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fill-mask | transformers |
# roberta-large-japanese-aozora
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-large-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYas... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/roberta-large-japanese-aozora | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-large-japanese-aozora
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune 'roberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
## Reference
安岡孝一: Transformersと国語研長単位による日... | [
"# roberta-large-japanese-aozora",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune 'roberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use",
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"# roberta-large-japanese-aozora",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tun... | [
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token-classification | transformers |
# roberta-large-japanese-char-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-large-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora-char). Every long-unit-word is tagged by [UPOS](ht... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/roberta-large-japanese-char-luw-upos | null | [
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"dataset:universal_dependencies",
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|
# roberta-large-japanese-char-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-large-japanese-aozora-char. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
## Reference
安岡孝... | [
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"## How to Use\n\n\n\nor",
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token-classification | transformers |
# roberta-large-japanese-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universal... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/roberta-large-japanese-luw-upos | null | [
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|
# roberta-large-japanese-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-large-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## Reference
安岡孝一: Transformersと国語研長... | [
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fill-mask | transformers |
# roberta-small-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune `roberta-small-japanese-aozora-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-char-luw-upos), dependen... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/roberta-small-japanese-aozora-char | null | [
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"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
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#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-small-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune 'roberta-small-japanese-aozora-char' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# roberta-small-japanese-aozora-char",
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"## How to Use"
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fill-mask | transformers |
# roberta-small-japanese-aozora
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-small-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYas... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/roberta-small-japanese-aozora | null | [
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"roberta",
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"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-small-japanese-aozora
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune 'roberta-small-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# roberta-small-japanese-aozora",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts with Japanese-LUW-Tokenizer. You can fine-tune 'roberta-small-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use"
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token-classification | transformers |
# roberta-small-japanese-char-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-small-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-aozora-char). Every long-unit-word is tagged by [UPOS](ht... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/roberta-small-japanese-char-luw-upos | null | [
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"roberta",
"token-classification",
"japanese",
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"dependency-parsing",
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"dataset:universal_dependencies",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
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|
# roberta-small-japanese-char-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-small-japanese-aozora-char. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokeni... | [
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"## How to Use\n\n\n\nor",
"## See ... | [
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token-classification | transformers |
# roberta-small-japanese-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-small-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universal... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/roberta-small-japanese-luw-upos | null | [
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"roberta",
"token-classification",
"japanese",
"pos",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
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|
# roberta-small-japanese-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-small-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-ta... | [
"# roberta-small-japanese-luw-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from roberta-small-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
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"# roberta-small-japanese-luw-upos",
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token-classification | transformers |
# xlm-roberta-base-english-upos
## Model Description
This is an XLM-RoBERTa model pre-trained with [UD_English-EWT](https://github.com/UniversalDependencies/UD_English-EWT) for POS-tagging and dependency-parsing, derived from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). Every word is tagged by [UPOS]... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["english", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/xlm-roberta-base-english-upos | null | [
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"xlm-roberta",
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"english",
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"dependency-parsing",
"en",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
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|
# xlm-roberta-base-english-upos
## Model Description
This is an XLM-RoBERTa model pre-trained with UD_English-EWT for POS-tagging and dependency-parsing, derived from xlm-roberta-base. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Depen... | [
"# xlm-roberta-base-english-upos",
"## Model Description\n\nThis is an XLM-RoBERTa model pre-trained with UD_English-EWT for POS-tagging and dependency-parsing, derived from xlm-roberta-base. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenize... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #english #pos #dependency-parsing #en #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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text-generation | null | #Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Konggate/DialoGPT-small-harrypotter | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
| #Harry Potter DialoGPT Model | [] | [
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fill-mask | transformers |
# Α lite RoBERTa fill mask model trained mostly in greek tweets
The training dataset of this model consists of 23 million tweets in Greek, of approximately 5000 users in total, spanning from 2008 to 2018.
The model has been trained to support the work for the paper [Multimodal Hate Speech Detection in Greek Social... | {"language": "el", "widget": [{"text": "\u03bc\u03c0\u03b1\u03b9\u03bd\u03c9 \u03c3\u03c4\u03bf <mask> \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03bd\u03b1 \u03b4\u03c9."}]} | Konstantinos/BERTaTweetGR | null | [
"transformers",
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"jax",
"roberta",
"fill-mask",
"el",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #el #autotrain_compatible #endpoints_compatible #region-us
|
# Α lite RoBERTa fill mask model trained mostly in greek tweets
The training dataset of this model consists of 23 million tweets in Greek, of approximately 5000 users in total, spanning from 2008 to 2018.
The model has been trained to support the work for the paper Multimodal Hate Speech Detection in Greek Social ... | [
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null | null | from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
model = AutoModelForCausalLM.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") | {} | Kookly/Kooklybots | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
model = AutoModelForCausalLM.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") | [] | [
"TAGS\n#region-us \n"
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"TAGS\n#region-us \n"
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text-generation | transformers |
I'm dumb | {"tags": ["conversational"]} | Koriyy/DialoGPT-medium-gf | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
I'm dumb | [] | [
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text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | Koro/DialoGPT-medium-rickandmorty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
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text-generation | null |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | Koro/DialoGPT-small-rickandmorty | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Rick and Morty DialoGPT Model | [
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fill-mask | transformers | # Bangla BERT Base
Here we published a pretrained Bangla bert language model as **bangla-bert**! which is now available in huggingface model hub.
Here we described [bangla-bert](https://github.com/Kowsher/bert-base-bangla) which is a pretrained Bangla language model based on mask language modeling described in [BERT](... | {"language": "bn", "tags": ["Bert base Bangla", "Bengali Bert", "Bengali lm", "Bangla Base Bert", "Bangla Bert language model", "Bangla Bert"], "datasets": ["BanglaLM dataset"]} | Kowsher/bangla-bert | null | [
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"Bert base Bangla",
"Bengali Bert",
"Bengali lm",
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"Bangla Bert language model",
"Bangla Bert",
"bn",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1810.04805"
] | [
"bn"
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#transformers #pytorch #bert #fill-mask #Bert base Bangla #Bengali Bert #Bengali lm #Bangla Base Bert #Bangla Bert language model #Bangla Bert #bn #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us
| # Bangla BERT Base
Here we published a pretrained Bangla bert language model as bangla-bert! which is now available in huggingface model hub.
Here we described bangla-bert which is a pretrained Bangla language model based on mask language modeling described in BERT and the GitHub repository
## Corpus Details
We trai... | [
"# Bangla BERT Base\nHere we published a pretrained Bangla bert language model as bangla-bert! which is now available in huggingface model hub. \nHere we described bangla-bert which is a pretrained Bangla language model based on mask language modeling described in BERT and the GitHub repository",
"## Corpus Det... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | Krassy/xlm-roberta-base-finetuned-marc-en | null | [
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"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9005
* Mae: 0.5
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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text-generation | transformers |
# Santa Chatbot | {"tags": ["conversational"]} | KringleClaus/Dialog-santa | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Santa Chatbot | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-plot
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
It achi... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-plot", "results": []}]} | KrishParikh/gpt2_imdb_movie_plots | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-plot
This model is a fine-tuned version of gpt2-medium on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information need... | [
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null | null | ---
tags:
- conversational
--- | {} | KrishnaChandra4/DialoGPT-small-Rick | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| ---
tags:
- conversational
--- | [] | [
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text-generation | transformers |
# Harry Potter DialoGPTModel | {"tags": ["conversational"]} | KrispyIChris/DialoGPT-small-harrypotter | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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text-generation | transformers | # Buro discord bot | {"tags": ["conversational"]} | Kryptone/Burobot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Buro discord bot | [
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text-generation | transformers | # Rin chatbot | {"tags": ["conversational"]} | Kryptone/RinAI | null | [
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"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Rin chatbot | [
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text-generation | transformers |
# MoniKA unstable | {"tags": ["conversational"]} | Kryptone/monikAI-Unstable | null | [
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"gpt2",
"text-generation",
"conversational",
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"text-generation-inference",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
# MoniKA unstable | [
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text-generation | transformers | # Monika Discord Chatbot | {"tags": ["conversational"]} | Kryptone/monikAI | null | [
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"text-generation",
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"text-generation-inference",
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] |
text2text-generation | transformers | ## mDialBART: A Cross-Lingual Dialogue Summarization Model
This model is introduced by [*ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization*](https://arxiv.org/abs/2202.05599). | {"license": "cc-by-nc-sa-4.0"} | Krystalan/mdialbart_de | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2202.05599",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2202.05599"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2202.05599 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ## mDialBART: A Cross-Lingual Dialogue Summarization Model
This model is introduced by *ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization*. | [
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text2text-generation | transformers | ## mDialBART: A Cross-Lingual Dialogue Summarization Model
This model is introduced by [*ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization*](https://arxiv.org/abs/2202.05599). | {"license": "cc-by-nc-sa-4.0"} | Krystalan/mdialbart_zh | null | [
"transformers",
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"mbart",
"text2text-generation",
"arxiv:2202.05599",
"license:cc-by-nc-sa-4.0",
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] | null | 2022-03-02T23:29:04+00:00 | [
"2202.05599"
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#transformers #pytorch #mbart #text2text-generation #arxiv-2202.05599 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ## mDialBART: A Cross-Lingual Dialogue Summarization Model
This model is introduced by *ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization*. | [
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text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | Kshaunish/DialoGPT-small-rick | null | [
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"gpt2",
"text-generation",
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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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... | Kumicho/distilbert-base-uncased-finetuned-cola | null | [
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#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: 0.7758
* Matthews Correlation: 0.5259
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: 1",
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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. -->
# librispeech-100h-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "librispeech-100h-supervised", "results": []}]} | Kuray107/librispeech-100h-supervised | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| librispeech-100h-supervised
===========================
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0955
* Wer: 0.0345
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: 24\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... | [
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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. -->
# timit-5percent-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "timit-5percent-supervised", "results": []}]} | Kuray107/timit-5percent-supervised | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| timit-5percent-supervised
=========================
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6615
* Wer: 0.2788
Model description
-----------------
More information needed
Intended uses & limitati... | [
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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. -->
# timit-supervised
This model is a fine-tuned version of [Experiments/single_dataset/timit-supervised/checkpoint-3500](https://hug... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "timit-supervised", "results": []}]} | Kuray107/timit-supervised | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| timit-supervised
================
This model is a fine-tuned version of Experiments/single\_dataset/timit-supervised/checkpoint-3500 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1272
* Wer: 0.0532
Model description
-----------------
More information needed
Intended u... | [
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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. -->
# wsj0-full-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wsj0-full-supervised", "results": []}]} | Kuray107/wsj0-full-supervised | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wsj0-full-supervised
====================
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0623
* Wer: 0.0343
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Kush/DialoGPT-small-harrypotter | null | [
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feature-extraction | transformers | This is **KOREAN** Bert Masked LM pretrained model adapted in **BEAUTY** domain. (BertForMaskedLM)
About 60,000 reviews were used.
It was fine-tuned based on _beomi/kcbert-base_ model weights.
Enjoy! | {} | Kyoungmin/beauty-base-KLCP | null | [
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"bert",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| This is KOREAN Bert Masked LM pretrained model adapted in BEAUTY domain. (BertForMaskedLM)
About 60,000 reviews were used.
It was fine-tuned based on _beomi/kcbert-base_ model weights.
Enjoy! | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
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23
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fill-mask | transformers | **Second** BertForMaskedLM pretrained model in **KOREAN Beauty** domain.
About 120,000 reviews were used.
It was trained based on _beomi/kcbert-base_ .
Check out _Kyoungmin/beauty-base-KLCP_ for smaller model !! | {} | Kyoungmin/beauty-base-KLCP2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Second BertForMaskedLM pretrained model in KOREAN Beauty domain.
About 120,000 reviews were used.
It was trained based on _beomi/kcbert-base_ .
Check out _Kyoungmin/beauty-base-KLCP_ for smaller model !! | [] | [
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null | null | No use | {} | Kyoungmin/beauty-word2vec | null | [
"region:us"
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#region-us
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fill-mask | transformers | This is practice model for kcbert-base with Korean petition data! | {} | Kyoungmin/kcbert-base-petition | null | [
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"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This is practice model for kcbert-base with Korean petition data! | [] | [
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text-generation | transformers |
#VADER DialogGPT Model | {"tags": ["conversational"]} | LARACHNIDE/DialogGPT-small-sw | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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#VADER DialogGPT Model | [] | [
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multiple-choice | transformers |
# Roberta Large Fine Tuned on RACE
## Model description
This model follows the implementation by Allen AI team about [Aristo Roberta V7 Model](https://leaderboard.allenai.org/arc/submission/blcotvl7rrltlue6bsv0) given in [ARC Challenge](https://leaderboard.allenai.org/arc/submissions/public)
#### How to use
```pyt... | {"language": "english", "license": "mit", "datasets": ["race", "ai2_arc", "openbookqa"], "metrics": ["accuracy"]} | LIAMF-USP/aristo-roberta | null | [
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"english"
] | TAGS
#transformers #pytorch #tf #jax #roberta #multiple-choice #dataset-race #dataset-ai2_arc #dataset-openbookqa #license-mit #endpoints_compatible #region-us
| Roberta Large Fine Tuned on RACE
================================
Model description
-----------------
This model follows the implementation by Allen AI team about Aristo Roberta V7 Model given in ARC Challenge
#### How to use
Training data
-------------
the Training data was the same as proposed here
The on... | [
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multiple-choice | transformers |
# Roberta Large Fine Tuned on RACE
## Model description
This model is a fine-tuned model of Roberta-large applied on RACE
#### How to use
```python
import datasets
from transformers import RobertaTokenizer
from transformers import RobertaForMultipleChoice
tokenizer = RobertaTokenizer.from_pretrained(
"LIAMF-USP... | {"language": "english", "license": "mit", "datasets": ["race"], "metrics": ["accuracy"]} | LIAMF-USP/roberta-large-finetuned-race | null | [
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#transformers #pytorch #tf #jax #roberta #multiple-choice #dataset-race #license-mit #endpoints_compatible #region-us
| Roberta Large Fine Tuned on RACE
================================
Model description
-----------------
This model is a fine-tuned model of Roberta-large applied on RACE
#### How to use
Training data
-------------
The initial model was roberta large model which was then fine-tuned on RACE dataset
Training pro... | [
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null | null | git lfs install
git clone https://huggingface.co/LPM/AI_1 | {} | LPM/AI_1 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
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text-generation | transformers |
# Rick DioloGPT Model
| {"tags": ["conversational"]} | LactoseLegend/DialoGPT-small-Rick | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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# Rick DioloGPT Model
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text-generation | transformers | ### Model information
* Fine tuning dataset: https://www.kaggle.com/seungguini/bts-youtube-comments
* Base model: GPT2 Small
* Epoch: 5
* API page: [Ainize](https://ainize.ai/teachable-ainize/gpt2-train?branch=train/cv695m9g40av0cdabuqp)
* Demo page: [End-point](https://kubecon-tabtab-ainize-team.endpoint.ainize.ai/?mo... | {} | Laeyoung/BTS-comments-generator | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Model information
* Fine tuning dataset: URL
* Base model: GPT2 Small
* Epoch: 5
* API page: Ainize
* Demo page: End-point
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
* Teachable NLP: Teachable NLP... | [
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text-generation | transformers |
#Witcher1 Geralt DialoGPT small model | {"tags": ["conversational"]} | Laezor/DialoGPT-small-witcher1 | null | [
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#Witcher1 Geralt DialoGPT small model | [] | [
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text-generation | transformers |
#Yakuza 0 DialoGPT Model | {"tags": ["conversational"]} | Laezor/DialoGPT-small-yakuza_0 | null | [
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text-generation | transformers |
# Dialogue From Persona 3 | {"tags": ["conversational"]} | LaiJY/DialoGPTChatbot | null | [
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translation | transformers | ### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set scores: 15.53
## Training
Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-Project). Experiments tr... | {"tags": ["translation", "torch==1.8.0"], "widget": [{"text": "Inference Unavailable"}]} | Lalita/marianmt-th-zh_cn | null | [
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"marian",
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"torch==1.8.0",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us
| ### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set scores: 15.53
## Training
Training scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-th-zh_cn.
## Usage
#... | [
"### marianmt-th-zh_cn\n* source languages: th\n* target languages: zh_cn\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set scores: 15.53",
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translation | transformers | ### marianmt-zh_cn-th
* source languages: zh_cn
* target languages: th
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set scores: syllable: 15.95, word: 8.43
## Training
Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-P... | {"tags": ["translation", "torch==1.8.0"], "widget": [{"text": "Inference Unavailable"}]} | Lalita/marianmt-zh_cn-th | null | [
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#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### marianmt-zh_cn-th
* source languages: zh_cn
* target languages: th
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set scores: syllable: 15.95, word: 8.43
## Training
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null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on cnceleb
This repository provides all the necessary tool... | {"language": "zh", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "ECAPA", "TDNN"], "datasets": ["cnceleb"], "metrics": ["EER"]} | LanceaKing/spkrec-ecapa-cnceleb | null | [
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|
Speaker Verification with ECAPA-TDNN embeddings on cnceleb
==========================================================
This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
The system can be used to extract speaker embeddings as w... | [
"### Compute your speaker embeddings\n\n\nThe system is trained with recordings sampled at 16kHz (single channel).\nThe code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify\\_file* if needed. Make sure your input tensor is compliant with the expected sampli... | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-starter
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the Langame/starter ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Langame/starter"], "model-index": [{"name": "distilgpt2-starter", "results": []}]} | Langame/distilgpt2-starter | null | [
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| distilgpt2-starter
==================
This model is a fine-tuned version of distilgpt2 on the Langame/starter dataset.
It achieves the following results on the evaluation set:
* Loss: 6.0234
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with... | [
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text-generation | transformers |
# Langame/gpt2-waiting
This fine-tuned model can generate funny waiting messages.
[Langame](https://langa.me) uses these within its platform 😛.
| {"language": ["en"], "license": "mit", "tags": ["text-generation"], "datasets": ["waiting-messages"], "widget": [{"text": "List of funny waiting messages:", "example_title": "Funny waiting messages"}]} | Langame/gpt2-waiting | null | [
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|
# Langame/gpt2-waiting
This fine-tuned model can generate funny waiting messages.
Langame uses these within its platform .
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fill-mask | transformers | # Mengzi-BERT base fin model (Chinese)
Continue trained mengzi-bert-base with 20G financial news and research reports. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](http... | {"language": ["zh"], "license": "apache-2.0"} | Langboat/mengzi-bert-base-fin | null | [
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"license:apache-2.0",
"autotrain_compatible",
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"region:us"
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| # Mengzi-BERT base fin model (Chinese)
Continue trained mengzi-bert-base with 20G financial news and research reports. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
## Us... | [
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fill-mask | transformers |
# Mengzi-BERT base model (Chinese)
Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
[Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model](https://arxiv.org/abs/2110.06696)
## Usage
``... | {"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]} | Langboat/mengzi-bert-base | null | [
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| Mengzi-BERT base model (Chinese)
================================
Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model
Usage
-----
Sc... | [] | [
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fill-mask | transformers |
# Mengzi-oscar-base-caption (Chinese Multi-modal Image Caption model)
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
Mengzi-oscar-base-caption is fine-tuned based on Chinese multi-modal pre-training model [Mengzi-Oscar](https://github.com/Langboat/Mengzi/... | {"language": ["zh"], "license": "apache-2.0"} | Langboat/mengzi-oscar-base-caption | null | [
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|
# Mengzi-oscar-base-caption (Chinese Multi-modal Image Caption model)
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
Mengzi-oscar-base-caption is fine-tuned based on Chinese multi-modal pre-training model Mengzi-Oscar, on AIC-ICC Chinese image caption dataset.
## Usage
#### Installation
Ch... | [
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fill-mask | transformers | # Mengzi-oscar-base-retrieval (Chinese Image-text retrieval model)
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
Mengzi-oscar-base-retrieval is fine-tuned based on Chinese multi-modal pre-training model [Mengzi-Oscar](https://github.com/Langboat/Mengzi/bl... | {"language": ["zh"], "license": "apache-2.0"} | Langboat/mengzi-oscar-base-retrieval | null | [
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"zh"
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#transformers #pytorch #bert #fill-mask #zh #arxiv-2110.06696 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Mengzi-oscar-base-retrieval (Chinese Image-text retrieval model)
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
Mengzi-oscar-base-retrieval is fine-tuned based on Chinese multi-modal pre-training model Mengzi-Oscar, on COCO-ir dataset.
## Usage
#### Installation
Check URL for installatio... | [
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fill-mask | transformers |
# Mengzi-oscar-base (Chinese Multi-modal pre-training model)
Mengzi-oscar is trained based on the Multi-modal pre-training model [Oscar](https://github.com/microsoft/Oscar), and is initialized using [Mengzi-Bert-Base](https://github.com/Langboat/Mengzi). 3.7M pairs of images and texts were used, including 0.7M Chinese... | {"language": ["zh"], "license": "apache-2.0"} | Langboat/mengzi-oscar-base | null | [
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# Mengzi-oscar-base (Chinese Multi-modal pre-training model)
Mengzi-oscar is trained based on the Multi-modal pre-training model Oscar, and is initialized using Mengzi-Bert-Base. 3.7M pairs of images and texts were used, including 0.7M Chinese image-caption pairs, 3M Chinese image-question pairs, a total of 0.22M diff... | [
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text2text-generation | transformers |
# Mengzi-T5 model (Chinese)
Pretrained model on 300G Chinese corpus.
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("Langboat/men... | {"language": ["zh"], "license": "apache-2.0"} | Langboat/mengzi-t5-base | null | [
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|
# Mengzi-T5 model (Chinese)
Pretrained model on 300G Chinese corpus.
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
## Usage
If you find the technical report or resource is useful, please cite the following technical report in your paper.
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text-generation | transformers |
# Gandalf DialoGPT Model | {"tags": ["conversational"]} | Laptop/DialoGPT-small-gandalf | null | [
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# Gandalf DialoGPT Model | [
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token-classification | transformers |
## DeFormer
DeFormer är en modell som har tränats på att skilja mellan `de` och `dem` i svenska meningar. Modellen kan testas direkt i panelerna till höger under **Hosted Inference API** genom att skriva in en mening och trycka på **Compute**.
**Uppdatering 2023-05-06:** Modellen kan nu hantera även borttappade t:n... | {"widget": [{"text": "dem har s\u00f6kt upp de f\u00f6r att prata.", "example_title": "de/dem exempel 1"}, {"text": "Jag s\u00e5g de komma runt h\u00f6rnet och g\u00e5 i riktning mot dem byggnaderna.", "example_title": "de/dem exempel 2"}, {"text": "de \u00e4r ganska tr\u00e5kigt att de blivit s\u00e5h\u00e4r, men de v... | Lauler/deformer | null | [
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| DeFormer
--------
DeFormer är en modell som har tränats på att skilja mellan 'de' och 'dem' i svenska meningar. Modellen kan testas direkt i panelerna till höger under Hosted Inference API genom att skriva in en mening och trycka på Compute.
Uppdatering 2023-05-06: Modellen kan nu hantera även borttappade t:n i det... | [] | [
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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. -->
# results
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "results", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi"... | Lazaro97/results | null | [
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| results
=======
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3793
* Accuracy: 0.8404
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
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feature-extraction | transformers |
# LeBenchmark: wav2vec2 base model trained on 1K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version o... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-1K-base | null | [
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|
# LeBenchmark: wav2vec2 base model trained on 1K hours of French speech
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feature-extraction | transformers |
# LeBenchmark: wav2vec2 large model trained on 1K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version ... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-1K-large | null | [
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|
# LeBenchmark: wav2vec2 large model trained on 1K hours of French speech
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feature-extraction | transformers |
# LeBenchmark: wav2vec2 base model trained on 2.6K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-2.6K-base | null | [
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# LeBenchmark: wav2vec2 base model trained on 2.6K hours of French speech
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feature-extraction | transformers |
# LeBenchmark: wav2vec2 base model trained on 3K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version o... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-3K-base | null | [
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# LeBenchmark: wav2vec2 base model trained on 3K hours of French speech
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feature-extraction | transformers |
# LeBenchmark: wav2vec2 large model trained on 3K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version ... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-3K-large | null | [
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# LeBenchmark: wav2vec2 large model trained on 3K hours of French speech
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feature-extraction | transformers |
# LeBenchmark: wav2vec2 base model trained on 7K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version o... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-7K-base | null | [
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"2309.05472"
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|
# LeBenchmark: wav2vec2 base model trained on 7K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version o... | [
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feature-extraction | transformers |
# LeBenchmark: wav2vec2 large model trained on 7K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version ... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-7K-large | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"feature-extraction",
"fr",
"arxiv:2309.05472",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2309.05472"
] | [
"fr"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #feature-extraction #fr #arxiv-2309.05472 #license-apache-2.0 #endpoints_compatible #region-us
|
# LeBenchmark: wav2vec2 large model trained on 7K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version ... | [
"# LeBenchmark: wav2vec2 large model trained on 7K hours of French speech\n\n \n\nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended ... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #feature-extraction #fr #arxiv-2309.05472 #license-apache-2.0 #endpoints_compatible #region-us \n",
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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_xls_r_300m_hi_cv7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "Wav2Vec2_xls_r_300m_hi_cv7", "results": []}]} | LegolasTheElf/Wav2Vec2_xls_r_300m_hi_cv7 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2\_xls\_r\_300m\_hi\_cv7
===============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6567
* Wer: 0.6273
* Cer: 0.2093
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
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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_xls_r_300m_hi_final
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "Openslr Multilingual", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "model-index": [{"name": "Wav2Vec2_xls_r_300m_hi_final", "results": []}]} | LegolasTheElf/Wav2Vec2_xls_r_300m_hi_final | null | [
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"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #Openslr Multilingual #mozilla-foundation/common_voice_7_0 #generated_from_trainer #hi #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2\_xls\_r\_300m\_hi\_final
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the 'Openslr Multilingual and code-switching ASR challenge' dataset and 'mozilla-foundation/common\_voice\_7\_0' dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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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_xls_r_300m_hi_final
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook... | {"language": ["hi"], "license": "apache-2.0", "tags": ["Openslr Multilingual", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "Wav2Vec2_xls_r_300m... | LegolasTheElf/Wav2Vec2_xls_r_lm_300m_hi | null | [
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"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"Openslr Multilingual",
"generated_from_trainer",
"hf-asr-leaderboard",
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"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index... | null | 2022-03-02T23:29:04+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #Openslr Multilingual #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2\_xls\_r\_300m\_hi\_final
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the 'Openslr Multilingual and code-switching ASR challenge' dataset and 'mozilla-foundation/common\_voice\_7\_0' dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #Openslr Multilingual #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
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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_xls_r_openslr_Hi_V2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "Harveenchadha/indic-voice", "generated_from_trainer"], "model-index": [{"name": "Wav2Vec2_xls_r_openslr_Hi_V2", "results": []}]} | LegolasTheElf/Wav2Vec2_xls_r_openslr_Hi_V2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"Harveenchadha/indic-voice",
"generated_from_trainer",
"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #Harveenchadha/indic-voice #generated_from_trainer #hi #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2\_xls\_r\_openslr\_Hi\_V2
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Harveenchadha/indic-voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3184
* Wer: 0.3104
* Cer: 0.0958
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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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": []}]} | Leisa/distilbert-base-uncased-finetuned-imdb | null | [
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"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #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: 2.3114
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... | [
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"TAGS\n#transformers #pytorch #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\\_batch\\_size: 64... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | Leisa/marian-finetuned-kde4-en-to-fr | null | [
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"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8558
- Bleu: 52.9454
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8558\n- Bleu: 52.9454",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
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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. -->
#
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collect... | {"language": ["lb"], "license": "mit", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-4h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"lb",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us
|
#
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collected from URL. Then the model was fine-tuned on 4h of labelled
Luxembourgish speech from the same domain.
## Intended uses & limitations
More information needed
## Training and evaluati... | [
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"## Model description\n\nWe fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech\ncollected from URL. Then the model was fine-tuned on 4h of labelled\nLuxembourgish speech from the same domain.",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad-Endpoint_with_impossible.csv
This model is a fine-tuned version of [distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad-Endpoint_with_impossible.csv", "results": []}]} | LenaSchmidt/distilbert-base-uncased-finetuned-squad-Endpoint_with_impossible.csv | null | [
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"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad-Endpoint\_with\_impossible.csv
======================================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7950
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | LenaSchmidt/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
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"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7713
Model description
-----------------
More information needed
Intended uses... | [
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | LenaT/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
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"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
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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. -->
# first
This model is a fine-tuned version of [longformer-gottbert-base-8192-aw512-](https://huggingface.co/longformer-8192-aw512-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "first", "results": []}]} | LennartKeller/longformer-gottbert-base-8192-aw512 | null | [
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"safetensors",
"longformer",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #longformer #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| first
=====
This model is a fine-tuned version of longformer-gottbert-base-8192-aw512- on the a 500 million token subset of the german parts of the OSCAR dataset.
It achieves the following results on the custom evaluation set:
* Loss: 1.4981
Model description
-----------------
The weights of the model are initi... | [
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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. -->
# first
This model is a fine-tuned version of [nystromformer-gottbert-base-8192](https://huggingface.co/nystromformer-gottbert-bas... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "first", "results": []}]} | LennartKeller/nystromformer-gottbert-base-8192 | null | [
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"nystromformer",
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"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #nystromformer #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| first
=====
This model is a fine-tuned version of nystromformer-gottbert-base-8192 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5135
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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text-generation | transformers |
#Kobayashi DialoGPT Model | {"tags": ["conversational"]} | Lenza/DialoGPT-medium-Kobayashi | null | [
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"text-generation",
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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summarization | transformers | ## Hyperparameters
{
"num_train_epochs": 3,
"seed": 7,
"summary_column": "output_text",
"text_column": "text",
"encoder_max_length" : 512,
"decoder_max_length" :36,
"batch_size" : 256
}
## Usage
## Results
| key | value |
| --- | ----- |
| eval loss | 4.539857387542725|
| eval_rou... | {"language": "es", "license": "apache-2.0", "tags": ["summarization", "spanish", "beto2beto", "encoder-decoder"], "datasets": ["LeoCordoba/CC-NEWS-ES-titles"], "widget": [{"text": "La chocotorta, el tradicional y pr\u00e1ctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por cr\u00edticos ... | LeoCordoba/beto2beto-cc-news-es-titles | null | [
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"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"spanish",
"beto2beto",
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
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| Hyperparameters
---------------
{
```
"num_train_epochs": 3,
"seed": 7,
"summary_column": "output_text",
"text_column": "text",
"encoder_max_length" : 512,
"decoder_max_length" :36,
"batch_size" : 256
```
}
Usage
-----
Results
-------
| [] | [
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] |
summarization | transformers | ## beto2beto-mlsum
This model was trained on the Spanish section of MLSum: https://paperswithcode.com/sota/abstractive-text-summarization-on-mlsum.
## Hyperparameters
{
"dataset_config": "es",
"dataset_name": "mlsum",
"do_eval": true,
"do_predict": true,
"do_train": true,
"fp16": true,
... | {"language": "es", "license": "apache-2.0", "tags": ["summarization", "spanish", "encoder-decoder", "beto"], "datasets": ["mlsum - es"], "widget": [{"text": "La chocotorta, el tradicional y pr\u00e1ctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por cr\u00edticos de restaurants internac... | LeoCordoba/beto2beto-mlsum | null | [
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"spanish",
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #spanish #beto #es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| beto2beto-mlsum
---------------
This model was trained on the Spanish section of MLSum: URL
Hyperparameters
---------------
```
{
"dataset_config": "es",
"dataset_name": "mlsum",
"do_eval": true,
"do_predict": true,
"do_train": true,
"fp16": true,
"max_target_length": 64,
"num_train_epochs": 10,
"per_device_eval... | [] | [
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] | [
62
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] |
text-generation | transformers | ## beto2beto
Usage example here: https://colab.research.google.com/drive/18a2ZfF1e_Kyyydlv8INQIkJbv294xcAm?usp=sharing
Entrenado por 3 epochs sobre CC-NEWS-ES (2019), aproximadamente 68.000 steps. Encoder max length: 40•Decoder max length: 128
## Hyperparameters
## Usage
## Results
| key | value |
| --- | ----- |
... | {"language": "es", "license": "apache-2.0", "tags": ["text-generation", "spanish", "encoder-decoder", "beto"], "datasets": ["LeoCordoba/CC-NEWS-ES"]} | LeoCordoba/beto2beto | null | [
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| beto2beto
---------
Usage example here: URL
Entrenado por 3 epochs sobre CC-NEWS-ES (2019), aproximadamente 68.000 steps. Encoder max length: 40•Decoder max length: 128
Hyperparameters
---------------
Usage
-----
Results
-------
| [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #text-generation #spanish #beto #es #dataset-LeoCordoba/CC-NEWS-ES #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
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68
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] |
summarization | transformers |
## Hyperparameters
{
"max_target_length": 64,
"model_name_or_path": "google/mt5-small",
"num_train_epochs": 3,
"seed": 7,
"summary_column": "output_text",
"text_column": "text",
"encoder_max_length" : 512,
"decoder_max_length" :36,
"batch_size" : 128
}
## Usage
```
arti... | {"language": "es", "license": "apache-2.0", "tags": ["summarization", "mt5", "spanish"], "datasets": ["LeoCordoba/CC-NEWS-ES-titles"], "widget": [{"text": "La chocotorta, el tradicional y pr\u00e1ctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por cr\u00edticos de restaurants internacio... | LeoCordoba/mt5-small-cc-news-es-titles | null | [
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"pytorch",
"mt5",
"text2text-generation",
"summarization",
"spanish",
"es",
"dataset:LeoCordoba/CC-NEWS-ES-titles",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #spanish #es #dataset-LeoCordoba/CC-NEWS-ES-titles #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hyperparameters
---------------
{
```
"max_target_length": 64,
"model_name_or_path": "google/mt5-small",
"num_train_epochs": 3,
"seed": 7,
"summary_column": "output_text",
"text_column": "text",
"encoder_max_length" : 512,
"decoder_max_length" :36,
"batch_size" : 128
```
}
Usage
-----
Results
-------... | [] | [
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] |
summarization | transformers | ## mt5-small-mlsum
This model was trained on the Spanish section of MLSum: https://paperswithcode.com/sota/abstractive-text-summarization-on-mlsum based on mt5-small.
## Hyperparameters
{
"dataset_config": "es",
"dataset_name": "mlsum",
"do_eval": true,
"do_predict": true,
"do_train": true,
"fp... | {"language": "es", "license": "apache-2.0", "tags": ["summarization", "sagemaker", "mt5", "spanish"], "datasets": ["mlsum - es"], "widget": [{"text": "La chocotorta, el tradicional y pr\u00e1ctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por cr\u00edticos de restaurants internacionales... | LeoCordoba/mt5-small-mlsum | null | [
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"license:apache-2.0",
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"autotrain_compatible",
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"es"
] | TAGS
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| mt5-small-mlsum
---------------
This model was trained on the Spanish section of MLSum: URL based on mt5-small.
Hyperparameters
---------------
{
"dataset\_config": "es",
"dataset\_name": "mlsum",
"do\_eval": true,
"do\_predict": true,
"do\_train": true,
"fp16": true,
"max\_target\_length": 64,
"model\_name\_or\_... | [] | [
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66
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] |
text-generation | transformers |
This is Chandler.
Chandler is your friend too. | {"tags": ["conversational"]} | Leonel/DialoGPT-small-chandler | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is Chandler.
Chandler is your friend too. | [] | [
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text-generation | transformers |
# Michael DialoGPT Model | {"tags": ["conversational"]} | Leostronkest/DialoGPT-small-michael | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
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] |
text-generation | transformers |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | Leostronkest/DialoGPT | null | [
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"text-generation",
"conversational",
"arxiv:1911.00536",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1911.00536"
] | [] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
------------------------------------------------------------------------------
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The human evaluation results indicate that the respons... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
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"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
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] |
fill-mask | transformers | # scibert-wechsel-korean
Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique.
### Description
- SciBERT is trained on papers from the corpus of semanticscholar.org. Corpus size is 1.14M papers, 3.1B tokens.
- Wechsel is converting embedding layer's subword tokens from source language to target language... | {} | LeverageX/scibert-wechsel-korean | null | [
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"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # scibert-wechsel-korean
Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique.
### Description
- SciBERT is trained on papers from the corpus of URL. Corpus size is 1.14M papers, 3.1B tokens.
- Wechsel is converting embedding layer's subword tokens from source language to target language.
- SciBERT tra... | [
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text-generation | transformers |
# Jake99 DialoGPT model | {"tags": ["conversational"]} | Leviii03/Dialogpt-small-Jake99 | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jake99 DialoGPT model | [
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text-classification | transformers | [bert-base-uncased](https://huggingface.co/bert-base-uncased) fine-tuned on the [QNLI](https://huggingface.co/datasets/glue) dataset for 2 epochs.
The fine-tuning process was performed on 2x NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
```
max_seq_length=512
per_device_train_batch_size=8
gradient_accu... | {} | Li/bert-base-uncased-qnli | null | [
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"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased fine-tuned on the QNLI dataset for 2 epochs.
The fine-tuning process was performed on 2x NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
## Evaluation results
eval_accuracy = 0.916895
## More information
The QNLI (Question-answering NLI) dataset is a Natural Language Inference data... | [
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question-answering | transformers | [roberta-base](https://huggingface.co/roberta-base) fine-tuned on the [SQuAD2](https://rajpurkar.github.io/SQuAD-explorer) dataset for 2 epochs.
The fine-tuning process was performed on a single NVIDIA Tesla T4 GPU (15GB). The hyperparameters are:
```
max_seq_length=512
per_device_train_batch_size=8
gradient_accumul... | {} | Li/roberta-base-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #endpoints_compatible #region-us
| roberta-base fine-tuned on the SQuAD2 dataset for 2 epochs.
The fine-tuning process was performed on a single NVIDIA Tesla T4 GPU (15GB). The hyperparameters are:
## Evaluation results
## More information
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions po... | [
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"## More information\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, o... | [
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text-classification | transformers | At its core it uses an BERT-Base model (bert-base-uncased) fine-tuned on the MS MARCO passage classification task using the Sim-Pair marking strategy that highlights exact term matches between the query and the passage via marker tokens (#). It can be loaded using the TF/AutoModelForSequenceClassification classes.
Ref... | {} | LilaBoualili/bert-sim-pair | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| At its core it uses an BERT-Base model (bert-base-uncased) fine-tuned on the MS MARCO passage classification task using the Sim-Pair marking strategy that highlights exact term matches between the query and the passage via marker tokens (#). It can be loaded using the TF/AutoModelForSequenceClassification classes.
Ref... | [] | [
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text-classification | transformers | At its core it uses a BERT-Base model (bert-base-uncased) fine-tuned on the MS MARCO passage classification task. It can be loaded using the TF/AutoModelForSequenceClassification classes.
Refer to our [github repository](https://github.com/BOUALILILila/ExactMatchMarking) for a usage example for ad hoc ranking. | {} | LilaBoualili/bert-vanilla | null | [
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"tf",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| At its core it uses a BERT-Base model (bert-base-uncased) fine-tuned on the MS MARCO passage classification task. It can be loaded using the TF/AutoModelForSequenceClassification classes.
Refer to our github repository for a usage example for ad hoc ranking. | [] | [
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text-classification | transformers | At its core it uses an ELECTRA-Base model (google/electra-base-discriminator) fine-tuned on the MS MARCO passage classification task using the Sim-Pair marking strategy that highlights exact term matches between the query and the passage via marker tokens (#). It can be loaded using the TF/AutoModelForSequenceClassific... | {} | LilaBoualili/electra-sim-pair | null | [
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"pytorch",
"tf",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
| At its core it uses an ELECTRA-Base model (google/electra-base-discriminator) fine-tuned on the MS MARCO passage classification task using the Sim-Pair marking strategy that highlights exact term matches between the query and the passage via marker tokens (#). It can be loaded using the TF/AutoModelForSequenceClassific... | [] | [
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text-classification | transformers | At its core it uses an ELECTRA-Base model (google/electra-base-discriminator) fine-tuned on the MS MARCO passage classification task. It can be loaded using the TF/AutoModelForSequenceClassification classes but it follows the same classification layer defined for BERT similarly to the TFElectraRelevanceHead in the Capr... | {} | LilaBoualili/electra-vanilla | null | [
"transformers",
"pytorch",
"tf",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
| At its core it uses an ELECTRA-Base model (google/electra-base-discriminator) fine-tuned on the MS MARCO passage classification task. It can be loaded using the TF/AutoModelForSequenceClassification classes but it follows the same classification layer defined for BERT similarly to the TFElectraRelevanceHead in the Capr... | [] | [
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null | null | okuma lan kardeş,im | {} | LinuxMac/denema | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| okuma lan kardeş,im | [] | [
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text2text-generation | transformers | ## End-to-end Conversational search model
A end-to-end system of conversational search system for online shopping. It was introduced in [this paper](https://arxiv.org/abs/2109.05460) published on conference EMNLP.
## Model description
ConvSearch is an end-to-end conversational search system that deeply combines the di... | {} | LiqiangXiao/ConvSearch_QU | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2109.05460",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2109.05460"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2109.05460 #autotrain_compatible #endpoints_compatible #region-us
| ## End-to-end Conversational search model
A end-to-end system of conversational search system for online shopping. It was introduced in this paper published on conference EMNLP.
## Model description
ConvSearch is an end-to-end conversational search system that deeply combines the dialog and search system to improve th... | [
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text2text-generation | transformers | ## Copy-or-Rewrite
This repository contains the code of paper "Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning". A model built for human-like summarization task and trained with Actor-critic Reinforcement Learning. This work significantly improved the ROUGE scores on CNN/DM dataset by 1.7... | {} | LiqiangXiao/summarization | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| ## Copy-or-Rewrite
This repository contains the code of paper "Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning". A model built for human-like summarization task and trained with Actor-critic Reinforcement Learning. This work significantly improved the ROUGE scores on CNN/DM dataset by 1.7... | [
"## Copy-or-Rewrite\nThis repository contains the code of paper \"Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning\". A model built for human-like summarization task and trained with Actor-critic Reinforcement Learning. This work significantly improved the ROUGE scores on CNN/DM datase... | [
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text-classification | transformers |
# bert-base-cased-sentiment
Es un modelo de BERT (bert-base-cased) afinado para el analisis de sentimientos para dos clases.
El sentimiento solo se define como positivo negativo según sea el caso de la oración suministrada.
## Training data
El set de datos utilizado para el entrenamiento del modelo fue a traves ... | {"language": ["en"], "pipeline_tag": "text-classification"} | Littlejohn/analisis_sentimientos | null | [
"transformers",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #text-classification #en #endpoints_compatible #region-us
|
# bert-base-cased-sentiment
Es un modelo de BERT (bert-base-cased) afinado para el analisis de sentimientos para dos clases.
El sentimiento solo se define como positivo negativo según sea el caso de la oración suministrada.
## Training data
El set de datos utilizado para el entrenamiento del modelo fue a traves ... | [
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