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fill-mask | transformers |
# Takalani Sesame - Northern Sotho 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techn... | {"language": ["nso"], "license": "mit", "tags": ["nso", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_nso_roberta | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"nso",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nso"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #nso #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Northern Sotho 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise ... | [
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"## Model description\n\nTakalani Sesame (named after the South African version of Sesame S... |
fill-mask | transformers |
# Takalani Sesame - Southern Sotho 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techn... | {"language": ["sot"], "license": "mit", "tags": ["sot", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_sot_roberta | null | [
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"roberta",
"fill-mask",
"sot",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sot"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #sot #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Southern Sotho 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise ... | [
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"## Model description\n\nTakalani Sesame (named after the South African version of Sesame S... |
fill-mask | transformers |
# Takalani Sesame - Tswana 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques fo... | {"language": ["tn"], "license": "mit", "tags": ["tn", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_ssw_roberta | null | [
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"fill-mask",
"tn",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tn"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #tn #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Tswana 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performa... | [
"# Takalani Sesame - Tswana 🇿🇦\n\n<img src=\"URL width=\"600\"/>",
"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equ... | [
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"# Takalani Sesame - Tswana 🇿🇦\n\n<img src=\"URL width=\"600\"/>",
"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is... |
fill-mask | transformers |
# Takalani Sesame - Tswana 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques fo... | {"language": ["tn"], "license": "mit", "tags": ["tn", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_tsn_roberta | null | [
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"roberta",
"fill-mask",
"tn",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tn"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #tn #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Tswana 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performa... | [
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"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equ... | [
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"# Takalani Sesame - Tswana 🇿🇦\n\n<img src=\"URL width=\"600\"/>",
"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is... |
fill-mask | transformers |
# Takalani Sesame - Tsonga 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques fo... | {"language": ["ts"], "license": "mit", "tags": ["ts", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_tso_roberta | null | [
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"ts",
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"license:mit",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ts"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #ts #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Tsonga 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performa... | [
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"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is... |
fill-mask | transformers |
# Takalani Sesame - Venda 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for... | {"language": ["ven"], "license": "mit", "tags": ["ven", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_ven_roberta | null | [
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"fill-mask",
"ven",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ven"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #ven #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Venda 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performan... | [
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"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equa... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #ven #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Takalani Sesame - Venda 🇿🇦\n\n<img src=\"URL width=\"600\"/>",
"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is... |
fill-mask | transformers |
# Takalani Sesame - Xhosa 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for... | {"language": ["xho"], "license": "mit", "tags": ["xho", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_xho_roberta | null | [
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"fill-mask",
"xho",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"xho"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #xho #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Xhosa 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performan... | [
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"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is... |
fill-mask | transformers |
# Takalani Sesame - Zulu 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for ... | {"language": ["zul"], "license": "mit", "tags": ["zul", "fill-mask", "pytorch", "roberta", "masked-lm"], "thumbnail": "https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg"} | jannesg/takalane_zul_roberta | null | [
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"fill-mask",
"zul",
"masked-lm",
"license:mit",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zul"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #zul #masked-lm #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Takalani Sesame - Zulu 🇿🇦
<img src="URL width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performanc... | [
"# Takalani Sesame - Zulu 🇿🇦\n\n<img src=\"URL width=\"600\"/>",
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"# Takalani Sesame - Zulu 🇿🇦\n\n<img src=\"URL width=\"600\"/>",
"## Model description\n\nTakalani Sesame (named after the South African version of Sesame Street) is ... |
image-classification | transformers |
# dogs
Autogenerated by HuggingPics🤗🖼️
## Example Images
#### golden retriever
#### great dane
#### husky
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | jasmeen/dogs | null | [
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"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# dogs
Autogenerated by HuggingPics️
## Example Images
#### golden retriever
!golden retriever
#### great dane
!great dane
#### husky
!husky | [
"# dogs\n\nAutogenerated by HuggingPics️",
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"## Example Images",
"#### golden retriever\n\n!golden retriever",
"#### great dane\n\n!great dane",
"#### ... |
text-classification | transformers |
# Finetuning
## Result
### Base Model
| | Size | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) | **Korean-Hate-Speech (Dev)**<br/>(F1) |
| :-------------------- ... | {"language": "ko", "datasets": ["kor_hate"], "widget": [{"text": "\uc751 \uc5b4\uca54\ud2f0\ube44~"}]} | jason9693/SoongsilBERT-base-beep | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #ko #dataset-kor_hate #autotrain_compatible #endpoints_compatible #has_space #region-us
| Finetuning
==========
Result
------
### Base Model
### Small Model
Reference
---------
* Transformers Examples
* NSMC
* Naver NER Dataset
* PAWS
* KorNLI/KorSTS
* Question Pair
* KorQuad
* Korean Hate Speech
* KoELECTRA
* KoBERT
* HanBERT
* HanBert Transformers
| [
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text-classification | transformers | # Finetuning
## Result
### Base Model
| | Size | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) | **Korean-Hate-Speech (Dev)**<br/>(F1) |
| :-------------------- |... | {} | jason9693/SoongsilBERT-nsmc-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Finetuning
==========
Result
------
### Base Model
### Small Model
Reference
---------
* Transformers Examples
* NSMC
* Naver NER Dataset
* PAWS
* KorNLI/KorSTS
* Question Pair
* KorQuad
* Korean Hate Speech
* KoELECTRA
* KoBERT
* HanBERT
* HanBert Transformers
| [
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text-generation | transformers |
# Homer Simpson DialoGPT Model | {"tags": ["conversational"]} | jasper/DialoGPT-large-homersimpson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Homer Simpson DialoGPT Model | [
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] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | jatinshah/bert-finetuned-ner | null | [
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"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0599
* Precision: 0.9330
* Recall: 0.9492
* F1: 0.9410
* Accuracy: 0.9862
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | jatinshah/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | jatinshah/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4726
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": []}]} | jatinshah/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8815
- Score: 52.2204
- Counts: [166010, 120787, 91973, 70929]
- Totals: [228361, 207343, 189354, 173335]
- Precisions: [72.696301... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8815\n- Score: 52.2204\n- Counts: [166010, 120787, 91973, 70929]\n- Totals: [228361, 207343, 189354, 173335]\n- Precisions: ... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the k... |
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-xlsr-hindhi-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hindhi-demo-colab", "results": []}]} | jawaharreddy247/wav2vec2-large-xlsr-hindhi-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+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-xlsr-hindhi-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# wav2vec2-large-xlsr-hindhi-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 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 ... | [
"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-xlsr-hindhi-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common... |
text2text-generation | transformers | We pre-trained `t5-large` on SAMSum Dialogue Summarization corpus.
If you use this work for your research, please cite our work [Dialogue Summaries as Dialogue States ({DS}2), Template-Guided Summarization for Few-shot Dialogue State Tracking](https://arxiv.org/abs/2203.01552)
### Citation
```
@inproceedings{shin-eta... | {} | jaynlp/t5-large-samsum | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2203.01552",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.01552"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2203.01552 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| We pre-trained 't5-large' on SAMSum Dialogue Summarization corpus.
If you use this work for your research, please cite our work Dialogue Summaries as Dialogue States ({DS}2), Template-Guided Summarization for Few-shot Dialogue State Tracking
We used the following prompt for training
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2203.01552 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | We reproduced the [TransferQA paper's](https://arxiv.org/abs/2109.04655) QA pre-trained weights.
If you use this work for your research, please cite our work [Dialogue Summaries as Dialogue States ({DS}2), Template-Guided Summarization for Few-shot Dialogue State Tracking](https://arxiv.org/abs/2203.01552)
### Citati... | {} | jaynlp/t5-large-transferqa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2109.04655",
"arxiv:2203.01552",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.04655",
"2203.01552"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2109.04655 #arxiv-2203.01552 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| We reproduced the TransferQA paper's QA pre-trained weights.
If you use this work for your research, please cite our work Dialogue Summaries as Dialogue States ({DS}2), Template-Guided Summarization for Few-shot Dialogue State Tracking
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2109.04655 #arxiv-2203.01552 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | # bert-base-multilingual-cased-language-detection
A model for language detection with support for 45 languages
## Model description
This model was created by fine-tuning
[bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [common language](https://huggingface.co/datasets/common_l... | {} | jb2k/bert-base-multilingual-cased-language-detection | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # bert-base-multilingual-cased-language-detection
A model for language detection with support for 45 languages
## Model description
This model was created by fine-tuning
bert-base-multilingual-cased on the common language dataset.
This dataset has support for 45 languages, which are listed below:
## Evaluation
This ... | [
"# bert-base-multilingual-cased-language-detection\nA model for language detection with support for 45 languages",
"## Model description\nThis model was created by fine-tuning \nbert-base-multilingual-cased on the common language dataset.\nThis dataset has support for 45 languages, which are listed below:",
"##... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bert-base-multilingual-cased-language-detection\nA model for language detection with support for 45 languages",
"## Model description\nThis model was created by fine-tuning \nbert-b... |
text-generation | transformers | This model was trained on the OSCAR ga dataset for experimental purposes. The files used for training the tokenizer and model are included in this repository. | {} | jbarry/irish-gpt2 | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model was trained on the OSCAR ga dataset for experimental purposes. The files used for training the tokenizer and model are included in this repository. | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody.
This model was created by fine-tuning the `facebook/wav2vec2-large-robust-ft-libri-960h` checkpoint... | {} | jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
| This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody.
This model was created by fine-tuning the 'facebook/wav2vec2-large-robust-ft-libri-960h' checkpoint... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #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. -->
# sentence_similarity_concierge
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sentence_similarity_concierge", "results": []}]} | jcai1/sentence_similarity_concierge | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sentence\_similarity\_concierge
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1165
* Accuracy: 0.9748
* F1: 0.9680
Model description
-----------------
More information needed
Int... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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. -->
# ss_mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "ss_mrpc", "results": []}]} | jcai1/ss_mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ss\_mrpc
========
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5960
* Accuracy: 0.8799
* F1: 0.9148
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
fill-mask | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-lar... | {"language": "tl", "license": "gpl-3.0", "tags": ["bert", "tagalog", "filipino"], "inference": false} | jcblaise/bert-tagalog-base-cased-WWM | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# BERT Tagalog Base Cased (Whole Word Masking)
Tagalog version of BERT... | [
"# BERT Tagalog Base Cased (Whole Word Masking)\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This particular version us... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us \n",
"# BERT Tagalog Base Cased (Whole Word Masking)\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a la... |
fill-mask | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-l... | {"language": "tl", "license": "gpl-3.0", "tags": ["bert", "tagalog", "filipino"], "inference": false} | jcblaise/bert-tagalog-base-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #has_space #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# BERT Tagalog Base Cased
Tagalog version of BERT trained on a lar... | [
"# BERT Tagalog Base Cased\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\r\n\r\ns\r\nAll model details and training setu... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #has_space #region-us \n",
"# BERT Tagalog Base Cased\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger resea... |
fill-mask | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-lar... | {"language": "tl", "license": "gpl-3.0", "tags": ["bert", "tagalog", "filipino"], "inference": false} | jcblaise/bert-tagalog-base-uncased-WWM | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# BERT Tagalog Base Uncased (Whole Word Masking)
Tagalog version of BE... | [
"# BERT Tagalog Base Uncased (Whole Word Masking)\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This particular version ... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us \n",
"# BERT Tagalog Base Uncased (Whole Word Masking)\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a ... |
fill-mask | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-lar... | {"language": "tl", "license": "gpl-3.0", "tags": ["bert", "tagalog", "filipino"], "inference": false} | jcblaise/bert-tagalog-base-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# BERT Tagalog Base Uncased
Tagalog version of BERT trained on a large... | [
"# BERT Tagalog Base Uncased\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\r\n\r\ns\r\nAll model details and training se... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us \n",
"# BERT Tagalog Base Uncased\r\nTagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research proje... |
null | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-lar... | {"language": "tl", "license": "gpl-3.0", "tags": ["distilbert", "bert", "tagalog", "filipino"], "inference": false} | jcblaise/distilbert-tagalog-base-cased | null | [
"transformers",
"pytorch",
"jax",
"distilbert",
"bert",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #jax #distilbert #bert #tagalog #filipino #tl #license-gpl-3.0 #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# DistilBERT Tagalog Base Cased
Tagalog version of DistilBERT, distill... | [
"# DistilBERT Tagalog Base Cased\r\nTagalog version of DistilBERT, distilled from 'bert-tagalog-base-cased'. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.",
"## Usage\r\nThe model can be loaded and used in both PyTorch and Tenso... | [
"TAGS\n#transformers #pytorch #jax #distilbert #bert #tagalog #filipino #tl #license-gpl-3.0 #region-us \n",
"# DistilBERT Tagalog Base Cased\r\nTagalog version of DistilBERT, distilled from 'bert-tagalog-base-cased'. This model is part of a larger research project. We open-source the model to allow greater usage... |
null | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-large)... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-base-cased-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# ELECTRA Tagalog Base Cased Discriminator
Tagalog ELECTRA model pretrained wi... | [
"# ELECTRA Tagalog Base Cased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the discriminator model, which is the main Trans... | [
"TAGS\n#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us \n",
"# ELECTRA Tagalog Base Cased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allo... |
fill-mask | transformers |
# ELECTRA Tagalog Base Cased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pretr... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-base-cased-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us
|
# ELECTRA Tagalog Base Cased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pretr... | [
"# ELECTRA Tagalog Base Cased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the generator model used to sample synthetic text an... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us \n",
"# ELECTRA Tagalog Base Cased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source t... |
null | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-large)... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-base-uncased-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# ELECTRA Tagalog Base Uncased Discriminator
Tagalog ELECTRA model pretrained ... | [
"# ELECTRA Tagalog Base Uncased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the discriminator model, which is the main Tra... | [
"TAGS\n#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us \n",
"# ELECTRA Tagalog Base Uncased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to al... |
fill-mask | transformers |
# ELECTRA Tagalog Base Uncased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pre... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-base-uncased-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us
|
# ELECTRA Tagalog Base Uncased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pre... | [
"# ELECTRA Tagalog Base Uncased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the generator model used to sample synthetic text ... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us \n",
"# ELECTRA Tagalog Base Uncased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source... |
null | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-large)... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-small-cased-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# ELECTRA Tagalog Small Cased Discriminator
Tagalog ELECTRA model pretrained w... | [
"# ELECTRA Tagalog Small Cased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the discriminator model, which is the main Tran... | [
"TAGS\n#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us \n",
"# ELECTRA Tagalog Small Cased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to all... |
fill-mask | transformers |
# ELECTRA Tagalog Small Cased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pret... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-small-cased-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us
|
# ELECTRA Tagalog Small Cased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pret... | [
"# ELECTRA Tagalog Small Cased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the generator model used to sample synthetic text a... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us \n",
"# ELECTRA Tagalog Small Cased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source ... |
null | transformers |
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-large)... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-small-uncased-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us
|
Deprecation Notice
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use 'jcblaise/roberta-tagalog-base' or 'jcblaise/roberta-tagalog-large' instead for better performance.
---
# ELECTRA Tagalog Small Uncased Discriminator
Tagalog ELECTRA model pretrained... | [
"# ELECTRA Tagalog Small Uncased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the discriminator model, which is the main Tr... | [
"TAGS\n#transformers #pytorch #electra #pretraining #tagalog #filipino #tl #license-gpl-3.0 #region-us \n",
"# ELECTRA Tagalog Small Uncased Discriminator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to a... |
fill-mask | transformers |
# ELECTRA Tagalog Small Uncased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pr... | {"language": "tl", "license": "gpl-3.0", "tags": ["electra", "tagalog", "filipino"], "inference": false} | jcblaise/electra-tagalog-small-uncased-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us
|
# ELECTRA Tagalog Small Uncased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pr... | [
"# ELECTRA Tagalog Small Uncased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.\n\nThis is the generator model used to sample synthetic text... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #region-us \n",
"# ELECTRA Tagalog Small Uncased Generator\nTagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-sourc... |
text-generation | transformers |
# GPT-2 Tagalog
The Tagalog GPT-2 model used to benchmark our fake news detection system Cruz et al. (2020). We make available an improved version of our GPT-2 model trained with NewsPH in addition to WikiText-TL-39.
## Limitations and Bias
The model was trained with two language modeling datasets for Tagalog:
* **Wi... | {"language": "tl", "license": "gpl-3.0", "tags": ["gpt2", "tagalog", "filipino"], "inference": false} | jcblaise/gpt2-tagalog | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"tagalog",
"filipino",
"tl",
"license:gpl-3.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #has_space #text-generation-inference #region-us
|
# GPT-2 Tagalog
The Tagalog GPT-2 model used to benchmark our fake news detection system Cruz et al. (2020). We make available an improved version of our GPT-2 model trained with NewsPH in addition to WikiText-TL-39.
## Limitations and Bias
The model was trained with two language modeling datasets for Tagalog:
* Wiki... | [
"# GPT-2 Tagalog\nThe Tagalog GPT-2 model used to benchmark our fake news detection system Cruz et al. (2020). We make available an improved version of our GPT-2 model trained with NewsPH in addition to WikiText-TL-39.",
"## Limitations and Bias\nThe model was trained with two language modeling datasets for Tagal... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #tagalog #filipino #tl #license-gpl-3.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# GPT-2 Tagalog\nThe Tagalog GPT-2 model used to benchmark our fake news detection system Cruz et al. (2020). We make available an impr... |
fill-mask | transformers |
# RoBERTa Tagalog Base
Tagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage within the... | {"language": "tl", "license": "cc-by-sa-4.0", "tags": ["roberta", "tagalog", "filipino"], "inference": false} | jcblaise/roberta-tagalog-base | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #tagalog #filipino #tl #license-cc-by-sa-4.0 #autotrain_compatible #has_space #region-us
|
# RoBERTa Tagalog Base
Tagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage within the... | [
"# RoBERTa Tagalog Base\nTagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage withi... | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #tagalog #filipino #tl #license-cc-by-sa-4.0 #autotrain_compatible #has_space #region-us \n",
"# RoBERTa Tagalog Base\nTagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more to... |
fill-mask | transformers |
# RoBERTa Tagalog Large
Tagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage within th... | {"language": "tl", "license": "cc-by-sa-4.0", "tags": ["roberta", "tagalog", "filipino"], "inference": false} | jcblaise/roberta-tagalog-large | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"tagalog",
"filipino",
"tl",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #tagalog #filipino #tl #license-cc-by-sa-4.0 #autotrain_compatible #has_space #region-us
|
# RoBERTa Tagalog Large
Tagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage within th... | [
"# RoBERTa Tagalog Large\nTagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage with... | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #tagalog #filipino #tl #license-cc-by-sa-4.0 #autotrain_compatible #has_space #region-us \n",
"# RoBERTa Tagalog Large\nTagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more t... |
text-generation | transformers |
# Evan Model | {"tags": ["conversational"]} | jchen/DialoGPT-evan | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Evan Model | [
"# Evan Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Evan Model"
] |
null | speechbrain |
Speechbrain SLU fine-tuned for Intent Classification
---
Model: Direct SLU
Encoder: Pre-trained ASR encoder -> LSTM
Decoder: GRU + beamsearch
Tokens: BPE with unigram
Data: fluent_speech_commands_dataset (http://140.112.21.28:9000/fluent.tar.gz)
Test wer: 0.07 | {"library_name": "speechbrain", "tags": ["audio", "intent classification"], "datasets": ["fluent_speech_commands_dataset"], "metrics": ["wer"]} | jcmc/speechbrain-ic-slu | null | [
"speechbrain",
"audio",
"intent classification",
"dataset:fluent_speech_commands_dataset",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#speechbrain #audio #intent classification #dataset-fluent_speech_commands_dataset #model-index #region-us
|
Speechbrain SLU fine-tuned for Intent Classification
---
Model: Direct SLU
Encoder: Pre-trained ASR encoder -> LSTM
Decoder: GRU + beamsearch
Tokens: BPE with unigram
Data: fluent_speech_commands_dataset (http://140.112.21.28:9000/URL)
Test wer: 0.07 | [] | [
"TAGS\n#speechbrain #audio #intent classification #dataset-fluent_speech_commands_dataset #model-index #region-us \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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["ga-IE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | jcmc/wav2vec-1b-cv8-ir-n | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga-IE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9810
* Wer: 0.4761
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
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 is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["ga-IE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ga-IE", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec-1b-cv8-ir", "results": ... | jcmc/wav2vec-1b-cv8-ir | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ga-IE",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compati... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga-IE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ga-IE #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8445
* Wer: 0.5585
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ga-IE #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyper... |
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 is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["ga-IE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ga-IE", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec-cv7-1b-ir", "results": ... | jcmc/wav2vec-cv7-1b-ir | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ga-IE",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga-IE"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ga-IE #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9562
* Wer: 0.4801
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ga-IE #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
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 is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)... | {"language": ["ga-IE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | jcmc/wav2vec2-large-xlsr-53-ir | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga-IE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0835
* Wer: 0.7490
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-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... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
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 is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["ga-IE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | jcmc/wav2vec2-xls-r-1b-ir | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga-IE"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6569
* Wer: 0.8623
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | jcsilva/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7665
* Wer: 0.6956
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
fill-mask | transformers | # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in [this paper](https://arxiv.org/abs/1810.04805). This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example ... | {} | jean-paul/KinyaBERT-large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us
| # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in this paper. This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda.
# T... | [
"# Model description\n\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in this paper. This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model description\n\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in this paper. This Kiny... |
fill-mask | transformers | # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in [this paper](https://arxiv.org/abs/1810.04805). This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example i... | {} | jean-paul/KinyaBERT-small | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us
| # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in this paper. This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda.
# Tra... | [
"# Model description\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in this paper. This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda."... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model description\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in this paper. This KinyaB... |
fill-mask | transformers | # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in [this paper](https://arxiv.org/abs/1907.11692). This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example... | {} | jean-paul/kinyaRoberta-large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in this paper. This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda.
# ... | [
"# Model description\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in this paper. This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model description\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in this paper. This Kiny... |
fill-mask | transformers | # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in [this paper](https://arxiv.org/abs/1907.11692). This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example... | {} | jean-paul/kinyaRoberta-small | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| # Model description
A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in this paper. This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda.
# ... | [
"# Model description\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in this paper. This KinyaRoBERTa model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model description\nA Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. RoBerta model was first introduced in this paper. This Kiny... |
text-classification | transformers | This model is a demonstration of the [Ernie library] (https://github.com/labteral/ernie) for fine-tuning sentence classification models. The base model is bert-base-uncased. The dataset for fine-tuning consists of two labeled example sentences. Because this is a toy example, we do not recommend it for anything other th... | {} | jeang/bert-finetuned-sentence-classification-toy | null | [
"transformers",
"tf",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model is a demonstration of the [Ernie library] (URL for fine-tuning sentence classification models. The base model is bert-base-uncased. The dataset for fine-tuning consists of two labeled example sentences. Because this is a toy example, we do not recommend it for anything other than for demonstrating the Ernie ... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
#Chatbot gay | {"tags": ["conversational"]} | jeanlks/DialogGPT-small-gayvid | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Chatbot gay | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Chatbot pato | {"tags": ["conversational"]} | jeanlks/DialogGPT-small-pato | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Chatbot pato | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# vision-transformers--spain-or-italy-fan
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https:/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | jeffboudier/vision-transformers-spain-or-italy-fan | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vision-transformers--spain-or-italy-fan
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### italy soccer fan
!italy soccer fan
#### spain soccer fan
!spain soccer fan | [
"# vision-transformers--spain-or-italy-fan\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### italy soccer fan\n\n!italy soccer fan",
"#### spain soccer fan... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vision-transformers--spain-or-italy-fan\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the dem... |
sentence-similarity | sentence-transformers |
# bert-base-dutch-cased-snli
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 beco... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jegormeister/bert-base-dutch-cased-snli | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# bert-base-dutch-cased-snli
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:
... | [
"# bert-base-dutch-cased-snli\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 inst... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# bert-base-dutch-cased-snli\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 cl... |
sentence-similarity | sentence-transformers |
# bert-base-dutch-cased-snli
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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 beco... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jegormeister/bert-base-dutch-cased | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# bert-base-dutch-cased-snli
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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:
... | [
"# bert-base-dutch-cased-snli\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 inst... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# bert-base-dutch-cased-snli\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like cl... |
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 is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["ta"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | jejomi/xls-r-ta | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ta",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ta"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ta #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - TA dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# \n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - TA dataset.\nIt achieves the following results on the evaluation set:\n- Loss: inf\n- Wer: 1.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ta #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION... |
token-classification | transformers |
🦔 HEDGEhog 🦔: BERT-based multi-class uncertainty cues recognition
====================================================================
# Description
A fine-tuned multi-class classification model that detects four different types of uncertainty cues (a.k.a hedges) on a token level.
# Uncertainty types
label | type ... | {"language": "en", "license": "mit", "inference": false} | jeniakim/hedgehog | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #has_space #region-us
| HEDGEhog : BERT-based multi-class uncertainty cues recognition
==============================================================
Description
===========
A fine-tuned multi-class classification model that detects four different types of uncertainty cues (a.k.a hedges) on a token level.
Uncertainty types
=============... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: [Code and Named Entity Recognition in StackOverflow](https://www.aclweb.org/anthology/2020.acl-main.443/). We would lik... | {} | jeniya/BERTOverflow | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
|
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and Named Entity Recognition in StackOverflow. We would like to thank Wuwei Lan for helping us in training this mo... | [
"# BERTOverflow",
"## Model description\n\nWe pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and Named Entity Recognition in StackOverflow. We would like to thank Wuwei Lan for helping us in trainin... | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n",
"# BERTOverflow",
"## Model description\n\nWe pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and ... |
feature-extraction | transformers |
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: [Code and Named Entity Recognition in StackOverflow](https://www.aclweb.org/anthology/2020.acl-main.443/). We would lik... | {} | jeniya/BERTOverflow_stackoverflow_github | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
|
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and Named Entity Recognition in StackOverflow. We would like to thank Wuwei Lan for helping us in training this mo... | [
"# BERTOverflow",
"## Model description\n\nWe pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and Named Entity Recognition in StackOverflow. We would like to thank Wuwei Lan for helping us in trainin... | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n",
"# BERTOverflow",
"## Model description\n\nWe pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: Code and ... |
text2text-generation | transformers | training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1, # total number of training epochs
per_device_train_batch_size=8, # batch size per device during training
#per_device_eval_batch_size=2, # batch size for evaluation
warmup_steps=... | {} | jenspt/byt5_ft_all_clean_data | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1, # total number of training epochs
per_device_train_batch_size=8, # batch size per device during training
#per_device_eval_batch_size=2, # batch size for evaluation
warmup_steps=... | [
"# output directory\n num_train_epochs=1, # total number of training epochs\n per_device_train_batch_size=8, # batch size per device during training\n #per_device_eval_batch_size=2, # batch size for evaluation\n warmup_steps=500, # number of warmup steps for learning rate ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# output directory\n num_train_epochs=1, # total number of training epochs\n per_device_train_batch_size=8, # batch size per device during training\n ... |
text2text-generation | transformers | training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1, # total number of training epochs
per_device_train_batch_size=8, # batch size per device during training
#per_device_eval_batch_size=2, # batch size for evaluation
warmup_steps=... | {} | jenspt/byt5_ft_all_clean_data_lr_1e4 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1, # total number of training epochs
per_device_train_batch_size=8, # batch size per device during training
#per_device_eval_batch_size=2, # batch size for evaluation
warmup_steps=... | [
"# output directory\n num_train_epochs=1, # total number of training epochs\n per_device_train_batch_size=8, # batch size per device during training\n #per_device_eval_batch_size=2, # batch size for evaluation\n warmup_steps=3000, # number of warmup steps for learning rate... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# output directory\n num_train_epochs=1, # total number of training epochs\n per_device_train_batch_size=8, # batch size per device during training\n ... |
text2text-generation | transformers | training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1, # total number of training epochs
per_device_train_batch_size=8, # batch size per device during training
#per_device_eval_batch_size=2, # batch size for evaluation
warmup_steps=... | {} | jenspt/byt5_ft_all_clean_data_ws3000 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1, # total number of training epochs
per_device_train_batch_size=8, # batch size per device during training
#per_device_eval_batch_size=2, # batch size for evaluation
warmup_steps=... | [
"# output directory\n num_train_epochs=1, # total number of training epochs\n per_device_train_batch_size=8, # batch size per device during training\n #per_device_eval_batch_size=2, # batch size for evaluation\n warmup_steps=3000, # number of warmup steps for learning rate... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# output directory\n num_train_epochs=1, # total number of training epochs\n per_device_train_batch_size=8, # batch size per device during training\n ... |
null | null | https://natureecoevocommunity.nature.com/users/123movies-hd-watch-hitman-s-wife-s-bodyguard-2021-full-movie-online
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-a-quiet-place-part-2-2021-full-movie-online-free-1d44a4a0-bbe0-4b52-a56c-c86e7ce72c1c
https://natureecoevocommunity.nature.com/users/123mov... | {} | jephthah/dfjgidfhj | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | jery33/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7637
* Matthews Correlation: 0.5373
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_all-distilroberta-v1_100_Epochs | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #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 #roberta #feature-extraction #sentence-similarity #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 or semantic sear... |
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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_all-distilroberta-v1_10_Epochs | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #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 #roberta #feature-extraction #sentence-similarity #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 or semantic sear... |
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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_all-distilroberta-v1_1_Epochs | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #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 #roberta #feature-extraction #sentence-similarity #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 or semantic sear... |
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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_all-distilroberta-v1_30_Epochs | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #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 #roberta #feature-extraction #sentence-similarity #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 or semantic sear... |
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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_all-distilroberta-v1_50_Epochs | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #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 #roberta #feature-extraction #sentence-similarity #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 or semantic sear... |
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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_all-distilroberta-v1_5_Epochs | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #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 #roberta #feature-extraction #sentence-similarity #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 or semantic sear... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_bert-base-multilingual-uncased_100_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_bert-base-multilingual-uncased_10_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_bert-base-multilingual-uncased_1_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_bert-base-multilingual-uncased_30_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_bert-base-multilingual-uncased_50_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_bert-base-multilingual-uncased_5_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_100_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_10_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_1_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_30_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_50_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_5_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 256 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 256 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 #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_distiluse-base-multilingual-cased-v1_100_Epochs | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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 512 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 #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_distiluse-base-multilingual-cased-v1_10_Epochs | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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 512 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 #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_distiluse-base-multilingual-cased-v1_1_Epochs | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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 512 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 #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_distiluse-base-multilingual-cased-v1_30_Epochs | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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 512 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 #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_distiluse-base-multilingual-cased-v1_50_Epochs | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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 512 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 #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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"], "pipeline_tag": "sentence-similarity"} | jfarray/Model_distiluse-base-multilingual-cased-v1_5_Epochs | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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 512 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 #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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"} | jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_100_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 384 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 #bert #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 384 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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"} | jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_10_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 384 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 #bert #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 384 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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"} | jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_1_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 384 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 #bert #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 384 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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"} | jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_30_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 384 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 #bert #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 384 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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"} | jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_50_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 384 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 #bert #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 384 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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"} | jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_5_Epochs | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 384 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 #bert #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 384 dimensional dense vector space and can be used for tasks like clustering or se... |
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"} | jfarray/Model_paraphrase-multilingual-mpnet-base-v2_100_Epochs | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #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 #xlm-roberta #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 clusterin... |
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"} | jfarray/Model_paraphrase-multilingual-mpnet-base-v2_10_Epochs | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #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 #xlm-roberta #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 clusterin... |
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"} | jfarray/Model_paraphrase-multilingual-mpnet-base-v2_1_Epochs | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #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 #xlm-roberta #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 clusterin... |
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