer for company communication procedures.
... | [] | [
"TAGS\n#region-us \n"
] |
sentence-similarity | sentence-transformers |
# Kalaoke/embeddings_dense_model
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model b... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Kalaoke/embeddings_dense_model | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T09:53:55+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# Kalaoke/embeddings_dense_model
This is a sentence-transformers model: It maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
... | [
"# Kalaoke/embeddings_dense_model\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers i... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# Kalaoke/embeddings_dense_model\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like cluster... |
text-generation | transformers | # GPT-Neo 1.3B - Adventure
## Model Description
GPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.
## Training data
The training data is a direct copy of the "cys" dataset by VE, a CYOA-based dataset.
### How to use
You can use this model directly with a pipeline for text generati... | {"language": "en", "license": "mit", "pipeline_tag": "text-generation"} | KoboldAI/GPT-Neo-1.3B-Adventure | null | [
"transformers",
"pytorch",
"safetensors",
"gpt_neo",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-14T10:10:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # GPT-Neo 1.3B - Adventure
## Model Description
GPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.
## Training data
The training data is a direct copy of the "cys" dataset by VE, a CYOA-based dataset.
### How to use
You can use this model directly with a pipeline for text generati... | [
"# GPT-Neo 1.3B - Adventure",
"## Model Description\r\nGPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.",
"## Training data\r\nThe training data is a direct copy of the \"cys\" dataset by VE, a CYOA-based dataset.",
"### How to use\r\nYou can use this model directly with a p... | [
"TAGS\n#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT-Neo 1.3B - Adventure",
"## Model Description\r\nGPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.",
"## Training... |
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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | lijingxin/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T10:33:00+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2782
* Accuracy: 0.9471
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: 48\n* eval\\_batch\\_size: 48\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",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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\\_rate:... |
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. -->
# distil_bert_uncased-finetuned-relations
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "recall", "f1"], "model-index": [{"name": "distil_bert_uncased-finetuned-relations", "results": []}]} | nikolamilosevic/distil_bert_uncased-finetuned-relations | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T11:08:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distil\_bert\_uncased-finetuned-relations
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4191
* Accuracy: 0.8866
* Prec: 0.8771
* Recall: 0.8866
* F1: 0.8808
Model descr... | [
"### 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 #safetensors #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... |
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-bart-large-no-adapter | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T12:33:35+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: 5.6120
* Wer: 1.0267
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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/fiqa-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:16:49+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/scifact-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:16:53+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/bioasq-1m-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:17:45+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/trec-covid-v2-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:18:01+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/cqadupstack-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:18:17+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/robust04-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:18:35+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/trec-covid-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:22:10+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/bioasq-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:22:29+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/arguana-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:22:45+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/climate-fever-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:23:02+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/dbpedia-entity-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:23:19+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/fever-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:23:36+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/hotpotqa-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:23:53+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/nfcorpus-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:24:10+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/nq-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:24:27+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/quora-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:24:44+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/signal1m-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:25:00+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/trec-news-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:25:17+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/webis-touche2020-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:25:34+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/scidocs-distilbert-tas-b-gpl-self_miner | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:25:59+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
text-classification | transformers | This model provides a MobileBERT [(Sun et al., 2020)](https://arxiv.org/abs/2004.02984) fine-tuned on the SST data with three sentiments (0 -- negative, 1 -- neutral, and 2 -- positive).
## Example Usage
Below, we provide illustrations on how to use this model to make sentiment predictions.
```python
import torch... | {} | cambridgeltl/sst_mobilebert-uncased | null | [
"transformers",
"pytorch",
"mobilebert",
"text-classification",
"arxiv:2004.02984",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T14:35:36+00:00 | [
"2004.02984"
] | [] | TAGS
#transformers #pytorch #mobilebert #text-classification #arxiv-2004.02984 #autotrain_compatible #endpoints_compatible #region-us
| This model provides a MobileBERT (Sun et al., 2020) fine-tuned on the SST data with three sentiments (0 -- negative, 1 -- neutral, and 2 -- positive).
## Example Usage
Below, we provide illustrations on how to use this model to make sentiment predictions.
:
If you find this model useful, please kindly cite our... | [
"## Example Usage\n\nBelow, we provide illustrations on how to use this model to make sentiment predictions. \n\n\n \n\n:\nIf you find this model useful, please kindly cite our model as"
] | [
"TAGS\n#transformers #pytorch #mobilebert #text-classification #arxiv-2004.02984 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Example Usage\n\nBelow, we provide illustrations on how to use this model to make sentiment predictions. \n\n\n \n\n:\nIf you find this model useful, please kindly cite ... |
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-12-v2 | null | [
"sentence-transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"license:mit",
"region:us"
] | null | 2022-03-14T14:52:30+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"
] |
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/1511150115582525442/9l-w... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/temapex | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-14T15:47:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ema Pex ペクスえま
@temapex
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 |
<!-- 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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the f... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds", "results": []}]} | peterhsu/codeparrot-ds | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-14T15:52:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codeparrot-ds
=============
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9729
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n... |
null | transformers |
# Graphcore/bert-base-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Graphcore/wikipedia-bert-128", "Graphcore/wikipedia-bert-512"], "model-index": [{"name": "Graphcore/bert-base-uncased", "results": []}]} | Graphcore/bert-base-uncased | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"bert",
"generated_from_trainer",
"dataset:Graphcore/wikipedia-bert-128",
"dataset:Graphcore/wikipedia-bert-512",
"arxiv:1904.00962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T15:54:38+00:00 | [
"1904.00962"
] | [] | TAGS
#transformers #pytorch #optimum_graphcore #bert #generated_from_trainer #dataset-Graphcore/wikipedia-bert-128 #dataset-Graphcore/wikipedia-bert-512 #arxiv-1904.00962 #license-apache-2.0 #endpoints_compatible #region-us
|
# Graphcore/bert-base-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphc... | [
"# Graphcore/bert-base-uncased\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #bert #generated_from_trainer #dataset-Graphcore/wikipedia-bert-128 #dataset-Graphcore/wikipedia-bert-512 #arxiv-1904.00962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Graphcore/bert-base-uncased\n\nOptimum Graphcore is a new open-source library an... |
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. -->
# detect-femicide-news-xlmr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "detect-femicide-news-xlmr", "results": []}]} | gossminn/detect-femicide-news-xlmr | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T16:21:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| detect-femicide-news-xlmr
=========================
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: 0.0161
* Accuracy: 0.9973
* Precision Neg: 0.9975
* Precision Pos: 0.9967
* Recall Neg: 0.9988
* Recall Pos: 0.9933
* F1 ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 128\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: 6",
"### 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 |
# ViT5-large
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
## How to use
For more details, do check out [our Github repo](https://github.com/vietai/ViT5).
[Finetunning Example can be found here](https://github.com/vietai/ViT5/tree/main/finetunning_huggingface).
```python
from... | {"language": "vi", "license": "mit", "tags": ["summarization", "translation", "question-answering"], "datasets": ["cc100"]} | VietAI/vit5-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"summarization",
"translation",
"question-answering",
"vi",
"dataset:cc100",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-14T16:35:55+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #translation #question-answering #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ViT5-large
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
## How to use
For more details, do check out our Github repo.
Finetunning Example can be found here.
| [
"# ViT5-large\n\nState-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.",
"## How to use\nFor more details, do check out our Github repo. \n\nFinetunning Example can be found here."
] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #translation #question-answering #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ViT5-large\n\nState-of-the-art pretrained Transformer-based encoder-decoder mod... |
question-answering | transformers |
# ViT5-base
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
## How to use
For more details, do check out [our Github repo](https://github.com/vietai/ViT5).
[Finetunning Example can be found here](https://github.com/vietai/ViT5/tree/main/finetunning_huggingface).
```python
from ... | {"language": "vi", "license": "mit", "tags": ["summarization", "translation", "question-answering"], "datasets": ["cc100"]} | VietAI/vit5-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"summarization",
"translation",
"question-answering",
"vi",
"dataset:cc100",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-14T16:36:06+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #translation #question-answering #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# ViT5-base
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
## How to use
For more details, do check out our Github repo.
Finetunning Example can be found here.
| [
"# ViT5-base\n\nState-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.",
"## How to use\nFor more details, do check out our Github repo. \n\nFinetunning Example can be found here."
] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #translation #question-answering #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# ViT5-base\n\nState-of-the-art pretrained Transformer-based encoder-d... |
text-classification | transformers | ### distilbert-base-uncased-finetuned-btc for PH66 Unwanted Event
- This is our initial attempt on using the transformers for BTC-PH66.
- This test file used in this model are in projects with Ids [1065, 950, 956, 2650]. The other 4 projects were not included as they resulted very low accuracy with ML models.
- The... | {} | mmohamme/distilbert-base-uncased-finetuned-btc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-14T16:41:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### distilbert-base-uncased-finetuned-btc for PH66 Unwanted Event
- This is our initial attempt on using the transformers for BTC-PH66.
- This test file used in this model are in projects with Ids [1065, 950, 956, 2650]. The other 4 projects were not included as they resulted very low accuracy with ML models.
- The... | [
"### distilbert-base-uncased-finetuned-btc for PH66 Unwanted Event\n\n- This is our initial attempt on using the transformers for BTC-PH66.\n\n- This test file used in this model are in projects with Ids [1065, 950, 956, 2650]. The other 4 projects were not included as they resulted very low accuracy with ML mode... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### distilbert-base-uncased-finetuned-btc for PH66 Unwanted Event\n\n- This is our initial attempt on using the transformers for BTC-PH66.\n\n- This test file used in... |
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. -->
# finetune_indian_asr
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700](https://huggi... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "finetune_indian_asr", "results": []}]} | Simply-divine/finetune_indian_asr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T16:58:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| finetune\_indian\_asr
=====================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4215
* Wer: 0.3403
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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\\_batch\\_... |
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/disti... | {"license": "apache-2.0", "tags": ["hf-course", "generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "model-index": [{"name": "distilhubert-finetuned-gtzan", "results": []}]} | lewtun/distilhubert-finetuned-gtzan | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"hf-course",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-14T17:10:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #hf-course #generated_from_trainer #dataset-marsyas/gtzan #license-apache-2.0 #endpoints_compatible #has_space #region-us
| distilhubert-finetuned-gtzan
============================
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6694
* Accuracy: 0.82
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #audio-classification #hf-course #generated_from_trainer #dataset-marsyas/gtzan #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... |
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. -->
# ASCEND_Dataset_Model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ASCEND_Dataset_Model", "results": []}]} | GleamEyeBeast/ASCEND_Dataset_Model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T17:38:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ASCEND\_Dataset\_Model
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9199
* Wer: 0.9540
* Cer: 0.9868
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
text-generation | transformers | This is A GPT2 Fine Tuned Model for Poems in Portuguese
This Model still has a lot to improve, to generate a Poem you need to write on the generator "Poema: " or "Poema: ",
the Title of the Poem and \n. You are only allowed to use this software for academic purposes any commercial is not allowed,
any paper or research... | {} | MarioJ/Portuguese-Poems-Small-Gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-14T17:47:43+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is A GPT2 Fine Tuned Model for Poems in Portuguese
This Model still has a lot to improve, to generate a Poem you need to write on the generator "Poema: " or "Poema: ",
the Title of the Poem and \n. You are only allowed to use this software for academic purposes any commercial is not allowed,
any paper or research... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | fastai |
# Fine Tune En-ML translation
* source group: English
* target group: Malayalam
This is a Machine translation model created for fun to translate from English text to Malayalam which was fine-tuned for KDE-Dataset.
[Tweet](https://twitter.com/kurianbenoy2/status/1503082136009465857?s=20&t=7Hn-KUqHZRY6VJ16-i1... | {"language": ["en", "ml"], "license": "mit", "tags": ["fastai", "translation"]} | kurianbenoy/kde_en_ml_translation_model | null | [
"fastai",
"pytorch",
"marian",
"translation",
"en",
"ml",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-14T17:58:27+00:00 | [] | [
"en",
"ml"
] | TAGS
#fastai #pytorch #marian #translation #en #ml #license-mit #has_space #region-us
|
# Fine Tune En-ML translation
* source group: English
* target group: Malayalam
This is a Machine translation model created for fun to translate from English text to Malayalam which was fine-tuned for KDE-Dataset.
Tweet
# Model card
## Model description
Used a fine tuned model on top of MarianMT mo... | [
"# Fine Tune En-ML translation\r\n\r\n* source group: English\r\n* target group: Malayalam\r\n\r\nThis is a Machine translation model created for fun to translate from English text to Malayalam which was fine-tuned for KDE-Dataset.\r\n\r\nTweet",
"# Model card",
"## Model description\r\n\r\nUsed a fine tuned mo... | [
"TAGS\n#fastai #pytorch #marian #translation #en #ml #license-mit #has_space #region-us \n",
"# Fine Tune En-ML translation\r\n\r\n* source group: English\r\n* target group: Malayalam\r\n\r\nThis is a Machine translation model created for fun to translate from English text to Malayalam which was fine-tuned for KD... |
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. -->
# xtreme_s_xlsr_minds14
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "google/xtreme_s", "generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_xlsr_minds14", "results": []}]} | anton-l/xtreme_s_xlsr_300m_minds14_old_splits | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"automatic-speech-recognition",
"google/xtreme_s",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-14T18:02:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #automatic-speech-recognition #google/xtreme_s #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| xtreme\_s\_xlsr\_minds14
========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - MINDS14 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2890
* F1: 0.9474
* Accuracy: 0.9470
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #automatic-speech-recognition #google/xtreme_s #generated_from_trainer #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* ... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/librispeech_conformer_hop_length160`
This model was trained by Yifan Peng using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 33edd1fc077f6a35e8cb0a59f208cb4564aa4cfb
pip install -e .
cd egs2/libris... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | pyf98/librispeech_conformer_hop_length160 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-14T18:16:15+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/librispeech\_conformer\_hop\_length160'
This model was trained by Yifan Peng using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Mar 14 12:26:10 EDT 2022'
* python version: '3.9.7 (default, ... | [
"### 'pyf98/librispeech\\_conformer\\_hop\\_length160'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Mar 14 12:26:10 EDT 2022'\n* python version: '3.9.7 (default, Sep 16 2021,... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/librispeech\\_conformer\\_hop\\_length160'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\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. -->
# pegasus-large-finetuned-Pubmed
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasu... | {"tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-large-finetuned-Pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_summarization_dataset... | Kevincp560/pegasus-large-finetuned-Pubmed | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T18:17:23+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-large-finetuned-Pubmed
==============================
This model is a fine-tuned version of google/pegasus-large on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7669
* Rouge1: 39.1107
* Rouge2: 15.4127
* Rougel: 24.3729
* Rougelsum: 35.1236... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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 #pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #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: 2... |
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... | keerthisaran/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 | 2022-03-14T18:45:20+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: 0.2183
* Accuracy: 0.92
* F1: 0.9204
Model description
-----------------
More... | [
"### 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... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 640218740
- CO2 Emissions (in grams): 43.078469852595994
## Validation Metrics
- Loss: 0.8302136063575745
- Accuracy: 0.7887341933835739
- Macro F1: 0.5756730305293746
- Micro F1: 0.7887341933835739
- Weighted F1: 0.787894257091572... | {"language": "unk", "tags": "autonlp", "datasets": ["gabitoo1234/autonlp-data-mut_uchile"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 43.078469852595994} | gabitoo1234/autonlp-mut_uchile-640218740 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:gabitoo1234/autonlp-data-mut_uchile",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T18:57:53+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-gabitoo1234/autonlp-data-mut_uchile #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 640218740
- CO2 Emissions (in grams): 43.078469852595994
## Validation Metrics
- Loss: 0.8302136063575745
- Accuracy: 0.7887341933835739
- Macro F1: 0.5756730305293746
- Micro F1: 0.7887341933835739
- Weighted F1: 0.787894257091572... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 640218740\n- CO2 Emissions (in grams): 43.078469852595994",
"## Validation Metrics\n\n- Loss: 0.8302136063575745\n- Accuracy: 0.7887341933835739\n- Macro F1: 0.5756730305293746\n- Micro F1: 0.7887341933835739\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-gabitoo1234/autonlp-data-mut_uchile #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 640218740\n- CO2 Emissions (in... |
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. -->
# finetuning-sentiment-model-12000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ag_news"], "model-index": [{"results": []}]} | mansidw/finetuning-sentiment-model-12000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T19:40:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-ag_news #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-12000-samples
This model is a fine-tuned version of distilbert-base-uncased on the ag_news dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# finetuning-sentiment-model-12000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ag_news 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 #distilbert #text-classification #generated_from_trainer #dataset-ag_news #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-12000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the a... |
text-classification | transformers |
Check https://github.com/tae898/erc for the details
[Watch a demo video!](https://youtu.be/qbr7fNd6J28)
# Emotion Recognition in Coversation (ERC)
[](h... | {"language": "en", "license": "mit", "tags": ["emoberta", "roberta"], "datasets": ["MELD", "IEMOCAP"]} | tae898/emoberta-base | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"emoberta",
"en",
"dataset:MELD",
"dataset:IEMOCAP",
"arxiv:2108.12009",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T20:03:08+00:00 | [
"2108.12009"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #emoberta #en #dataset-MELD #dataset-IEMOCAP #arxiv-2108.12009 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Check URL for the details
Watch a demo video!
Emotion Recognition in Coversation (ERC)
========================================
![PWC](URL
![PWC](URL
At the moment, we only use the text modality to correctly classify the emotion of the utterances.The experiments were carried out on two datasets (i.e. MELD and I... | [
"### Huggingface\n\n\nWe have released our models on huggingface:\n\n\n* emoberta-base\n* emoberta-large\n\n\nThey are based on RoBERTa-base and RoBERTa-large, respectively. They were trained on both MELD and IEMOCAP datasets. Our deployed models are neither speaker-aware nor take previous utterances into account, ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #emoberta #en #dataset-MELD #dataset-IEMOCAP #arxiv-2108.12009 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface\n\n\nWe have released our models on huggingface:\n\n\n* emoberta-base\n* emoberta-large\n\n\nThey are... |
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. -->
# xtreme_s_xlsr_300m_mls
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "model-index": [{"name": "xtreme_s_xlsr_mls", "results": []}]} | anton-l/xtreme_s_xlsr_300m_mls | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"google/xtreme_s",
"generated_from_trainer",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T20:25:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #google/xtreme_s #generated_from_trainer #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_mls
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - MLS dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6215
* Wer: 0.3033
* Cer: 0.0951
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #google/xtreme_s #generated_from_trainer #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
Check https://github.com/tae898/erc for the details
[Watch a demo video!](https://youtu.be/qbr7fNd6J28)
# Emotion Recognition in Coversation (ERC)
[](h... | {"language": "en", "license": "mit", "tags": ["emoberta", "roberta"], "datasets": ["MELD", "IEMOCAP"]} | tae898/emoberta-large | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"emoberta",
"en",
"dataset:MELD",
"dataset:IEMOCAP",
"arxiv:2108.12009",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T20:33:23+00:00 | [
"2108.12009"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #emoberta #en #dataset-MELD #dataset-IEMOCAP #arxiv-2108.12009 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Check URL for the details
Watch a demo video!
Emotion Recognition in Coversation (ERC)
========================================
![PWC](URL
![PWC](URL
At the moment, we only use the text modality to correctly classify the emotion of the utterances.The experiments were carried out on two datasets (i.e. MELD and I... | [
"### Huggingface\n\n\nWe have released our models on huggingface:\n\n\n* emoberta-base\n* emoberta-large\n\n\nThey are based on RoBERTa-base and RoBERTa-large, respectively. They were trained on both MELD and IEMOCAP datasets. Our deployed models are neither speaker-aware nor take previous utterances into account, ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #emoberta #en #dataset-MELD #dataset-IEMOCAP #arxiv-2108.12009 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface\n\n\nWe have released our models on huggingface:\n\n\n* emoberta-base\n* emoberta-large\n\n\nThey are... |
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. -->
# m2m-tai-en-de-gen-1.2B-1k-steps
This model was trained from scratch on an unknown dataset.
## Model description
More informati... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "m2m-tai-en-de-gen-1.2B-1k-steps", "results": []}]} | bettertextapp/m2m-tai-en-de-gen-1.2B-1k-steps | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T20:40:42+00:00 | [] | [] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# m2m-tai-en-de-gen-1.2B-1k-steps
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fol... | [
"# m2m-tai-en-de-gen-1.2B-1k-steps\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tra... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# m2m-tai-en-de-gen-1.2B-1k-steps\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended us... |
automatic-speech-recognition | transformers |
A freshly initialized seq2seq model | {"license": "apache-2.0"} | anton-l/xls-r-300m-mbart-large-50 | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T20:43:33+00:00 | [] | [] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #license-apache-2.0 #endpoints_compatible #region-us
|
A freshly initialized seq2seq model | [] | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
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_English
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](htt... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner-CRAFT_English", "results": []}]} | StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-CRAFT_English | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-14T22:56:59+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\_English
====================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1614
* Precision: 0.8585
* Recall: 0.8623
* F1: 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\\_... |
image-classification | transformers |
# digital
## Example Images
#### ansys
#### blender
#### roblox
#### sketchup
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | lazyturtl/digital | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T00:21:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# digital
## Example Images
#### ansys
!ansys
#### blender
!blender
#### roblox
!roblox
#### sketchup
!sketchup | [
"# digital",
"## Example Images",
"#### ansys\n\n!ansys",
"#### blender\n\n!blender",
"#### roblox\n\n!roblox",
"#### sketchup\n\n!sketchup"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# digital",
"## Example Images",
"#### ansys\n\n!ansys",
"#### blender\n\n!blender",
"#### roblox\n\n!roblox",
"#### sketchup\n\n!sketchup"
] |
text2text-generation | transformers |
# IteraTeR BART model
This model was obtained by fine-tuning [facebook/bart-base](https://huggingface.co/facebook/bart-base) on [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <b... | {"datasets": ["IteraTeR_full_sent"]} | wanyu/IteraTeR-BART-Revision-Generator | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T01:21:43+00:00 | [
"2203.03802"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #dataset-IteraTeR_full_sent #arxiv-2203.03802 #autotrain_compatible #endpoints_compatible #region-us
| IteraTeR BART model
===================
This model was obtained by fine-tuning facebook/bart-base on IteraTeR-full-sent dataset.
Paper: Understanding Iterative Revision from Human-Written Text
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
Text Revision Task
--------... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #dataset-IteraTeR_full_sent #arxiv-2203.03802 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark.
The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/tw... | {"language": "en", "datasets": ["tweet_eval"], "widget": [{"text": "Covid cases are increasing fast!"}]} | cardiffnlp/twitter-roberta-base-sentiment-latest | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"arxiv:2202.03829",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-15T01:21:58+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #roberta #text-classification #en #dataset-tweet_eval #arxiv-2202.03829 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark.
The original Twitter-based RoBERTa model can be found here and the original reference paper is Tw... | [
"# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)\n\nThis is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. \nThe original Twitter-based RoBERTa model can be found here and the original reference paper... | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #en #dataset-tweet_eval #arxiv-2202.03829 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)\n\nThis is a RoBERTa-base model trained on ~124M tweets from January 2018... |
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. -->
# poem-gen-gpt2-small-spanish
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datific... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-gpt2-small-spanish", "results": []}]} | hackathon-pln-es/poem-gen-gpt2-small-spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-15T04:09:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# poem-gen-gpt2-small-spanish
This model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.1366
- eval_runtime: 25.1623
- eval_samples_per_second: 43.676
- eval_steps_per_second: 10.929
- epoch: 0.78
- step: 2040
... | [
"# poem-gen-gpt2-small-spanish\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 4.1366\n- eval_runtime: 25.1623\n- eval_samples_per_second: 43.676\n- eval_steps_per_second: 10.929\n- epoch: 0.78\n- s... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# poem-gen-gpt2-small-spanish\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown d... |
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... | mjc00/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 | 2022-03-15T05:23:44+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: 0.2153
* Accuracy: 0.924
* F1: 0.9241
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... |
text2text-generation | transformers | IndicBARTSS is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBARTSS model to build natural language generation applications for Indian languages by finetuning the model wi... | {} | ai4bharat/IndicBARTSS | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2109.02903",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-15T05:24:04+00:00 | [
"2109.02903"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2109.02903 #autotrain_compatible #endpoints_compatible #has_space #region-us
| IndicBARTSS is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBARTSS model to build natural language generation applications for Indian languages by finetuning the model wi... | [
"# Pre-training corpus\n\nWe used the <a href=\"URL data spanning 12 languages with 452 million sentences (9 billion tokens). The model was trained using the text-infilling objective used in mBART.",
"# Usage:",
"# Notes:\n1. This is compatible with the latest version of transformers but was developed with vers... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #arxiv-2109.02903 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Pre-training corpus\n\nWe used the <a href=\"URL data spanning 12 languages with 452 million sentences (9 billion tokens). The model was trained using the text-inf... |
null | transformers | # BioBERTurk- Turkish Biomedical Language Models
---
language:
- tr
--- | {} | hazal/BioBERTurkcased-con-trM | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T06:29:39+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| # BioBERTurk- Turkish Biomedical Language Models
---
language:
- tr
--- | [
"# BioBERTurk- Turkish Biomedical Language Models\n---\nlanguage: \n - tr\n---"
] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"# BioBERTurk- Turkish Biomedical Language Models\n---\nlanguage: \n - tr\n---"
] |
automatic-speech-recognition | transformers |
# wav2vec2-base-da-ft-nst
This the [alvenir wav2vec2 model](https://huggingface.co/Alvenir/wav2vec2-base-da) for Danish ASR finetuned by Alvenir on the public NST dataset. The model is trained on 16kHz, so make sure your data is the same sample rate.
The model was trained using fairseq and then converted to hugg... | {"language": "da", "license": "apache-2.0", "tags": ["speech-to-text"]} | Alvenir/wav2vec2-base-da-ft-nst | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech-to-text",
"da",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-15T08:16:18+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech-to-text #da #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-da-ft-nst
=======================
This the alvenir wav2vec2 model for Danish ASR finetuned by Alvenir on the public NST dataset. The model is trained on 16kHz, so make sure your data is the same sample rate.
The model was trained using fairseq and then converted to huggingface/transformers format.
A... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech-to-text #da #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | null | Muppet image generator, based on ruDALL-E.
You can perform inference using this [Colab notebook](https://github.com/Norod/my-colab-experiments/blob/master/ruDALLE_muppets_norod78.ipynb)
| {"license": "mit"} | Norod78/ml-generated-muppets-rudalle | null | [
"pytorch",
"license:mit",
"region:us"
] | null | 2022-03-15T08:17:59+00:00 | [] | [] | TAGS
#pytorch #license-mit #region-us
| Muppet image generator, based on ruDALL-E.
You can perform inference using this Colab notebook
!Лягушонок
| [] | [
"TAGS\n#pytorch #license-mit #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. -->
# hebert-finetuned-hebrew-squad
This model fine-tunes avichr/heBERT model on SQuAD dataset auto-translated to Hebrew.
## Intend... | {"language": "he", "tags": ["generated_from_trainer", "avichr/heBERT", "he"], "datasets": ["tdklab/Hebrew_Squad_v1"], "widget": [{"text": "\u05de\u05ea\u05d9 \u05d4\u05d5\u05e7\u05de\u05d4 \u05d4\u05db\u05e8\u05de\u05dc\u05d9\u05ea ?", "context": "\u05db\u05e8\u05de\u05dc\u05d9\u05ea \u05d4\u05d9\u05d0 \u05db\u05dc\u05... | tdklab/hebert-finetuned-hebrew-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"avichr/heBERT",
"he",
"dataset:tdklab/Hebrew_Squad_v1",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-15T09:03:21+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #avichr/heBERT #he #dataset-tdklab/Hebrew_Squad_v1 #endpoints_compatible #has_space #region-us
| hebert-finetuned-hebrew-squad
=============================
This model fine-tunes avichr/heBERT model on SQuAD dataset auto-translated to Hebrew.
Intended uses & limitations
---------------------------
Hebrew SQuAD
Training and evaluation data
----------------------------
Dataset: Hebrew\_Squad\_v1, Split: tr... | [
"# samples: 52,405\nDataset: Hebrew\\_Squad\\_v1, Split: validation, # samples: 7,455\n\n\nTraining procedure\n------------------",
"### 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... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #avichr/heBERT #he #dataset-tdklab/Hebrew_Squad_v1 #endpoints_compatible #has_space #region-us \n",
"# samples: 52,405\nDataset: Hebrew\\_Squad\\_v1, Split: validation, # samples: 7,455\n\n\nTraining procedure\n------------------",
... |
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