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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5small-opus_infopankki-en-zh
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_info... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_infopankki"], "model-index": [{"name": "t5small-opus_infopankki-en-zh", "results": []}]} | 0x12/t5small-opus_infopankki-en-zh | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T04:07:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5small-opus\_infopankki-en-zh
==============================
This model is a fine-tuned version of t5-small on the opus\_infopankki dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0385
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
null | fastai |
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([docume... | {"tags": ["fastai"]} | osanseviero/cool_synth_learner | null | [
"fastai",
"region:us"
] | null | 2022-04-27T06:46:00+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
feature-extraction | transformers |
# rinna/japanese-clip-vit-b-16
This is a Japanese [CLIP (Contrastive Language-Image Pre-Training)](https://arxiv.org/abs/2103.00020) model trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/).
Please see [japanese-clip](https://github.com/rinnakk/japanese-clip) for the other available ... | {"language": "ja", "license": "apache-2.0", "tags": ["feature-extraction", "ja", "japanese", "clip", "vision"], "thumbnail": "https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png"} | rinna/japanese-clip-vit-b-16 | null | [
"transformers",
"pytorch",
"safetensors",
"clip",
"zero-shot-image-classification",
"feature-extraction",
"ja",
"japanese",
"vision",
"arxiv:2103.00020",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T06:52:33+00:00 | [
"2103.00020"
] | [
"ja"
] | TAGS
#transformers #pytorch #safetensors #clip #zero-shot-image-classification #feature-extraction #ja #japanese #vision #arxiv-2103.00020 #license-apache-2.0 #endpoints_compatible #region-us
|
# rinna/japanese-clip-vit-b-16
!rinna-icon
This is a Japanese CLIP (Contrastive Language-Image Pre-Training) model trained by rinna Co., Ltd..
Please see japanese-clip for the other available models.
# How to use the model
1. Install package
2. Run
# Model architecture
The model was trained a ViT-B/16 Tr... | [
"# rinna/japanese-clip-vit-b-16\n\n!rinna-icon\n\nThis is a Japanese CLIP (Contrastive Language-Image Pre-Training) model trained by rinna Co., Ltd..\n\nPlease see japanese-clip for the other available models.",
"# How to use the model\n\n\n1. Install package\n\n\n\n2. Run",
"# Model architecture\nThe model was... | [
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"# rinna/japanese-clip-vit-b-16\n\n!rinna-icon\n\nThis is a Japanese CLIP (Contrastive Language-Image Pre-Training)... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Lost Job (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of `neuralmind/bert-base-portuguese-cased` finetuned to recognize Portuguese tweets whe... | {"language": "pt", "widget": [{"text": "hoje perdi o meu trabalho.."}]} | manueltonneau/bert-twitter-pt-lost-job | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pt",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T06:52:37+00:00 | [
"2203.09178"
] | [
"pt"
] | TAGS
#transformers #pytorch #bert #text-classification #pt #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Lost Job (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Portuguese tweets whe... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Lost Job (1), else (0)\n- country: BR \n- language: Portuguese\n- architecture: BERT base",
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"## Model main characteristics:\n- class: Lost Job (1), else (0)\n- country: BR \n- language: Portuguese\n- archit... |
token-classification | transformers |
# BERT-base-multilingual-cased finetuned for Part-of-Speech tagging
This is a multilingual BERT model fine tuned for part-of-speech tagging for English. It is trained using the Penn TreeBank (Marcus et al., 1993) and achieves an F1-score of 96.69.
## Usage
A *transformers* pipeline can be used to run the model:
```... | {"language": ["en"], "license": "cc-by-nc-3.0", "tags": ["part-of-speech", "finetuned"]} | QCRI/bert-base-multilingual-cased-pos-english | null | [
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"bert",
"token-classification",
"part-of-speech",
"finetuned",
"en",
"license:cc-by-nc-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-27T07:15:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #part-of-speech #finetuned #en #license-cc-by-nc-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-base-multilingual-cased finetuned for Part-of-Speech tagging
This is a multilingual BERT model fine tuned for part-of-speech tagging for English. It is trained using the Penn TreeBank (Marcus et al., 1993) and achieves an F1-score of 96.69.
## Usage
A *transformers* pipeline can be used to run the model:
... | [
"# BERT-base-multilingual-cased finetuned for Part-of-Speech tagging\n\nThis is a multilingual BERT model fine tuned for part-of-speech tagging for English. It is trained using the Penn TreeBank (Marcus et al., 1993) and achieves an F1-score of 96.69.",
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"# BERT-base-multilingual-cased finetuned for Part-of-Speech tagging\n\nThis is a multilingual BERT model fine tuned for part-of-spe... |
feature-extraction | transformers |
# rinna/japanese-cloob-vit-b-16
This is a Japanese [CLOOB (Contrastive Leave One Out Boost)](https://arxiv.org/abs/2110.11316) model trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/).
Please see [japanese-clip](https://github.com/rinnakk/japanese-clip) for the other available models... | {"language": "ja", "license": "apache-2.0", "tags": ["feature-extraction", "ja", "japanese", "clip", "cloob", "vision"], "thumbnail": "https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png"} | rinna/japanese-cloob-vit-b-16 | null | [
"transformers",
"pytorch",
"cloob",
"feature-extraction",
"ja",
"japanese",
"clip",
"vision",
"arxiv:2110.11316",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-27T07:29:29+00:00 | [
"2110.11316"
] | [
"ja"
] | TAGS
#transformers #pytorch #cloob #feature-extraction #ja #japanese #clip #vision #arxiv-2110.11316 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# rinna/japanese-cloob-vit-b-16
!rinna-icon
This is a Japanese CLOOB (Contrastive Leave One Out Boost) model trained by rinna Co., Ltd..
Please see japanese-clip for the other available models.
# How to use the model
1. Install package
2. Run
# Model architecture
The model was trained a ViT-B/16 Transform... | [
"# rinna/japanese-cloob-vit-b-16\n\n!rinna-icon\n\nThis is a Japanese CLOOB (Contrastive Leave One Out Boost) model trained by rinna Co., Ltd..\n\nPlease see japanese-clip for the other available models.",
"# How to use the model\n\n\n1. Install package\n\n\n\n2. Run",
"# Model architecture\nThe model was train... | [
"TAGS\n#transformers #pytorch #cloob #feature-extraction #ja #japanese #clip #vision #arxiv-2110.11316 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# rinna/japanese-cloob-vit-b-16\n\n!rinna-icon\n\nThis is a Japanese CLOOB (Contrastive Leave One Out Boost) model trained by rinna Co., Ltd.... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner-final
This model was trained from scratch on the None dataset.
## Model description
More... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner-final", "results": []}]} | Lilya/distilbert-base-uncased-finetuned-ner-final | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T07:29:58+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-ner-final
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameter... | [
"# distilbert-base-uncased-finetuned-ner-final\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
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"# distilbert-base-uncased-finetuned-ner-final\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"#... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Hired (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of `neuralmind/bert-base-portuguese-cased` finetuned to recognize Portuguese tweets whe... | {"language": "pt", "widget": [{"text": "Primeiro dia do novo emprego!"}]} | manueltonneau/bert-twitter-pt-is-hired | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pt",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T07:34:39+00:00 | [
"2203.09178"
] | [
"pt"
] | TAGS
#transformers #pytorch #bert #text-classification #pt #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Hired (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Portuguese tweets whe... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Hired (1), else (0)\n- country: BR \n- language: Portuguese\n- architecture: BERT base",
"## Model description \nThis model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Portu... | [
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"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Hired (1), else (0)\n- country: BR \n- language: Portuguese\n- archit... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples-pi
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples-pi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args... | peringe/finetuning-sentiment-model-3000-samples-pi | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T07:37:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples-pi
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: 0.3344
- Accuracy: 0.8633
- F1: 0.8664
## Model description
More information needed
## Intended uses & limitations
More... | [
"# finetuning-sentiment-model-3000-samples-pi\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3344\n- Accuracy: 0.8633\n- F1: 0.8664",
"## Model description\n\nMore information needed",
"## Intended uses &... | [
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"# finetuning-sentiment-model-3000-samples-pi\n\nThis model is a fine-tuned version of distilbert-base-unca... |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Unemployed (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of `neuralmind/bert-base-portuguese-cased` finetuned to recognize Portuguese tweet... | {"language": "pt", "widget": [{"text": "T\u00f4 desempregada!"}]} | manueltonneau/bert-twitter-pt-is-unemployed | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pt",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T07:56:53+00:00 | [
"2203.09178"
] | [
"pt"
] | TAGS
#transformers #pytorch #bert #text-classification #pt #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Is Unemployed (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Portuguese tweet... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Is Unemployed (1), else (0)\n- country: BR \n- language: Portuguese\n- architecture: BERT base",
"## Model description \nThis model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize ... | [
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"## Model main characteristics:\n- class: Is Unemployed (1), else (0)\n- country: BR \n- language: Portuguese\n- a... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 791824374
- CO2 Emissions (in grams): 1119.6398037843474
## Validation Metrics
- Loss: 1.6432833671569824
- Rouge1: 38.5315
- Rouge2: 18.0869
- RougeL: 32.3742
- RougeLsum: 32.3801
- Gen Len: 19.846
## Usage
You can use cURL to access this ... | {"language": "en", "tags": "autotrain", "datasets": ["faisalahmad/autotrain-data-nsut-nlp-project-textsummarization"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1119.6398037843474} | faisalahmad/autotrain-nsut-nlp-project-textsummarization-791824374 | null | [
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"pytorch",
"bart",
"text2text-generation",
"autotrain",
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"dataset:faisalahmad/autotrain-data-nsut-nlp-project-textsummarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T08:08:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-faisalahmad/autotrain-data-nsut-nlp-project-textsummarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 791824374
- CO2 Emissions (in grams): 1119.6398037843474
## Validation Metrics
- Loss: 1.6432833671569824
- Rouge1: 38.5315
- Rouge2: 18.0869
- RougeL: 32.3742
- RougeLsum: 32.3801
- Gen Len: 19.846
## Usage
You can use cURL to access this ... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 791824374\n- ... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 791824379
- CO2 Emissions (in grams): 736.9366247330848
## Validation Metrics
- Loss: 1.7805895805358887
- Rouge1: 37.8222
- Rouge2: 16.7598
- RougeL: 31.2959
- RougeLsum: 31.3048
- Gen Len: 19.7213
## Usage
You can use cURL to access this ... | {"language": "en", "tags": "autotrain", "datasets": ["faisalahmad/autotrain-data-nsut-nlp-project-textsummarization"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 736.9366247330848} | faisalahmad/summarizer1 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"en",
"dataset:faisalahmad/autotrain-data-nsut-nlp-project-textsummarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T08:08:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-faisalahmad/autotrain-data-nsut-nlp-project-textsummarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 791824379
- CO2 Emissions (in grams): 736.9366247330848
## Validation Metrics
- Loss: 1.7805895805358887
- Rouge1: 37.8222
- Rouge2: 16.7598
- RougeL: 31.2959
- RougeLsum: 31.3048
- Gen Len: 19.7213
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 791824379\n- CO2 Emissions (in grams): 736.9366247330848",
"## Validation Metrics\n\n- Loss: 1.7805895805358887\n- Rouge1: 37.8222\n- Rouge2: 16.7598\n- RougeL: 31.2959\n- RougeLsum: 31.3048\n- Gen Len: 19.7213",
"## Usage\n\nYou can... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-faisalahmad/autotrain-data-nsut-nlp-project-textsummarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 791824379\n- ... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 791824381
- CO2 Emissions (in grams): 4444.804304528572
## Validation Metrics
- Loss: 1.4599040746688843
- Rouge1: 46.5461
- Rouge2: 23.8595
- RougeL: 38.526
- RougeLsum: 38.5219
- Gen Len: 23.468
## Usage
You can use cURL to access this mo... | {"language": "en", "tags": "autotrain", "datasets": ["faisalahmad/autotrain-data-nsut-nlp-project-textsummarization"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4444.804304528572} | faisalahmad/summarizer2 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"en",
"dataset:faisalahmad/autotrain-data-nsut-nlp-project-textsummarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T08:09:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-faisalahmad/autotrain-data-nsut-nlp-project-textsummarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 791824381
- CO2 Emissions (in grams): 4444.804304528572
## Validation Metrics
- Loss: 1.4599040746688843
- Rouge1: 46.5461
- Rouge2: 23.8595
- RougeL: 38.526
- RougeLsum: 38.5219
- Gen Len: 23.468
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 791824381\n- CO2 Emissions (in grams): 4444.804304528572",
"## Validation Metrics\n\n- Loss: 1.4599040746688843\n- Rouge1: 46.5461\n- Rouge2: 23.8595\n- RougeL: 38.526\n- RougeLsum: 38.5219\n- Gen Len: 23.468",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-faisalahmad/autotrain-data-nsut-nlp-project-textsummarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 791824381\... |
summarization | transformers |
# mt5-cpe-kmutt-thai-sentence-sum
This repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of the model is based on mT5 model and fine-tuned on text-summarization pairs in Thai. Also, this project is a Senior Project of Computer Engineering Student at King Mongkutโs Univ... | {"language": ["th"], "tags": ["summarization", "mT5"], "widget": [{"text": "simplify: \u0e16\u0e49\u0e32\u0e1e\u0e39\u0e14\u0e16\u0e36\u0e07\u0e02\u0e19\u0e21\u0e2b\u0e27\u0e32\u0e19\u0e43\u0e19\u0e15\u0e33\u0e19\u0e32\u0e19\u0e17\u0e35\u0e48\u0e0a\u0e37\u0e48\u0e19\u0e43\u0e08\u0e17\u0e35\u0e48\u0e2a\u0e38\u0e14\u0e41... | thanathorn/mt5-cpe-kmutt-thai-sentence-sum | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"th",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T08:12:47+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #th #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# mt5-cpe-kmutt-thai-sentence-sum
This repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of the model is based on mT5 model and fine-tuned on text-summarization pairs in Thai. Also, this project is a Senior Project of Computer Engineering Student at King Mongkutโs Univ... | [
"# mt5-cpe-kmutt-thai-sentence-sum\nThis repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of the model is based on mT5 model and fine-tuned on text-summarization pairs in Thai. Also, this project is a Senior Project of Computer Engineering Student at King Mongkutโs... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #th #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# mt5-cpe-kmutt-thai-sentence-sum\nThis repository contains the finetuned mT5-base model for Thai sentence summarization. The architec... |
image-classification | transformers |
A small Resnet model for MNIST. Achieves 0.985 accuracy on the validation set. | {"license": "gpl-3.0"} | fxmarty/resnet-tiny-mnist | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T08:15:31+00:00 | [] | [] | TAGS
#transformers #pytorch #resnet #image-classification #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
A small Resnet model for MNIST. Achieves 0.985 accuracy on the validation set. | [] | [
"TAGS\n#transformers #pytorch #resnet #image-classification #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417602105192468480/UZFq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pollinations_ai/1651051095670/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pollinations_ai | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T08:16:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Pollinations
@pollinations\_ai
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | dannytkn/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T08:17:34+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1562047623887986688/YRlT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ai_curio_bot/1666644371831/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ai_curio_bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T08:34:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ai\_curio\_bot
@ai\_curio\_bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of `neuralmind/bert-base-portuguese-cased` finetuned to recognize Portuguese tweets co... | {"language": "pt", "widget": [{"text": "VAGA - Assistente Comercial - S\u00e3o Paulo; Interessados mandar curr\u00edculo"}]} | manueltonneau/bert-twitter-pt-job-offer | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pt",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T08:40:11+00:00 | [
"2203.09178"
] | [
"pt"
] | TAGS
#transformers #pytorch #bert #text-classification #pt #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Portuguese tweets co... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Offer (1), else (0)\n- country: BR \n- language: Portuguese\n- architecture: BERT base",
"## Model description \nThis model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Port... | [
"TAGS\n#transformers #pytorch #bert #text-classification #pt #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Offer (1), else (0)\n- country: BR \n- language: Portuguese\n- archi... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Prinernian/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T08:46:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1383
* F1: 0.8589
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
translation | transformers |
# t5-small-24L-ccmatrix-multi
A [t5-small-24L-dutch-english](https://huggingface.co/yhavinga/t5-small-24L-dutch-english) model finetuned for Dutch to English and English to Dutch translation on the CCMatrix dataset.
Evaluation metrics of this model are listed in the **Translation models** section below.
You can use ... | {"language": ["nl", "en"], "license": "apache-2.0", "tags": ["t5", "translation", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned", "yhavinga/ccmatrix"], "pipeline_tag": "translation", "widget": [{"text": "It is a painful and tragic spectacle that rises before me: I have drawn back the curtain from the rottenness of ... | yhavinga/t5-small-24L-ccmatrix-multi | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"seq2seq",
"nl",
"en",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:yhavinga/ccmatrix",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inf... | null | 2022-04-27T08:46:23+00:00 | [] | [
"nl",
"en"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/ccmatrix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5-small-24L-ccmatrix-multi
===========================
A t5-small-24L-dutch-english model finetuned for Dutch to English and English to Dutch translation on the CCMatrix dataset.
Evaluation metrics of this model are listed in the Translation models section below.
You can use this model directly with a pipeline for... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #translation #seq2seq #nl #en #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/ccmatrix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Search (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of `neuralmind/bert-base-portuguese-cased` finetuned to recognize Portuguese tweets m... | {"language": "pt", "widget": [{"text": "Preciso de um emprego"}]} | manueltonneau/bert-twitter-pt-job-search | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pt",
"arxiv:2203.09178",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T09:12:44+00:00 | [
"2203.09178"
] | [
"pt"
] | TAGS
#transformers #pytorch #bert #text-classification #pt #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us
|
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Search (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT base
## Model description
This model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Portuguese tweets m... | [
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Search (1), else (0)\n- country: BR \n- language: Portuguese\n- architecture: BERT base",
"## Model description \nThis model is a version of 'neuralmind/bert-base-portuguese-cased' finetuned to recognize Por... | [
"TAGS\n#transformers #pytorch #bert #text-classification #pt #arxiv-2203.09178 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Detection of employment status disclosures on Twitter",
"## Model main characteristics:\n- class: Job Search (1), else (0)\n- country: BR \n- language: Portuguese\n- arch... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_300m_fleurs_asr_western_european
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hugg... | {"language": ["all"], "license": "apache-2.0", "tags": ["fleurs-asr", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "model-index": [{"name": "xtreme_s_xlsr_300m_fleurs_asr_western_european", "results": []}]} | anton-l/xtreme_s_xlsr_300m_fleurs_asr_western_european | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"fleurs-asr",
"google/xtreme_s",
"generated_from_trainer",
"all",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T09:27:11+00:00 | [] | [
"all"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #fleurs-asr #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_fleurs\_asr\_western\_european
=====================================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - FLEURS.ALL dataset.
It achieves the following results on the evaluation set:
* Cer: 0.2484
* Cer Ast Es: 0.1598
* C... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 8\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #fleurs-asr #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
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-fr3
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsin... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type":... | Ghost1/marian-finetuned-kde4-en-to-fr3 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T09:55:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr3
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: 1.3274
- Bleu: 45.6906
## Model description
More information needed
## Intended uses & limitations
More information needed
## ... | [
"# marian-finetuned-kde4-en-to-fr3\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: 1.3274\n- Bleu: 45.6906",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore info... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr3\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner-TRANS
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner-TRANS", "results": []}]} | Lilya/distilbert-base-uncased-finetuned-ner-TRANS | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T10:44:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner-TRANS
===========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1053
* Precision: 0.7911
* Recall: 0.8114
* F1: 0.8011
* Accuracy: 0.9815
Mo... | [
"### 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: 12",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | ahmad573/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T10:53:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5827
* Wer: 0.4147
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-qmsum-meeting-summarization
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/ss... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["yawnick/QMSum"], "metrics": ["rouge"], "model-index": [{"name": "bart-qmsum-meeting-summarization", "results": []}]} | mikeadimech/bart-qmsum-meeting-summarization | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:yawnick/QMSum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T10:54:40+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-qmsum-meeting-summarization
================================
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the QMSum dataset.
It achieves the following results on the evaluation set:
* Loss: 4.3354
* Rouge1: 39.5539
* Rouge2: 12.1134
* Rougel: 23.9163
* Rougelsum: 36.0299
* Gen Len: 11... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #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: 3e-07\n* trai... |
text2text-generation | transformers |
# ๐ Keyphrase Generation Model: T5-small-inspec
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first ... | {"language": "en", "license": "mit", "tags": ["keyphrase-generation"], "datasets": ["midas/inspec"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly a... | ml6team/keyphrase-generation-t5-small-inspec | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keyphrase-generation",
"en",
"dataset:midas/inspec",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T11:37:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keyphrase-generation #en #dataset-midas/inspec #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Keyphrase Generation Model: T5-small-inspec
===========================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it com... | [
"### Limitations\n\n\n* This keyphrase generation model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.\n* Only works for English documents.\n* Sometimes the output doesn't make any sense... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keyphrase-generation #en #dataset-midas/inspec #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Limitations\n\n\n* This keyphrase generation model is very domain-specific and wi... |
null | null | Spacy 3.0 model to use within Spacy-Prodigy framework. Models were trained on G06K sub-directory of patents.
'spacy_output' - consist model after training.
'prodigy_output' - after manual active learning
For more info check: https://github.com/kinivi/patent_ner_linking | {"license": "apache-2.0"} | kinivi/ner_patent_g06k | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-04-27T12:23:02+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Spacy 3.0 model to use within Spacy-Prodigy framework. Models were trained on G06K sub-directory of patents.
'spacy_output' - consist model after training.
'prodigy_output' - after manual active learning
For more info check: URL | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
text-generation | transformers |
#Michael Scott DialoGPT Model | {"tags": ["conversational"]} | kvnaraya/DialoGPT-small-michael | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T12:45:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Michael Scott DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
This model is a fine-tuned version of [distilbert-base-uncased](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mnli-amazon-query-shopping", "results": []}]} | LiYuan/amazon-query-product-ranking | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-27T13:12:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
============================================================
This model is a fine-tuned version of distilbert-base-uncased on an Amazon shopping query dataset. The code for the fine-tuning process can be found
here. This model is uncased: it does
not make a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fb-data2vec-finetuned-finance-classification
This model is a fine-tuned version of [facebook/data2vec-text-base](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "fb-data2vec-finetuned-finance-classification", "results": []}]} | nickmuchi/facebook-data2vec-finetuned-finance-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T13:19:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| fb-data2vec-finetuned-finance-classification
============================================
This model is a fine-tuned version of facebook/data2vec-text-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8993
* Accuracy: 0.8557
* F1: 0.8563
* Precision: 0.8576
* Recall: 0.855... | [
"### 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: 15\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #data2vec-text #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch... |
automatic-speech-recognition | espnet |
## ESPnet2 model
This model was trained by Chaitanya Narisetty using recipe in [espnet](https://github.com/espnet/espnet/).
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Apr 27 09:30:57 EDT 2022`
- python version: `3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0]... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr", "librispeech 960h"]} | espnet/chai_librispeech_asr_train_conformer-rnn_transducer_raw_en_bpe5000_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-27T13:25:15+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 model
-------------
This model was trained by Chaitanya Narisetty using recipe in espnet.
RESULTS
=======
Environments
------------
* date: 'Wed Apr 27 09:30:57 EDT 2022'
* python version: '3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch... | [
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-French123
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model d... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-French123", "results": []}]} | stevems1/bert-base-uncased-French123 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T13:40:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-French123
This model is a fine-tuned version of [](URL on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
Th... | [
"# bert-base-uncased-French123\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-uncased-French123\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intende... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 793324440
- CO2 Emissions (in grams): 0.0025078722090032795
## Validation Metrics
- Loss: 0.31105440855026245
- Accuracy: 0.9473684210526315
- Precision: 0.9
- Recall: 1.0
- AUC: 0.9444444444444445
- F1: 0.9473684210526316
## Usage
... | {"language": "zh", "tags": "autotrain", "datasets": ["EAST/autotrain-data-Rule"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.0025078722090032795} | EAST/autotrain-Rule-793324440 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"zh",
"dataset:EAST/autotrain-data-Rule",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T13:56:53+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-EAST/autotrain-data-Rule #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 793324440
- CO2 Emissions (in grams): 0.0025078722090032795
## Validation Metrics
- Loss: 0.31105440855026245
- Accuracy: 0.9473684210526315
- Precision: 0.9
- Recall: 1.0
- AUC: 0.9444444444444445
- F1: 0.9473684210526316
## Usage
... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 793324440\n- CO2 Emissions (in grams): 0.0025078722090032795",
"## Validation Metrics\n\n- Loss: 0.31105440855026245\n- Accuracy: 0.9473684210526315\n- Precision: 0.9\n- Recall: 1.0\n- AUC: 0.9444444444444445\n- F1: 0.947368421... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-EAST/autotrain-data-Rule #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 793324440\n- CO2 Emissions (in grams): 0.00... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-wikitext2", "results": []}]} | Das282000Prit/bert-base-uncased-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T14:00:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-wikitext2
=====================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7295
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-cc25-finetuned-en-to-ko2
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/fa... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mbart-large-cc25-finetuned-en-to-ko2", "results": []}]} | obokkkk/mbart-large-cc25-finetuned-en-to-ko2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T14:00:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mbart-large-cc25-finetuned-en-to-ko2
This model is a fine-tuned version of facebook/mbart-large-cc25 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tr... | [
"# mbart-large-cc25-finetuned-en-to-ko2\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## ... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart-large-cc25-finetuned-en-to-ko2\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on the None dataset.",
"## Model description\n\nM... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 793224456
- CO2 Emissions (in grams): 27.26671996544415
## Validation Metrics
- Loss: 1.5189369916915894
- Rouge1: 38.7852
- Rouge2: 17.0785
- RougeL: 32.1082
- RougeLsum: 32.1103
- Gen Len: 18.7332
## Usage
You can use cURL to access this ... | {"language": "en", "tags": "autotrain", "datasets": ["faisalahmad2/autotrain-data-nlp-text-summarization-by-faisal"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 27.26671996544415} | faisalahmad2/autotrain-nlp-text-summarization-by-faisal-793224456 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"en",
"dataset:faisalahmad2/autotrain-data-nlp-text-summarization-by-faisal",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T14:03:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #en #dataset-faisalahmad2/autotrain-data-nlp-text-summarization-by-faisal #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 793224456
- CO2 Emissions (in grams): 27.26671996544415
## Validation Metrics
- Loss: 1.5189369916915894
- Rouge1: 38.7852
- Rouge2: 17.0785
- RougeL: 32.1082
- RougeLsum: 32.1103
- Gen Len: 18.7332
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 793224456\n- CO2 Emissions (in grams): 27.26671996544415",
"## Validation Metrics\n\n- Loss: 1.5189369916915894\n- Rouge1: 38.7852\n- Rouge2: 17.0785\n- RougeL: 32.1082\n- RougeLsum: 32.1103\n- Gen Len: 18.7332",
"## Usage\n\nYou can... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n-... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ADEs_model_1
This model is a fine-tuned version of [jsylee/scibert_scivocab_uncased-finetuned-ner](https://huggin... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ADEs_model_1", "results": []}]} | ajtamayoh/bert-finetuned-ADEs_model_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T14:07:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ADEs\_model\_1
=============================
This model is a fine-tuned version of jsylee/scibert\_scivocab\_uncased-finetuned-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1938
* Precision: 0.6759
* Recall: 0.6710
* F1: 0.6735
* Accuracy: 0.9132
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 8\n* eval\\_... |
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | YASH312312/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T14:07:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7515
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
token-classification | transformers | pytorch version of [jplu/tf-xlm-r-ner-40-lang](https://huggingface.co/jplu/tf-xlm-r-ner-40-lang)
| {} | nbroad/jplu-xlm-r-ner-40-lang | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T14:22:16+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| pytorch version of jplu/tf-xlm-r-ner-40-lang
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-960h-finetuned_common_voice2
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-960h-finetuned_common_voice2", "results": []}]} | obokkkk/wav2vec2-base-960h-finetuned_common_voice2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T14:50:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-960h-finetuned_common_voice2
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure... | [
"# wav2vec2-base-960h-finetuned_common_voice2\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-960h-finetuned_common_voice2\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model... |
null | null |
## text-classification
---
| {"license": "mit", "widget": [{"text": "I like you. </s></s> I love you."}]} | wypa93/hate_speech_detection | null | [
"license:mit",
"region:us"
] | null | 2022-04-27T14:58:43+00:00 | [] | [] | TAGS
#license-mit #region-us
|
## text-classification
---
| [
"## text-classification\n---"
] | [
"TAGS\n#license-mit #region-us \n",
"## text-classification\n---"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | wypa93/keras-dummy-sequential-demo | null | [
"keras",
"region:us"
] | null | 2022-04-27T15:46:48+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | wypa93/autoencoder-keras-mnist-demo | null | [
"keras",
"region:us"
] | null | 2022-04-27T16:12:33+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
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-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mnli", "results": []}]} | LiYuan/amazon-cross-encoder | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T17:06:28+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mnli
======================================
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: 0.8244
* Accuracy: 0.6617
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ArOCRv4
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following resul... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ArOCRv4", "results": []}]} | gagan3012/ArOCRv4 | null | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"generated_from_trainer",
"doi:10.57967/hf/0018",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T17:49:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #doi-10.57967/hf/0018 #endpoints_compatible #region-us
| ArOCRv4
=======
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5811
* Cer: 0.1249
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #doi-10.57967/hf/0018 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | davidenam/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T17:53:15+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2230
* Accuracy: 0.9205
* F1: 0.9203
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
null | null | The model's kernels etc. source code ==> https://github.com/NVlabs/stylegan3 | {"tags": ["brainMRI", "GAN", "medicalimaging", "pytorch"], "metrics": ["fid50k"]} | SerdarHelli/Brain-MRI-GAN | null | [
"brainMRI",
"GAN",
"medicalimaging",
"pytorch",
"region:us"
] | null | 2022-04-27T18:07:39+00:00 | [] | [] | TAGS
#brainMRI #GAN #medicalimaging #pytorch #region-us
| The model's kernels etc. source code ==> URL | [] | [
"TAGS\n#brainMRI #GAN #medicalimaging #pytorch #region-us \n"
] |
null | txtai |
# T5-small finedtuned to generate txtai SQL
[T5 small](https://huggingface.co/t5-small) fine-tuned to generate [txtai](https://github.com/neuml/txtai) SQL. This model takes [Bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)) like commands and builds txtai-compatible SQL statements.
```
find -name "feel good stor... | {"language": "en", "license": "apache-2.0", "library_name": "txtai", "widget": [{"text": "translate Bash to SQL: find -name \"feel good story\" -mtime -1", "example_title": "Last day"}, {"text": "translate Bash to SQL: find -name \"show me sports stories\" -mtime -1 -team \"Red Sox\"", "example_title": "Last day with f... | NeuML/t5-small-bashsql | null | [
"txtai",
"pytorch",
"t5",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-04-27T18:28:37+00:00 | [] | [
"en"
] | TAGS
#txtai #pytorch #t5 #en #license-apache-2.0 #region-us
|
# T5-small finedtuned to generate txtai SQL
T5 small fine-tuned to generate txtai SQL. This model takes Bash) like commands and builds txtai-compatible SQL statements.
## Custom query syntax
This model is an example of creating a custom query syntax that can be translated into SQL txtai can understand. Any query ... | [
"# T5-small finedtuned to generate txtai SQL\n\nT5 small fine-tuned to generate txtai SQL. This model takes Bash) like commands and builds txtai-compatible SQL statements.",
"## Custom query syntax\n\nThis model is an example of creating a custom query syntax that can be translated into SQL txtai can understand. ... | [
"TAGS\n#txtai #pytorch #t5 #en #license-apache-2.0 #region-us \n",
"# T5-small finedtuned to generate txtai SQL\n\nT5 small fine-tuned to generate txtai SQL. This model takes Bash) like commands and builds txtai-compatible SQL statements.",
"## Custom query syntax\n\nThis model is an example of creating a custo... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | zasheza/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T18:34:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
null | fastai |
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([docume... | {"tags": ["fastai"]} | dl4phys/lewtun-top-tagging-nsubs | null | [
"fastai",
"region:us"
] | null | 2022-04-27T18:38:15+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-base-tas-b
This is a port of the [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is op... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | iamholmes/english-phrases-bible | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T18:48:50+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/msmarco-distilbert-base-tas-b
This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy... | [
"# sentence-transformers/msmarco-distilbert-base-tas-b\n\nThis is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model be... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/msmarco-distilbert-base-tas-b\n\nThis is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentenc... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | bdickson/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T18:56:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1617
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetune
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetune", "results": []}]} | rdchambers/distilbert-base-uncased-finetune | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T19:00:43+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetune
================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0149
* Precision: 0.8458
* Recall: 0.8060
* F1: 0.8255
* Accuracy: 0.9954
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-shuffled_take1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-shuffled_take1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "defa... | chv5/t5-small-shuffled_take1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T19:27:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-shuffled\_take1
========================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1788
* Rouge1: 11.9641
* Rouge2: 10.5245
* Rougel: 11.5825
* Rougelsum: 11.842
* Gen Len: 18.9838
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-ADEs_model_2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large)... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-large-finetuned-ADEs_model_2", "results": []}]} | ajtamayoh/roberta-large-finetuned-ADEs_model_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T19:28:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-ADEs\_model\_2
======================================
This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2580
* Precision: 0.5407
* Recall: 0.6311
* F1: 0.5824
* Accuracy: 0.8897
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_si... |
null | null | This my Fatima Fellowship notebokk | {} | Elie/NLP_Challenge | null | [
"region:us"
] | null | 2022-04-27T19:36:46+00:00 | [] | [] | TAGS
#region-us
| This my Fatima Fellowship notebokk | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers | ## BART Scientific Definition Generation
This is a finetuned BART Large model from the paper:
"Generating Scientific Definitions with Controllable Complexity"
By Tal August, Katharina Reinecke, and Noah A. Smith
Abstract: Unfamiliar terminology and complex language can present barriers to understanding science. Na... | {} | talaugust/bart-sci-definition | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T21:32:11+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| ## BART Scientific Definition Generation
This is a finetuned BART Large model from the paper:
"Generating Scientific Definitions with Controllable Complexity"
By Tal August, Katharina Reinecke, and Noah A. Smith
Abstract: Unfamiliar terminology and complex language can present barriers to understanding science. Na... | [
"## BART Scientific Definition Generation \nThis is a finetuned BART Large model from the paper:\n\n\"Generating Scientific Definitions with Controllable Complexity\" \n\nBy Tal August, Katharina Reinecke, and Noah A. Smith\n\nAbstract: Unfamiliar terminology and complex language can present barriers to understandi... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"## BART Scientific Definition Generation \nThis is a finetuned BART Large model from the paper:\n\n\"Generating Scientific Definitions with Controllable Complexity\" \n\nBy Tal August, Katharina ... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1510917391533830145/XW-z... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/afraidofwasps-dril-senn_spud/1654636210975/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/afraidofwasps-dril-senn_spud | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-27T23:36:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wint & Will Sennett & Boots, 'with the fur'
@afraidofwasps-dril-senn\_spud
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was develope... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | bdickson/bert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-27T23:58:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1240
- eval_runtime: 262.7193
- eval_samples_per_second: 41.048
- eval_steps_per_second: 2.565
- epoch: 3.0
- step: 16599
## Mode... | [
"# bert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1240\n- eval_runtime: 262.7193\n- eval_samples_per_second: 41.048\n- eval_steps_per_second: 2.565\n- epoch: 3.0\n- step: 165... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.\nIt achieves the following res... |
image-classification | transformers |
# ALL
Autogenerated by HuggingPics๐ค๐ผ๏ธ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Ahmed9275/ALL | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T00:00:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# ALL
Autogenerated by HuggingPics๏ธ
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images | [
"# ALL\n\n\nAutogenerated by HuggingPics๏ธ\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# ALL\n\n\nAutogenerated by HuggingPics๏ธ\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the ... |
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. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]} | bdickson/albert-base-v2-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T00:10:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of albert-base-v2 on the squad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0191
- eval_runtime: 291.8551
- eval_samples_per_second: 37.032
- eval_steps_per_second: 2.316
- epoch: 3.0
- step: 16620
## Model desc... | [
"# albert-base-v2-finetuned-squad\n\nThis model is a fine-tuned version of albert-base-v2 on the squad dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0191\n- eval_runtime: 291.8551\n- eval_samples_per_second: 37.032\n- eval_steps_per_second: 2.316\n- epoch: 3.0\n- step: 16620",
... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# albert-base-v2-finetuned-squad\n\nThis model is a fine-tuned version of albert-base-v2 on the squad dataset.\nIt achieves the following results... |
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. -->
# Clickbait3
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multi... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Clickbait3", "results": []}]} | caush/Clickbait3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T00:53:58+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Clickbait3
==========
This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0248
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_... |
image-classification | transformers |
# ALL-2
Autogenerated by HuggingPics๐ค๐ผ๏ธ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Ahmed9275/ALL-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T01:07:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# ALL-2
Autogenerated by HuggingPics๏ธ
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images | [
"# ALL-2\n\n\nAutogenerated by HuggingPics๏ธ\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# ALL-2\n\n\nAutogenerated by HuggingPics๏ธ\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with th... |
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. -->
# Clickbait5
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- L... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Clickbait5", "results": []}]} | caush/Clickbait5 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T01:50:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Clickbait5
==========
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0258
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and ev... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-cc25-finetuned-en-to-ko2
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/fa... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mbart-large-cc25-finetuned-en-to-ko2", "results": []}]} | ToToKr/mbart-large-cc25-finetuned-en-to-ko2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T02:44:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mbart-large-cc25-finetuned-en-to-ko2
This model is a fine-tuned version of facebook/mbart-large-cc25 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tr... | [
"# mbart-large-cc25-finetuned-en-to-ko2\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## ... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart-large-cc25-finetuned-en-to-ko2\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on the None dataset.",
"## Model description\n\nM... |
text-generation | transformers |
# **UPDATE (2023-09-23):**
This model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. [See here](https://huggingface.co/Crataco/ggml-ai-dungeon-2-classic).
***
# AID-Neo-125M
## Model description
This model was inspired by -- and finetuned on the same dat... | {"language": "en", "license": "mit", "pipeline_tag": "text-generation"} | Crataco/AID-Neo-125M | null | [
"transformers",
"pytorch",
"safetensors",
"gpt_neo",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T02:48:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# UPDATE (2023-09-23):
This model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. See here.
*
# AID-Neo-125M
## Model description
This model was inspired by -- and finetuned on the same dataset of -- KoboldAI's GPT-Neo-125M-AID (Mia) model: the AI Dungeon... | [
"# UPDATE (2023-09-23):\nThis model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. See here.\n*",
"# AID-Neo-125M",
"## Model description\nThis model was inspired by -- and finetuned on the same dataset of -- KoboldAI's GPT-Neo-125M-AID (Mia) model... | [
"TAGS\n#transformers #pytorch #safetensors #gpt_neo #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# UPDATE (2023-09-23):\nThis model is obsolete. Thanks to quantization you can run AI Dungeon 2 Classic (a 1.5B model) under equivalent hardware. See here.\n*",
"# A... |
null | espnet |
## ESPnet2 EnhS2T model
### `espnet/simpleoier_chime4_enh_asr_train_enh_asr_convtasnet_fbank_transformer_raw_en_char`
This model was trained by simpleoier using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 44971ff962aae30c962226f1ba3d... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-enhancement-recognition"], "datasets": ["chime4"]} | espnet/simpleoier_chime4_enh_asr_train_enh_asr_convtasnet_fbank_transformer_raw_en_char | null | [
"espnet",
"audio",
"speech-enhancement-recognition",
"en",
"dataset:chime4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-28T03:22:14+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #speech-enhancement-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 EnhS2T model
--------------------
### 'espnet/simpleoier\_chime4\_enh\_asr\_train\_enh\_asr\_convtasnet\_fbank\_transformer\_raw\_en\_char'
This model was trained by simpleoier using chime4 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Apr 2... | [
"### 'espnet/simpleoier\\_chime4\\_enh\\_asr\\_train\\_enh\\_asr\\_convtasnet\\_fbank\\_transformer\\_raw\\_en\\_char'\n\n\nThis model was trained by simpleoier using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Apr 28 00:09:17... | [
"TAGS\n#espnet #audio #speech-enhancement-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/simpleoier\\_chime4\\_enh\\_asr\\_train\\_enh\\_asr\\_convtasnet\\_fbank\\_transformer\\_raw\\_en\\_char'\n\n\nThis model was trained by simpleoier using chime4 recipe in esp... |
text2text-generation | transformers |
# How to use
```python3
from transformers import MT5Tokenizer, MT5ForConditionalGeneration
tokenizer = MT5Tokenizer.from_pretrained('juierror/thai-news-summarization')
model = MT5ForConditionalGeneration.from_pretrained('juierror/thai-news-summarization')
text = "some news with head line"
tokenized_text = tokenize... | {"language": "th", "license": "mit", "datasets": ["thaisum"], "widget": [{"text": "some news with head line"}]} | juierror/thai-news-summarization | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"th",
"dataset:thaisum",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T04:00:01+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #th #dataset-thaisum #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# How to use
| [
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"# How to use"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Los... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mt5-base", "results": []}]} | obokkkk/mt5-base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T04:42:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base
========
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2760
* Bleu: 8.6707
* Gen Len: 16.9319
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 256\n* total\\_train\\_batch\\_size: 2048\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_... |
text-generation | transformers |
Grepp KDT AI 3๊ธฐ ๊ณผ์ ํ๋ก์ ํธ.
[SKT-AI/KoGPT2](https://github.com/SKT-AI/KoGPT2) ๋ชจ๋ธ์ ๊ธฐ๋ฐ. ๋ชจ๋์ ๋ง๋ญ์น์ 2021 ๋ด์ค ๋ง๋ญ์น๋ฅผ ์ถ๊ฐ๋ก ์ธ์ด๋ชจ๋ธ๋ง ํ์ต ํ, 5๋ ์ผ๊ฐ์ง(์กฐ์ ์ผ๋ณด, ์ค์์ผ๋ณด, ๋์์ผ๋ณด, ํ๊ฒจ๋ , ๊ฒฝํฅ์ ๋ฌธ)๋ณ ๊ฐ ๋ง์ฌ๊ฐ์ ์ฌ์ค๋ก ๋ฏธ์ธ์กฐ์ ํ์์.
๋งค์ผ ๋ฐฑ์ฌ๊ฐ์ ์ฌ์ค๋ก ์ถ๊ฐ ๋ฏธ์ธ์กฐ์ ํ์ฌ ์ต์ ์ ์น์ ์ด์์ ๊ดํ ํ
์คํธ๋ ์ ์์ฑํจ.
| {"license": "apache-2.0"} | A2/kogpt2-taf | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T04:45:19+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Grepp KDT AI 3๊ธฐ ๊ณผ์ ํ๋ก์ ํธ.
SKT-AI/KoGPT2 ๋ชจ๋ธ์ ๊ธฐ๋ฐ. ๋ชจ๋์ ๋ง๋ญ์น์ 2021 ๋ด์ค ๋ง๋ญ์น๋ฅผ ์ถ๊ฐ๋ก ์ธ์ด๋ชจ๋ธ๋ง ํ์ต ํ, 5๋ ์ผ๊ฐ์ง(์กฐ์ ์ผ๋ณด, ์ค์์ผ๋ณด, ๋์์ผ๋ณด, ํ๊ฒจ๋ , ๊ฒฝํฅ์ ๋ฌธ)๋ณ ๊ฐ ๋ง์ฌ๊ฐ์ ์ฌ์ค๋ก ๋ฏธ์ธ์กฐ์ ํ์์.
๋งค์ผ ๋ฐฑ์ฌ๊ฐ์ ์ฌ์ค๋ก ์ถ๊ฐ ๋ฏธ์ธ์กฐ์ ํ์ฌ ์ต์ ์ ์น์ ์ด์์ ๊ดํ ํ
์คํธ๋ ์ ์์ฑํจ.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Das282000Prit/fyp-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Das282000Prit/fyp-finetuned-imdb", "results": []}]} | Das282000Prit/fyp-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T04:46:39+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Das282000Prit/fyp-finetuned-imdb
================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8566
* Validation Loss: 2.6019
* Epoch: 0
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-960h-finetuned_common_voice3
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-960h-finetuned_common_voice3", "results": []}]} | obokkkk/wav2vec2-base-960h-finetuned_common_voice3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T04:57:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-960h-finetuned_common_voice3
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure... | [
"# wav2vec2-base-960h-finetuned_common_voice3\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-960h-finetuned_common_voice3\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.",
"## Model... |
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. -->
# electra-small-discriminator-finetuned-squad-finetuned-squad
This model is a fine-tuned version of [bdickson/electra-small-discri... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "electra-small-discriminator-finetuned-squad-finetuned-squad", "results": []}]} | bdickson/electra-small-discriminator-finetuned-squad-finetuned-squad | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T05:16:38+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# electra-small-discriminator-finetuned-squad-finetuned-squad
This model is a fine-tuned version of bdickson/electra-small-discriminator-finetuned-squad on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mor... | [
"# electra-small-discriminator-finetuned-squad-finetuned-squad\n\nThis model is a fine-tuned version of bdickson/electra-small-discriminator-finetuned-squad on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"# electra-small-discriminator-finetuned-squad-finetuned-squad\n\nThis model is a fine-tuned version of bdickson/electra-small-discriminator-finetuned-squad on the squad dataset.... |
null | transformers |
# OFA-tiny
## Introduction
This is the **tiny** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple... | {"license": "apache-2.0"} | OFA-Sys/ofa-tiny | null | [
"transformers",
"pytorch",
"ofa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T05:16:45+00:00 | [] | [] | TAGS
#transformers #pytorch #ofa #license-apache-2.0 #endpoints_compatible #region-us
|
# OFA-tiny
## Introduction
This is the tiny version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple seq... | [
"# OFA-tiny",
"## Introduction\nThis is the tiny version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a s... | [
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"## Introduction\nThis is the tiny version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene... |
null | null |
# ResNet-50
## Model Description
ResNet-50 model from [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) paper.
## Original implementation
Follow [this link](https://huggingface.co/microsoft/resnet-50) to see the original implementation.
# How to use
You can use the `base` model tha... | {"language": ["en"], "tags": ["ResNet-50"]} | OWG/resnet-50 | null | [
"onnx",
"ResNet-50",
"en",
"arxiv:1512.03385",
"region:us"
] | null | 2022-04-28T05:22:56+00:00 | [
"1512.03385"
] | [
"en"
] | TAGS
#onnx #ResNet-50 #en #arxiv-1512.03385 #region-us
|
# ResNet-50
## Model Description
ResNet-50 model from Deep Residual Learning for Image Recognition paper.
## Original implementation
Follow this link to see the original implementation.
# How to use
You can use the 'base' model that returns 'last_hidden_state'.
Or you can use the model with classification head... | [
"# ResNet-50",
"## Model Description\n\nResNet-50 model from Deep Residual Learning for Image Recognition paper.",
"## Original implementation\n\nFollow this link to see the original implementation.",
"# How to use\n\nYou can use the 'base' model that returns 'last_hidden_state'.\n\n\nOr you can use the model... | [
"TAGS\n#onnx #ResNet-50 #en #arxiv-1512.03385 #region-us \n",
"# ResNet-50",
"## Model Description\n\nResNet-50 model from Deep Residual Learning for Image Recognition paper.",
"## Original implementation\n\nFollow this link to see the original implementation.",
"# How to use\n\nYou can use the 'base' model... |
text-generation | transformers |
# Hyperdrive DialoGPT Model | {"tags": ["conversational"]} | Hyperspace/DialoGPT-small-Hyperdrive | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T06:07:11+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Hyperdrive DialoGPT Model | [
"# Hyperdrive DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Hyperdrive DialoGPT Model"
] |
null | transformers |
# OFA-medium
## Introduction
This is the **medium** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a si... | {"license": "apache-2.0"} | OFA-Sys/ofa-medium | null | [
"transformers",
"pytorch",
"ofa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T06:13:38+00:00 | [] | [] | TAGS
#transformers #pytorch #ofa #license-apache-2.0 #endpoints_compatible #region-us
|
# OFA-medium
## Introduction
This is the medium version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple... | [
"# OFA-medium",
"## Introduction\nThis is the medium version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to... | [
"TAGS\n#transformers #pytorch #ofa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# OFA-medium",
"## Introduction\nThis is the medium version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image ... |
text-generation | transformers | # What is SamSum Bot?
This is a model fine-tuned on the [SamSum dataset](https://huggingface.co/datasets/samsum).
However, instead of training the system to summarize conversations, the model is trained to predict a conversation given a summary.
The prompt needs to be in the following form
```python
A partial summary ... | {} | fractalego/samsumbot | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T06:14:15+00:00 | [
"2106.09685"
] | [] | TAGS
#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #autotrain_compatible #endpoints_compatible #region-us
| # What is SamSum Bot?
This is a model fine-tuned on the SamSum dataset.
However, instead of training the system to summarize conversations, the model is trained to predict a conversation given a summary.
The prompt needs to be in the following form
where *{summary}* is a text as in
and the *{dialogue}* needs to be... | [
"# What is SamSum Bot?\nThis is a model fine-tuned on the SamSum dataset.\nHowever, instead of training the system to summarize conversations, the model is trained to predict a conversation given a summary. \nThe prompt needs to be in the following form\n\n\nwhere *{summary}* is a text as in\n\n\nand the *{dialogue... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #autotrain_compatible #endpoints_compatible #region-us \n",
"# What is SamSum Bot?\nThis is a model fine-tuned on the SamSum dataset.\nHowever, instead of training the system to summarize conversations, the model is trained to predict a conver... |
null | transformers |
# OFA-base
## Introduction
This is the **base** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple... | {"license": "apache-2.0"} | OFA-Sys/ofa-base | null | [
"transformers",
"pytorch",
"ofa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T06:27:45+00:00 | [] | [] | TAGS
#transformers #pytorch #ofa #license-apache-2.0 #endpoints_compatible #region-us
|
# OFA-base
## Introduction
This is the base version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple seq... | [
"# OFA-base",
"## Introduction\nThis is the base version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a s... | [
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"# OFA-base",
"## Introduction\nThis is the base version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m2m100_418M-finetuned-en-to-ko
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m1... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "m2m100_418M-finetuned-en-to-ko", "results": []}]} | hyerin/m2m100_418M-finetuned-en-to-ko | null | [
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T06:31:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| m2m100\_418M-finetuned-en-to-ko
===============================
This model is a fine-tuned version of facebook/m2m100\_418M on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 256\n* total\\_train\\_batch\\_size: 2048\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
null | transformers |
# OFA-large
## Introduction
This is the **large** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simp... | {"license": "apache-2.0"} | OFA-Sys/ofa-large | null | [
"transformers",
"pytorch",
"ofa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T06:41:55+00:00 | [] | [] | TAGS
#transformers #pytorch #ofa #license-apache-2.0 #endpoints_compatible #region-us
|
# OFA-large
## Introduction
This is the large version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple s... | [
"# OFA-large",
"## Introduction\nThis is the large version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a... | [
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"## Introduction\nThis is the large version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image ge... |
null | null |
Site Reliability Engineering
---
language: en
thumbnail: http://www.huggingtweets.com/slime_machine/1640253262516/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-l... | {} | GuillaumeSalouHF/slime-test | null | [
"region:us"
] | null | 2022-04-28T07:20:08+00:00 | [] | [] | TAGS
#region-us
| Site Reliability Engineering
----------------------------
language: en
thumbnail: URL
tags:
* huggingtweets
widget:
* text: "My dream is"
---
AI BOT
rich homie cron
@slime\_machine
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
---... | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
# wav2vec2-large-slavic-parlaspeech-hr
This model for Croatian ASR is based on the [facebook/wav2vec2-large-slavic-voxpopuli-v2 model](https://huggingface.co/facebook/wav2vec2-large-slavic-voxpopuli-v2) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpee... | {"language": "hr", "tags": ["audio", "automatic-speech-recognition", "parlaspeech"], "datasets": ["parlaspeech-hr"], "widget": [{"example_title": "example 1", "src": "https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a"}, {"example_title": "example 2", "src": "https://huggingface.co/classla/w... | classla/wav2vec2-large-slavic-parlaspeech-hr | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"parlaspeech",
"hr",
"dataset:parlaspeech-hr",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T08:39:31+00:00 | [] | [
"hr"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us
| wav2vec2-large-slavic-parlaspeech-hr
====================================
This model for Croatian ASR is based on the facebook/wav2vec2-large-slavic-voxpopuli-v2 model and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset ParlaSpeech-HR v1.0.
If you use this model,... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on the co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "ar... | vegetable/test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T09:12:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| test
====
This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7372
* Precision: 0.7696
* Recall: 0.8396
* F1: 0.8031
* Accuracy: 0.8847
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_300m_fleurs_asr_en_us
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"language": ["en_us"], "license": "apache-2.0", "tags": ["fleurs-asr", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "model-index": [{"name": "xtreme_s_xlsr_300m_fleurs_asr_en_us", "results": []}]} | anton-l/xtreme_s_xlsr_300m_fleurs_asr_en_us | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"fleurs-asr",
"google/xtreme_s",
"generated_from_trainer",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T09:45:25+00:00 | [] | [
"en_us"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #fleurs-asr #google/xtreme_s #generated_from_trainer #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_fleurs\_asr\_en\_us
==========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - FLEURS.EN\_US dataset.
It achieves the following results on the evaluation set:
* Cer: 0.1356
* Loss: 0.5599
* Wer: 0.3148
* Predict Samp... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 8\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #fleurs-asr #google/xtreme_s #generated_from_trainer #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-parlaspeech-hr-lm
This model for Croatian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpeech-HR v1.0](http://hdl.handle.net/... | {"language": "hr", "tags": ["audio", "automatic-speech-recognition", "parlaspeech"], "datasets": ["parlaspeech-hr"], "widget": [{"example_title": "example 1", "src": "https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr-lm/raw/main/1800.m4a"}, {"example_title": "example 2", "src": "https://huggingface.co/classl... | classla/wav2vec2-xls-r-parlaspeech-hr-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"parlaspeech",
"hr",
"dataset:parlaspeech-hr",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T10:31:35+00:00 | [] | [
"hr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us
| wav2vec2-xls-r-parlaspeech-hr-lm
================================
This model for Croatian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset ParlaSpeech-HR v1.0.
If you use this model, please cite the follow... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | icity/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T10:37:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #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 None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6022
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #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\\_siz... |
text2text-generation | transformers | --- |-
Model card metadata documentation and specifications moved to https://github.com/huggingface/huggingface_hub/
The canonical documentation about model cards is now located at https://huggingface.co/docs/hub/model-repos and you can open a PR to improve the docs in the same repository https://github.com/hugging... | {} | pfactorial/checkpoint-50-epoch-2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T10:59:51+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| --- |-
Model card metadata documentation and specifications moved to URL
The canonical documentation about model cards is now located at URL and you can open a PR to improve the docs in the same repository URL
You can also find a spec of the metadata at URL
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# ByT5-base for Finnish
Pretrained ByT5 model on Finnish language using a span-based masked language modeling (MLM) objective. ByT5 was introduced in
[this paper](https://arxiv.org/abs/2105.13626)
and first released at [this page](https://github.com/google-research/byt5).
**Note:** The Hugging Face inference widget ... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "t5", "byt5", "t5x", "seq2seq"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "inference": false} | Finnish-NLP/byt5-base-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"finnish",
"byt5",
"t5x",
"seq2seq",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:2105.13626",
"arxiv:2002.05202",
"license:apache-2.0",
"autotrain_compatible",
"text-generatio... | null | 2022-04-28T11:16:03+00:00 | [
"2105.13626",
"2002.05202"
] | [
"fi"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #byt5 #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-2105.13626 #arxiv-2002.05202 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| ByT5-base for Finnish
=====================
Pretrained ByT5 model on Finnish language using a span-based masked language modeling (MLM) objective. ByT5 was introduced in
this paper
and first released at this page.
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tu... | [
"### How to use\n\n\nNote: ByT5 works on raw UTF-8 bytes and can be used without a tokenizer. For batched inference & training it is however recommended using a tokenizer class for padding.\n\n\nHere is how to use this model in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data us... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #byt5 #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-2105.13626 #arxiv-2002.05202 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nNote: ByT5... |
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... | Rerare/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T11:36:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7643
* Matthews Correlation: 0.5291
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | espnet |
## ESPnet2 EnhS2T model
### `espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char`
This model was trained by simpleoier using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 2b663318cd1773fb8685b1... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-enhancement-recognition"], "datasets": ["chime4"]} | espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char | null | [
"espnet",
"audio",
"speech-enhancement-recognition",
"en",
"dataset:chime4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-28T11:38:58+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #speech-enhancement-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 EnhS2T model
--------------------
### 'espnet/simpleoier\_chime4\_enh\_asr\_convtasnet\_init\_noenhloss\_wavlm\_transformer\_init\_raw\_en\_char'
This model was trained by simpleoier using chime4 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu... | [
"### 'espnet/simpleoier\\_chime4\\_enh\\_asr\\_convtasnet\\_init\\_noenhloss\\_wavlm\\_transformer\\_init\\_raw\\_en\\_char'\n\n\nThis model was trained by simpleoier using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Apr 28 08... | [
"TAGS\n#espnet #audio #speech-enhancement-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/simpleoier\\_chime4\\_enh\\_asr\\_convtasnet\\_init\\_noenhloss\\_wavlm\\_transformer\\_init\\_raw\\_en\\_char'\n\n\nThis model was trained by simpleoier using chime4 recipe ... |
summarization | 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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | Ghost1/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-28T11:55:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0282
* Rouge1: 17.629
* Rouge2: 8.5256
* Rougel: 17.1329
* Rougelsum: 17.1403
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
automatic-speech-recognition | transformers |
# wav2vec2-large-slavic-parlaspeech-hr-lm
This model for Croatian ASR is based on the [facebook/wav2vec2-large-slavic-voxpopuli-v2 model](https://huggingface.co/facebook/wav2vec2-large-slavic-voxpopuli-v2) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaS... | {"language": "hr", "tags": ["audio", "automatic-speech-recognition", "parlaspeech"], "datasets": ["parlaspeech-hr"], "widget": [{"example_title": "example 1", "src": "https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a"}, {"example_title": "example 2", "src": "https://huggingface.co/classla/w... | classla/wav2vec2-large-slavic-parlaspeech-hr-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"parlaspeech",
"hr",
"dataset:parlaspeech-hr",
"endpoints_compatible",
"region:us"
] | null | 2022-04-28T11:56:15+00:00 | [] | [
"hr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us
| wav2vec2-large-slavic-parlaspeech-hr-lm
=======================================
This model for Croatian ASR is based on the facebook/wav2vec2-large-slavic-voxpopuli-v2 model and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset ParlaSpeech-HR v1.0 and enhanced with a... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# IT5 Cased Small Efficient EL32 for Formal-to-informal Style Transfer ๐ค
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el... | {"language": ["it"], "license": "apache-2.0", "tags": ["italian", "sequence-to-sequence", "style-transfer", "efficient", "formality-style-transfer"], "datasets": ["yahoo/xformal_it"], "metrics": ["rouge", "bertscore"], "widget": [{"text": "Questa performance \u00e8 a dir poco spiacevole."}, {"text": "In attesa di un Su... | it5/it5-efficient-small-el32-formal-to-informal | null | [
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"formality-style-transfer",
"it",
"dataset:yahoo/xformal_it",
"arxiv:2203.03759",
"arxiv:2109.10686",
"license:apache-2.0",
"model-... | null | 2022-04-28T12:29:43+00:00 | [
"2203.03759",
"2109.10686"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #style-transfer #efficient #formality-style-transfer #it #dataset-yahoo/xformal_it #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-... |
# IT5 Cased Small Efficient EL32 for Formal-to-informal Style Transfer
*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32
model fine-tuned on Formal-to-informal style transfer on the Italian subset of the X... | [
"# IT5 Cased Small Efficient EL32 for Formal-to-informal Style Transfer \n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32\n model fine-tuned on Formal-to-informal style transfer on the Italian subset... | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #style-transfer #efficient #formality-style-transfer #it #dataset-yahoo/xformal_it #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-gener... |
text2text-generation | transformers |
# IT5 Cased Small Efficient EL32 for Informal-to-formal Style Transfer ๐ง
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el... | {"language": ["it"], "license": "apache-2.0", "tags": ["italian", "sequence-to-sequence", "style-transfer", "efficient", "formality-style-transfer"], "datasets": ["yahoo/xformal_it"], "metrics": ["rouge", "bertscore"], "widget": [{"text": "maronn qualcuno mi spieg' CHECCOSA SUCCEDE?!?!"}, {"text": "wellaaaaaaa, ma frat... | it5/it5-efficient-small-el32-informal-to-formal | null | [
"transformers",
"pytorch",
"tf",
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"tensorboard",
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"it",
"dataset:yahoo/xformal_it",
"arxiv:2203.03759",
"arxiv:2109.10686",
"license:apache-2.0",
"model-... | null | 2022-04-28T12:48:32+00:00 | [
"2203.03759",
"2109.10686"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #style-transfer #efficient #formality-style-transfer #it #dataset-yahoo/xformal_it #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-... |
# IT5 Cased Small Efficient EL32 for Informal-to-formal Style Transfer
*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on Informal-to-formal style transfer on the Italian subset of the XF... | [
"# IT5 Cased Small Efficient EL32 for Informal-to-formal Style Transfer \n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on Informal-to-formal style transfer on the Italian subset o... | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #style-transfer #efficient #formality-style-transfer #it #dataset-yahoo/xformal_it #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-gener... |
text2text-generation | transformers | # IT5 Cased Small Efficient EL32 for News Headline Generation ๐๏ธ ๐ฎ๐น
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) ... | {"language": ["it"], "license": "apache-2.0", "tags": ["italian", "sequence-to-sequence", "newspaper", "ilgiornale", "repubblica", "efficient", "headline-generation"], "datasets": ["gsarti/change_it"], "metrics": ["rouge", "bertscore"], "widget": [{"text": "WASHINGTON - La Corea del Nord torna dopo nove anni nella blac... | it5/it5-efficient-small-el32-headline-generation | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"newspaper",
"ilgiornale",
"repubblica",
"efficient",
"headline-generation",
"it",
"dataset:gsarti/change_it",
"arxiv:2203.03759",
"arxiv:2109.10686",
"license:... | null | 2022-04-28T13:11:12+00:00 | [
"2203.03759",
"2109.10686"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #newspaper #ilgiornale #repubblica #efficient #headline-generation #it #dataset-gsarti/change_it #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #te... | # IT5 Cased Small Efficient EL32 for News Headline Generation ๏ธ ๐ฎ๐น
*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news headline generation on the Italian HeadGen-IT dataset as part of... | [
"# IT5 Cased Small Efficient EL32 for News Headline Generation ๏ธ ๐ฎ๐น\n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news headline generation on the Italian HeadGen-IT dataset a... | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #newspaper #ilgiornale #repubblica #efficient #headline-generation #it #dataset-gsarti/change_it #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatib... |
summarization | transformers | # IT5 Cased Small Efficient EL32 for News Summarization โ๏ธ๐๏ธ ๐ฎ๐น
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) mode... | {"language": ["it"], "license": "apache-2.0", "tags": ["italian", "sequence-to-sequence", "fanpage", "efficient", "ilpost", "summarization"], "datasets": ["ARTeLab/fanpage", "ARTeLab/ilpost"], "metrics": ["rouge", "bertscore"], "widget": [{"text": "Non lo vuole sposare. E\u2019 quanto emerge all\u2019interno dell\u2019... | it5/it5-efficient-small-el32-news-summarization | null | [
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"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"italian",
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"ilpost",
"summarization",
"it",
"dataset:ARTeLab/fanpage",
"dataset:ARTeLab/ilpost",
"arxiv:2203.03759",
"arxiv:2109.10686",
"license:a... | null | 2022-04-28T13:11:32+00:00 | [
"2203.03759",
"2109.10686"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #fanpage #efficient #ilpost #summarization #it #dataset-ARTeLab/fanpage #dataset-ARTeLab/ilpost #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has... | # IT5 Cased Small Efficient EL32 for News Summarization ๏ธ๏ธ ๐ฎ๐น
*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news summarization on the Fanpage and Il Post corpora as part of the exper... | [
"# IT5 Cased Small Efficient EL32 for News Summarization ๏ธ๏ธ ๐ฎ๐น\n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on news summarization on the Fanpage and Il Post corpora as part of ... | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #italian #sequence-to-sequence #fanpage #efficient #ilpost #summarization #it #dataset-ARTeLab/fanpage #dataset-ARTeLab/ilpost #arxiv-2203.03759 #arxiv-2109.10686 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatibl... |
text2text-generation | transformers | # IT5 Cased Small Efficient EL32 for Question Answering โ๏ธ ๐ฎ๐น
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) model f... | {"language": ["it"], "license": "apache-2.0", "tags": ["Italian", "efficient", "sequence-to-sequence", "squad_it", "text2text-question-answering", "text2text-generation"], "datasets": ["squad_it"], "metrics": ["f1", "exact-match"], "widget": [{"text": "In seguito all' evento di estinzione del Cretaceo-Paleogene, l' est... | it5/it5-efficient-small-el32-question-answering | null | [
"transformers",
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"tf",
"jax",
"tensorboard",
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"arxiv:2203.03759",
"license:apache-2.0",
"model-index",
"autotrain_compatible"... | null | 2022-04-28T13:11:55+00:00 | [
"2203.03759"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #Italian #efficient #sequence-to-sequence #squad_it #text2text-question-answering #it #dataset-squad_it #arxiv-2203.03759 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # IT5 Cased Small Efficient EL32 for Question Answering โ๏ธ ๐ฎ๐น
*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on extractive question answering on the SQuAD-IT corpus as part of the experi... | [
"# IT5 Cased Small Efficient EL32 for Question Answering โ๏ธ ๐ฎ๐น\n\n*Shout-out to Stefan Schweter for contributing the pre-trained efficient model!*\n\nThis repository contains the checkpoint for the IT5 Cased Small Efficient EL32 model fine-tuned on extractive question answering on the SQuAD-IT corpus as part of t... | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #Italian #efficient #sequence-to-sequence #squad_it #text2text-question-answering #it #dataset-squad_it #arxiv-2203.03759 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \... |
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