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translation | transformers | # opus-mt-tc-big-tr-en
Neural machine translation model for translating from Turkish (tr) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "tr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-tr-en", "results": [{"task": {"type": "translation", "name": "Translation tur-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "tur eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-tr-en | null | [
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"autotrain_compatible",
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] | null | 2022-04-13T16:02:58+00:00 | [] | [
"en",
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #tr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-tr-en
====================
Neural machine translation model for translating from Turkish (tr) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #tr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-zls-en
Neural machine translation model for translating from South Slavic languages (zls) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in th... | {"language": ["bg", "bs", "en", "hr", "mk", "sh", "sl", "sr", "zls"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zls-en", "results": [{"task": {"type": "translation", "name": "Translation bul-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101... | Helsinki-NLP/opus-mt-tc-big-zls-en | null | [
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"region:u... | null | 2022-04-13T16:12:36+00:00 | [] | [
"bg",
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"hr",
"mk",
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"sr",
"zls"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #bs #en #hr #mk #sh #sl #sr #zls #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zls-en
=====================
Neural machine translation model for translating from South Slavic languages (zls) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models a... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #bs #en #hr #mk #sh #sl #sr #zls #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | atomsspawn/DialoGPT-medium-dumbledore | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T16:16:57+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
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"# Harry Potter DialoGPT Model"
] |
translation | transformers | # opus-mt-tc-big-zlw-en
Neural machine translation model for translating from West Slavic languages (zlw) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["cs", "dsb", "en", "hsb", "pl", "zlw"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zlw-en", "results": [{"task": {"type": "translation", "name": "Translation ces-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ces ... | Helsinki-NLP/opus-mt-tc-big-zlw-en | null | [
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"zlw",
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"model-index",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T16:19:58+00:00 | [] | [
"cs",
"dsb",
"en",
"hsb",
"pl",
"zlw"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #cs #dsb #en #hsb #pl #zlw #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zlw-en
=====================
Neural machine translation model for translating from West Slavic languages (zlw) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #cs #dsb #en #hsb #pl #zlw #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-CUAD-IE
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-CUAD-IE", "results": []}]} | Gam/distilbert-base-uncased-finetuned-CUAD-IE | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T16:20:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-CUAD-IE
=========================================
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: 0.0108
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-CPV_Spanish
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/Plan... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "roberta-finetuned-CPV_Spanish", "results": []}]} | htufgg/roberta-finetuned-CPV_Spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T16:43:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-finetuned-CPV\_Spanish
==============================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0422
* F1: 0.7739
* Roc Auc: 0.8704
* Accuracy: 0.7201
* Coverage Error: 11.5798
* Label Ranking ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
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... | flood/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 | 2022-04-13T16:46:31+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: 0.1344
* F1: 0.8634
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_TitleWithOpinion_Attraction
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_TitleWithOpinion_Attraction", "results": []}]} | javilonso/Mex_Rbta_TitleWithOpinion_Attraction | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T16:46:47+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_TitleWithOpinion\_Attraction
=================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0064
* Validation Loss: 0.0515
* Epoch: 2
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 8979, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | Tianle/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 | 2022-04-13T16:56:19+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: 1.2169
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
unconditional-image-generation | transformers |
# Hugging NFT: mini-mutants
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/mini-mutants)... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/mini-mutants"]} | huggingnft/mini-mutants | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/mini-mutants",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T17:09:55+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/mini-mutants #license-mit #endpoints_compatible #has_space #region-us
|
# Hugging NFT: mini-mutants
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: li... | [
"# Hugging NFT: mini-mutants",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available h... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/mini-mutants #license-mit #endpoints_compatible #has_space #region-us \n",
"# Hugging NFT: mini-mutants",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed ... |
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. -->
# test_model1.2_updated
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/op... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "test_model1.2_updated", "results": []}]} | kabelomalapane/test_model1.2_updated | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T17:11:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test_model1.2_updated
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6856
- Bleu: 12.3864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training ... | [
"# test_model1.2_updated\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6856\n- Bleu: 12.3864",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test_model1.2_updated\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on the None dataset.\nIt achie... |
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": []}]} | Adrian/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 | 2022-04-13T17:33:41+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: 1.1484
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... |
text-classification | transformers | ## BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashi... | {} | Seethal/sentiment_analysis_generic_dataset | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T17:37:07+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashi... | [
"## BERT base model (uncased)\nPretrained model on English language using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.",
"## Model description\nBERT is a transformers model pretrained on a large corpus of English data in a self-super... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## BERT base model (uncased)\nPretrained model on English language using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between e... |
unconditional-image-generation | transformers |
# Hugging NFT: nftrex
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/nftrex).
Dataset i... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/nftrex"]} | huggingnft/nftrex | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/nftrex",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T17:41:07+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/nftrex #license-mit #endpoints_compatible #has_space #region-us
|
# Hugging NFT: nftrex
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link.
P... | [
"# Hugging NFT: nftrex",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available here.\n... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/nftrex #license-mit #endpoints_compatible #has_space #region-us \n",
"# Hugging NFT: nftrex",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the sit... |
unconditional-image-generation | transformers |
# Hugging NFT: dooggies
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/dooggies).
Datas... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/dooggies"]} | huggingnft/dooggies | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/dooggies",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T17:44:08+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/dooggies #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: dooggies
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link.
... | [
"# Hugging NFT: dooggies",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available here.... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/dooggies #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: dooggies",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at th... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_Opinion_Augmented_Attraction
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_Opinion_Augmented_Attraction", "results": []}]} | javilonso/Mex_Rbta_Opinion_Augmented_Attraction | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T17:50:01+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_Opinion\_Augmented\_Attraction
===================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0078
* Validation Loss: 0.0606
* Epoch: 2
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11565, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# KakkiDaisuki/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "KakkiDaisuki/bert-finetuned-ner", "results": []}]} | KakkiDaisuki/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T18:03:50+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| KakkiDaisuki/bert-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0259
* Validation Loss: 0.0580
* Epoch: 2
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_Opinion_Augmented_Polarity
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_Opinion_Augmented_Polarity", "results": []}]} | javilonso/Mex_Rbta_Opinion_Augmented_Polarity | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T19:16:54+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_Opinion\_Augmented\_Polarity
=================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6885
* Validation Loss: 0.6118
* Epoch: 0
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7710, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ClaireV/MLMA_Lab8
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ClaireV/MLMA_Lab8", "results": []}]} | ClaireV/MLMA_Lab8 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T19:33:42+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ClaireV/MLMA\_Lab8
==================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0232
* Validation Loss: 0.0598
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-urdu-colab-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-urdu-colab-cv8", "results": []}]} | omar47/wav2vec2-large-xls-r-300m-urdu-colab-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T19:46:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-urdu-colab-cv8
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4651
* Wer: 0.7
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 20220413-210552
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "20220413-210552", "results": []}]} | lilitket/20220413-210552 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T20:06:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| 20220413-210552
===============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0348
* Wer: 1.0006
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 1\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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\... |
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/1448393533921112064/q3fC... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/kc_lyricbot/1649884470723/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/kc_lyricbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T20:12:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
King Crimson Lyric Bot
@kc\_lyricbot
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 d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | cj-mills/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T20:50:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7796
* Accuracy: 0.9161
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_Opinion_Attraction
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/Pl... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_Opinion_Attraction", "results": []}]} | javilonso/Mex_Rbta_Opinion_Attraction | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T20:56:03+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_Opinion\_Attraction
========================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0061
* Validation Loss: 0.0386
* Epoch: 2
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 8979, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
sentence-similarity | sentence-transformers |
# use-cmlm-multilingual
This is a pytorch version of the [universal-sentence-encoder-cmlm/multilingual-base-br](https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-base-br/1) model. It can be used to map 109 languages to a shared vector space. As the model is based [LaBSE](https://huggingface.co/sent... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/use-cmlm-multilingual | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T21:06:49+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# use-cmlm-multilingual
This is a pytorch version of the universal-sentence-encoder-cmlm/multilingual-base-br model. It can be used to map 109 languages to a shared vector space. As the model is based LaBSE, it perform quite comparable on downstream tasks.
## Usage (Sentence-Transformers)
Using this model becomes e... | [
"# use-cmlm-multilingual\nThis is a pytorch version of the universal-sentence-encoder-cmlm/multilingual-base-br model. It can be used to map 109 languages to a shared vector space. As the model is based LaBSE, it perform quite comparable on downstream tasks.",
"## Usage (Sentence-Transformers)\n\nUsing this model... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# use-cmlm-multilingual\nThis is a pytorch version of the universal-sentence-encoder-cmlm/multilingual-base-br model. It can be used to map ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | jekdoieao/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T21:21:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3731
* Wer: 0.3635
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
unconditional-image-generation | transformers |
# Hugging NFT: cryptopunks
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/cryptopunks).
... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/cryptopunks"]} | huggingnft/cryptopunks | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/cryptopunks",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T21:22:48+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cryptopunks #license-mit #endpoints_compatible #has_space #region-us
|
# Hugging NFT: cryptopunks
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: lin... | [
"# Hugging NFT: cryptopunks",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available he... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cryptopunks #license-mit #endpoints_compatible #has_space #region-us \n",
"# Hugging NFT: cryptopunks",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed fr... |
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. -->
# SingleBertModel-ProtBertfinetuned-smilesBindingDB
This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "SingleBertModel-ProtBertfinetuned-smilesBindingDB", "results": []}]} | nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T21:27:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| SingleBertModel-ProtBertfinetuned-smilesBindingDB
=================================================
This model is a fine-tuned version of Rostlab/prot\_bert on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: nan
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_si... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# caotianyu1996/bert_finetuned_ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "caotianyu1996/bert_finetuned_ner", "results": []}]} | caotianyu1996/bert_finetuned_ner | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T22:16:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| caotianyu1996/bert\_finetuned\_ner
==================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0247
* Validation Loss: 0.0593
* Epoch: 2
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tiny-albert
This model is a fine-tuned version of [hf-internal-testing/tiny-albert](https://huggingface.co/hf-internal-testing/tiny-al... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tiny-albert", "results": []}]} | vumichien/tiny-albert | null | [
"transformers",
"pytorch",
"tf",
"albert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T22:31:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #albert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# tiny-albert
This model is a fine-tuned version of hf-internal-testing/tiny-albert on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informat... | [
"# tiny-albert\n\nThis model is a fine-tuned version of hf-internal-testing/tiny-albert on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation... | [
"TAGS\n#transformers #pytorch #tf #albert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# tiny-albert\n\nThis model is a fine-tuned version of hf-internal-testing/tiny-albert on an unknown dataset.\nIt achieves the following results on the evalua... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | cj-mills/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T22:53:31+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2525
* Accuracy: 0.9468
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-base-cased-trec-fine
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluati... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-base-cased-trec-fine", "results": []}]} | ndavid/bert-base-cased-trec-fine | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T22:56:54+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-cased-trec-fine
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
### How to use
## Training and evaluation data
More information needed
## Training pr... | [
"# bert-base-cased-trec-fine\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations",
"### How to use",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-cased-trec-fine\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model descript... |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_color_field | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-13T23:56:39+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us \n"
] |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_popart | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-14T00:05:56+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us \n"
] |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_abstract_expressionism | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-14T00:06:29+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us \n"
] |
text2text-generation | transformers |
**Don't use this model for any applied task. It too small to be practically useful. It is just a part of a weird research project.**
An extremely small version of T5 with these parameters
```python
"d_ff": 1024,
"d_kv": 64,
"d_model": 256,
"num_heads": 4,
"num_layers": 1, # yes, just one layer
```
The mo... | {"license": "apache-2.0"} | dropout05/t5-realnewslike-super-tiny | null | [
"transformers",
"jax",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T00:34:38+00:00 | [] | [] | TAGS
#transformers #jax #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Don't use this model for any applied task. It too small to be practically useful. It is just a part of a weird research project.
An extremely small version of T5 with these parameters
The model was pre-trained on 'realnewslike' subset of C4 for 1 epoch with sequence length '64'. Corresponding WandB run: click. | [] | [
"TAGS\n#transformers #jax #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# koelectra-base-v3-discriminator-finetuned-klue-v4
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminato... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "koelectra-base-v3-discriminator-finetuned-klue-v4", "results": []}]} | obokkkk/koelectra-base-v3-discriminator-finetuned-klue-v4 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T01:45:22+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| koelectra-base-v3-discriminator-finetuned-klue-v4
=================================================
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6219
Model description
-----------------
More... | [
"### 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: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size:... |
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-multilingual-cased-finetuned-klue
This model is a fine-tuned version of [bert-base-multilingual-cased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-klue", "results": []}]} | obokkkk/bert-base-multilingual-cased-finetuned-klue | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T02:17:34+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-multilingual-cased-finetuned-klue
===========================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4197
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 36\n* total\\_train\\_batch\\_size: 288\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-MIR_ST500_ASR
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py", "generated_from_trainer"], "datasets": ["mir_st500"], "model-index": [{"name": "wav2vec2-large-xlsr-53-MIR_ST500_ASR", "results": []}]} | gary109/wav2vec2-large-xlsr-53-MIR_ST500_ASR | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py",
"generated_from_trainer",
"dataset:mir_st500",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T02:20:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-MIR\_ST500\_ASR
======================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the /WORKSPACE/DATASETS/DATASETS/MIR\_ST500/MIR\_ST500.PY - ASR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5180
* Wer: 0.5824
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 8\n* total\\_eval\\_batch\\_size: 16\n* opt... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used du... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsattar/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsattar/bert-finetuned-ner", "results": []}]} | hsattar/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T03:19:28+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsattar/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0243
* Validation Loss: 0.0573
* Epoch: 2
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
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/credenzaclear2-dril-nia_mp4/1649911222622/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/credenzaclear2-dril-nia_mp4 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T03:39:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wint & Nia & Audrey Horne
@credenzaclear2-dril-nia\_mp4
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 re... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PENGMENGJIE-finetuned-sms
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "PENGMENGJIE-finetuned-sms", "results": []}]} | ASCCCCCCCC/PENGMENGJIE-finetuned-sms | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T05:37:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| PENGMENGJIE-finetuned-sms
=========================
This model is a fine-tuned version of bert-base-chinese on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Accuracy: 1.0
* F1: 1.0
Model description
-----------------
More information needed
Intended uses & limit... | [
"### 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: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_b... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-vgg16-bn-r | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T06:36:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-resnet18 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T06:39:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-resnet31 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T06:42:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-resnet34 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T06:48:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-resnet34-wide | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T06:51:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-resnet50 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:06:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
image-classification | transformers |
# Data2Vec-Vision (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-1k"]} | facebook/data2vec-vision-base | null | [
"transformers",
"pytorch",
"tf",
"data2vec-vision",
"feature-extraction",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-14T07:08:12+00:00 | [
"2202.03555",
"2106.08254"
] | [] | TAGS
#transformers #pytorch #tf #data2vec-vision #feature-extraction #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Data2Vec-Vision (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevsk... | [
"# Data2Vec-Vision (base-sized model, pre-trained only) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei ... | [
"TAGS\n#transformers #pytorch #tf #data2vec-vision #feature-extraction #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Data2Vec-Vision (base-sized model, pre-trained only) \n\nBEiT mode... |
image-classification | transformers |
# Data2Vec-Vision (large-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-1k"]} | facebook/data2vec-vision-large | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"data2vec-vision",
"feature-extraction",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:08:23+00:00 | [
"2202.03555",
"2106.08254"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #data2vec-vision #feature-extraction #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Vision (large-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevs... | [
"# Data2Vec-Vision (large-sized model, pre-trained only) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei... | [
"TAGS\n#transformers #pytorch #tf #safetensors #data2vec-vision #feature-extraction #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Vision (large-sized model, pre-trained only) \n\nBEiT m... |
image-classification | transformers |
# Data2Vec-Vision (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-1k"]} | facebook/data2vec-vision-large-ft1k | null | [
"transformers",
"pytorch",
"tf",
"data2vec-vision",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:09:04+00:00 | [
"2202.03555",
"2106.08254"
] | [] | TAGS
#transformers #pytorch #tf #data2vec-vision #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Data2Vec-Vision (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Vision and ... | [
"# Data2Vec-Vision (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Visio... | [
"TAGS\n#transformers #pytorch #tf #data2vec-vision #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data2Vec-Vision (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT mod... |
image-classification | transformers |
# Data2Vec-Vision (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and ... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-1k"]} | facebook/data2vec-vision-base-ft1k | null | [
"transformers",
"pytorch",
"tf",
"data2vec-vision",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:09:21+00:00 | [
"2202.03555",
"2106.08254"
] | [] | TAGS
#transformers #pytorch #tf #data2vec-vision #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Data2Vec-Vision (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Vision and L... | [
"# Data2Vec-Vision (base-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper data2vec: A General Framework for Self-supervised Learning in Speech, Vision... | [
"TAGS\n#transformers #pytorch #tf #data2vec-vision #image-classification #vision #dataset-imagenet #dataset-imagenet-1k #arxiv-2202.03555 #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data2Vec-Vision (base-sized model, fine-tuned on ImageNet-1k) \n\nBEiT mode... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-magc-resnet31 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:18:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-mobilenet-v3-small | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:25:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
token-classification | transformers | Used for extracting arguments from Thai text. | {} | pitiwat/argument_wangchanberta | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:30:23+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Used for extracting arguments from Thai text. | [] | [
"TAGS\n#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# GPT-2
Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-langua... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/gpt2-small-theseus-bg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"torch",
"custom_code",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:2002.02925",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T07:47:09+00:00 | [
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2002.02925 #license-mit #autotrain_compatible #text-generation-inference #region-us
|
# GPT-2
Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
This is the SMALL version compressed via progressive module replacing.
The compression was executed on Bulgarian text from OSCAR, Ch... | [
"# GPT-2\n\nPretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\n\nThis is the SMALL version compressed via progressive module replacing.\n\nThe compression was executed on Bulgarian tex... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2002.02925 #license-mit #autotrain_compatible #text-generation-inference #region-us \n",
"# GPT-2\n\nPretrained model on Bulgarian language using a causal language modeling (CLM)... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-torch-mobilenet-v3-large | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:49:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-torch-db-resnet34 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:51:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-torch-db-resnet50 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:54:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-torch-db-mobilenet-v3-large | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:57:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-torch-db-resnet50-rotation | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T07:58:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-torch-linknet-resnet18 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:02:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/Mex_Rbta_Opinion_Polarity
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/Plan... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_Opinion_Polarity", "results": []}]} | javilonso/Mex_Rbta_Opinion_Polarity | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:04:20+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_Opinion\_Polarity
======================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4033
* Validation Loss: 0.5572
* Epoch: 1
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 5986, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
image-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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | zzzzzzttt/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:04:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0654
* Accuracy: 0.9763
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-torch-linknet-resnet34 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:17:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
image-classification | transformers | `microsoft/swin-tiny-patch4-window7-224` fine-tuned on the `Matthijs/snacks` dataset.
Test set accuracy after 50 epochs: 0.9286.
| {} | Matthijs/snacks-classifier | null | [
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:19:01+00:00 | [] | [] | TAGS
#transformers #pytorch #swin #image-classification #autotrain_compatible #endpoints_compatible #region-us
| 'microsoft/swin-tiny-patch4-window7-224' fine-tuned on the 'Matthijs/snacks' dataset.
Test set accuracy after 50 epochs: 0.9286.
| [] | [
"TAGS\n#transformers #pytorch #swin #image-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-torch-linknet-resnet50 | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:19:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | null |
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
## Original implementation
Follow [this link](https:/... | {"language": ["en"], "license": "apache-2.0", "tags": ["causal-lm"], "datasets": ["the_pile"]} | OWG/gpt-j-6B | null | [
"onnx",
"causal-lm",
"en",
"dataset:the_pile",
"license:apache-2.0",
"region:us"
] | null | 2022-04-14T08:20:10+00:00 | [] | [
"en"
] | TAGS
#onnx #causal-lm #en #dataset-the_pile #license-apache-2.0 #region-us
|
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
## Original implementation
Follow this link to see the original implementation.
# How to use
Download the ... | [
"# GPT-J 6B",
"## Model Description\n\nGPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. \"GPT-J\" refers to the class of model, while \"6B\" represents the number of trainable parameters.",
"## Original implementation\n\nFollow this link to see the original implementation.",
"# H... | [
"TAGS\n#onnx #causal-lm #en #dataset-the_pile #license-apache-2.0 #region-us \n",
"# GPT-J 6B",
"## Model Description\n\nGPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. \"GPT-J\" refers to the class of model, while \"6B\" represents the number of trainable parameters.",
"## Orig... |
unconditional-image-generation | transformers |
# Hugging NFT: trippytoadznft
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/trippytoadz... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/trippytoadznft"]} | huggingnft/trippytoadznft | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/trippytoadznft",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:23:12+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/trippytoadznft #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: trippytoadznft
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: ... | [
"# Hugging NFT: trippytoadznft",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/trippytoadznft #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: trippytoadznft",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from th... |
unconditional-image-generation | transformers |
# Hugging NFT: etherbears
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/etherbears).
D... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/etherbears"]} | huggingnft/etherbears | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/etherbears",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:23:35+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/etherbears #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: etherbears
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link... | [
"# Hugging NFT: etherbears",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available her... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/etherbears #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: etherbears",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site a... |
image-to-text | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> fr... | {"language": "en", "pipeline_tag": "image-to-text"} | Felix92/doctr-dummy-torch-crnn-vgg16-bn | null | [
"transformers",
"pytorch",
"image-to-text",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:24:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #image-to-text #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: recognition
URL
### Example usage:
| [
"## Task: recognition\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #image-to-text #en #endpoints_compatible #region-us \n",
"## Task: recognition\n\nURL",
"### Example usage:"
] |
image-to-text | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> fr... | {"language": "en", "pipeline_tag": "image-to-text"} | Felix92/doctr-dummy-torch-crnn-mobilenet-v3-small | null | [
"transformers",
"pytorch",
"image-to-text",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-14T08:26:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #image-to-text #en #endpoints_compatible #has_space #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: recognition
URL
### Example usage:
| [
"## Task: recognition\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #image-to-text #en #endpoints_compatible #has_space #region-us \n",
"## Task: recognition\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> fr... | {"language": "en"} | Felix92/doctr-dummy-torch-crnn-mobilenet-v3-large | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:27:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: recognition
URL
### Example usage:
| [
"## Task: recognition\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: recognition\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: obj_detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> ... | {"language": "en"} | Felix92/doctr-dummy-torch-fasterrcnn-mobilenet-v3-large-fpn | null | [
"transformers",
"pytorch",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:28:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: obj_detection
URL
### Example usage:
| [
"## Task: obj_detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #pytorch #en #endpoints_compatible #region-us \n",
"## Task: obj_detection\n\nURL",
"### Example usage:"
] |
text-generation | transformers |
# 日本語 gpt2 蒸留モデル
このモデルは[rinna/japanese-gpt2-meduim](https://huggingface.co/rinna/japanese-gpt2-medium)を教師として蒸留したものです。
蒸留には、HuggigFace Transformersの[コード](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation)をベースとし、[りんなの訓練コード](https://github.com/rinnakk/japanese-pretrained-model... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["wikipedia", "cc100"]} | knok/japanese-distilgpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"ja",
"japanese",
"text-generation",
"lm",
"nlp",
"dataset:wikipedia",
"dataset:cc100",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T08:32:23+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #gpt2 #ja #japanese #text-generation #lm #nlp #dataset-wikipedia #dataset-cc100 #license-mit #endpoints_compatible #text-generation-inference #region-us
|
# 日本語 gpt2 蒸留モデル
このモデルはrinna/japanese-gpt2-meduimを教師として蒸留したものです。
蒸留には、HuggigFace Transformersのコードをベースとし、りんなの訓練コードと組み合わせてデータ扱うよう改造したものを使っています。
訓練用コード: URL
## 学習に関して
学習に当たり、Google Startup Programにて提供されたクレジットを用いました。
a2-highgpu-4インスタンス(A100 x 4)を使って4か月程度、何度かのresumeを挟んで訓練させました。
## 精度について
Wikipediaをコーパスとし、perplexity 4... | [
"# 日本語 gpt2 蒸留モデル\n\nこのモデルはrinna/japanese-gpt2-meduimを教師として蒸留したものです。\n蒸留には、HuggigFace Transformersのコードをベースとし、りんなの訓練コードと組み合わせてデータ扱うよう改造したものを使っています。\n\n訓練用コード: URL",
"## 学習に関して\n\n学習に当たり、Google Startup Programにて提供されたクレジットを用いました。\na2-highgpu-4インスタンス(A100 x 4)を使って4か月程度、何度かのresumeを挟んで訓練させました。",
"## 精度について\n\nWikiped... | [
"TAGS\n#transformers #pytorch #gpt2 #ja #japanese #text-generation #lm #nlp #dataset-wikipedia #dataset-cc100 #license-mit #endpoints_compatible #text-generation-inference #region-us \n",
"# 日本語 gpt2 蒸留モデル\n\nこのモデルはrinna/japanese-gpt2-meduimを教師として蒸留したものです。\n蒸留には、HuggigFace Transformersのコードをベースとし、りんなの訓練コードと組み合わせてデ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 739422530
- CO2 Emissions (in grams): 0.02238820299105448
## Validation Metrics
- Loss: 0.36623290181159973
- Accuracy: 0.9321753515301903
- Macro F1: 0.9066706944656866
- Micro F1: 0.9321753515301903
- Weighted F1: 0.93148586672... | {"language": "en", "tags": "autotrain", "datasets": ["ndavid/autotrain-data-trec-fine-bert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.02238820299105448} | ndavid/autotrain-trec-fine-bert-739422530 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:ndavid/autotrain-data-trec-fine-bert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:37:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-ndavid/autotrain-data-trec-fine-bert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 739422530
- CO2 Emissions (in grams): 0.02238820299105448
## Validation Metrics
- Loss: 0.36623290181159973
- Accuracy: 0.9321753515301903
- Macro F1: 0.9066706944656866
- Micro F1: 0.9321753515301903
- Weighted F1: 0.93148586672... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 739422530\n- CO2 Emissions (in grams): 0.02238820299105448",
"## Validation Metrics\n\n- Loss: 0.36623290181159973\n- Accuracy: 0.9321753515301903\n- Macro F1: 0.9066706944656866\n- Micro F1: 0.9321753515301903\n- Weighted... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-ndavid/autotrain-data-trec-fine-bert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 739422530\n- CO2 Emissions... |
null | null | This is an image feature extraction model which uses supervised contrastive learning to extract features from cifar10 dataset | {} | alihaiderrizvi/supcon_cifar10 | null | [
"region:us"
] | null | 2022-04-14T08:38:42+00:00 | [] | [] | TAGS
#region-us
| This is an image feature extraction model which uses supervised contrastive learning to extract features from cifar10 dataset | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers | # stocks-news-t5
This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned [T5](https://huggingface.co/t5-base) allows to analyze financial market news.
Automatically trained on [Fin... | {"language": ["en"], "license": "mit", "tags": ["Cometrain AutoCode", "Cometrain AlphaML"], "datasets": ["financial-sentiment-analysis"], "widget": [{"text": "April 14 (Reuters) - Rio Tinto (RIO.AX), one of the largest Australian mining companies, on Thursday confirmed its exit from the state mining lobby group after r... | cometrain/stocks-news-t5 | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"Cometrain AutoCode",
"Cometrain AlphaML",
"en",
"dataset:financial-sentiment-analysis",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-14T08:44:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #Cometrain AutoCode #Cometrain AlphaML #en #dataset-financial-sentiment-analysis #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # stocks-news-t5
This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned T5 allows to analyze financial market news.
Automatically trained on Financial Sentiment Analysis(2022) dat... | [
"# stocks-news-t5\nThis model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned T5 allows to analyze financial market news.\nAutomatically trained on Financial Sentiment Analysis(20... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #Cometrain AutoCode #Cometrain AlphaML #en #dataset-financial-sentiment-analysis #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# stocks-news-t5\nThis model has been automatically ... |
unconditional-image-generation | transformers |
# Butterfly GAN
## Model description
Based on [paper:](https://openreview.net/forum?id=1Fqg133qRaI) *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis*
which states:
"Notably, the model converges from scratch with just a **few hours of training** on a single RTX-2080 GPU, and ha... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/smithsonian_butterflies_subset"]} | ceyda/butterfly_cropped_uniq1K_512 | null | [
"transformers",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/smithsonian_butterflies_subset",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-14T08:48:08+00:00 | [] | [] | TAGS
#transformers #huggan #gan #unconditional-image-generation #dataset-huggan/smithsonian_butterflies_subset #license-mit #endpoints_compatible #has_space #region-us
|
# Butterfly GAN
## Model description
Based on paper: *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis*
which states:
"Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100... | [
"# Butterfly GAN",
"## Model description\n\nBased on paper: *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis* \n\nwhich states:\n\"Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with... | [
"TAGS\n#transformers #huggan #gan #unconditional-image-generation #dataset-huggan/smithsonian_butterflies_subset #license-mit #endpoints_compatible #has_space #region-us \n",
"# Butterfly GAN",
"## Model description\n\nBased on paper: *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image ... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-vgg16-bn-r | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T08:52:14+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
unconditional-image-generation | transformers |
# Hugging NFT: hapeprime
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/hapeprime).
Dat... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/hapeprime"]} | huggingnft/hapeprime | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/hapeprime",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T09:11:16+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/hapeprime #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: hapeprime
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link.... | [
"# Hugging NFT: hapeprime",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available here... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/hapeprime #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: hapeprime",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at ... |
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
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | Manishkalra/finetuning-sentiment-model-3000-samples | 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 | 2022-04-14T09:36:16+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
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: 0.3186
- Accuracy: 0.87
- F1: 0.8770
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\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: 0.3186\n- Accuracy: 0.87\n- F1: 0.8770",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-resnet18 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T09:36:17+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-resnet31 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T09:37:52+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-resnet34 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T09:43:19+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-resnet34-wide | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T09:47:08+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-resnet50 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:09:22+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-magc-resnet31 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:13:24+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
audio-classification | speechbrain |
# VoxLingua107 Wav2Vec Spoken Language Identification Model
## Model description
This is a spoken language identification model trained on the VoxLingua107 dataset using SpeechBrain.
The model is trained using weights of pretrained [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) mo... | {"language": "multilingual", "license": "cc-by-4.0", "tags": ["language-identification", "speechbrain", "wav2vec2", "pytorch", "embeddings", "Language", "Identification", "audio-classification", "wav2vec2.0", "XLS-R-300M", "VoxLingua107"], "datasets": ["voxlingua107"], "metrics": ["Accuracy"]} | TalTechNLP/voxlingua107-xls-r-300m-wav2vec | null | [
"speechbrain",
"wav2vec2",
"language-identification",
"pytorch",
"embeddings",
"Language",
"Identification",
"audio-classification",
"wav2vec2.0",
"XLS-R-300M",
"VoxLingua107",
"multilingual",
"dataset:voxlingua107",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-14T10:16:25+00:00 | [] | [
"multilingual"
] | TAGS
#speechbrain #wav2vec2 #language-identification #pytorch #embeddings #Language #Identification #audio-classification #wav2vec2.0 #XLS-R-300M #VoxLingua107 #multilingual #dataset-voxlingua107 #license-cc-by-4.0 #region-us
| VoxLingua107 Wav2Vec Spoken Language Identification Model
=========================================================
Model description
-----------------
This is a spoken language identification model trained on the VoxLingua107 dataset using SpeechBrain.
The model is trained using weights of pretrained facebook/wa... | [
"#### How to use",
"#### Limitations and bias\n\n\nSince the model is trained on VoxLingua107, it has many limitations and biases, some of which are:\n\n\n* Probably it's accuracy on smaller languages is quite limited\n* Probably it works worse on female speech than male speech (because YouTube data includes much... | [
"TAGS\n#speechbrain #wav2vec2 #language-identification #pytorch #embeddings #Language #Identification #audio-classification #wav2vec2.0 #XLS-R-300M #VoxLingua107 #multilingual #dataset-voxlingua107 #license-cc-by-4.0 #region-us \n",
"#### How to use",
"#### Limitations and bias\n\n\nSince the model is trained o... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>>... | {"language": "en"} | Felix92/doctr-dummy-tf-mobilenet-v3-large | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:22:55+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: classification
URL
### Example usage:
| [
"## Task: classification\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: classification\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-tf-db-resnet50 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:25:00+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-tf-db-mobilenet-v3-large | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:28:18+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-tf-linknet-resnet18 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:29:39+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-tf-linknet-resnet18-rotation | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:33:05+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-SmithsModel
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-SmithsModel", "results": []}]} | stevems1/distilroberta-base-SmithsModel | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:37:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-SmithsModel
==============================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3070
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: 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 #text-generation #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\\_... |
null | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from... | {"language": "en"} | Felix92/doctr-dummy-tf-linknet-resnet50 | null | [
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:37:48+00:00 | [] | [
"en"
] | TAGS
#transformers #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
| [
"## Task: detection\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #en #endpoints_compatible #region-us \n",
"## Task: detection\n\nURL",
"### Example usage:"
] |
image-to-text | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> fr... | {"language": "en", "pipeline_tag": "image-to-text"} | Felix92/doctr-dummy-tf-crnn-vgg16-bn | null | [
"transformers",
"image-to-text",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:42:26+00:00 | [] | [
"en"
] | TAGS
#transformers #image-to-text #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: recognition
URL
### Example usage:
| [
"## Task: recognition\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #image-to-text #en #endpoints_compatible #region-us \n",
"## Task: recognition\n\nURL",
"### Example usage:"
] |
image-to-text | transformers |
<p align="center">
<img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> fr... | {"language": "en", "pipeline_tag": "image-to-text"} | Felix92/doctr-dummy-tf-crnn-mobilenet-v3-large | null | [
"transformers",
"image-to-text",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:46:53+00:00 | [] | [
"en"
] | TAGS
#transformers #image-to-text #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: recognition
URL
### Example usage:
| [
"## Task: recognition\n\nURL",
"### Example usage:"
] | [
"TAGS\n#transformers #image-to-text #en #endpoints_compatible #region-us \n",
"## Task: recognition\n\nURL",
"### Example usage:"
] |
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
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | aaya/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T10:55:47+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
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
"# distilbert-base-uncased-finetuned-ner\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-ner\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
... |
fill-mask | transformers |
# BERTu
A Maltese monolingual model pre-trained from scratch on the Korpus Malti v4.0 using the BERT (base) architecture.
## License
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
Permissions beyond the scope of this license may be avai... | {"language": ["mt"], "license": "cc-by-nc-sa-4.0", "datasets": ["MLRS/korpus_malti"], "widget": [{"text": "Malta hija g\u017cira fil-[MASK]."}], "model-index": [{"name": "BERTu", "results": [{"task": {"type": "dependency-parsing", "name": "Dependency Parsing"}, "dataset": {"name": "Maltese Universal Dependencies Treeba... | MLRS/BERTu | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"mt",
"dataset:MLRS/korpus_malti",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-14T11:16:10+00:00 | [] | [
"mt"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #mt #dataset-MLRS/korpus_malti #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# BERTu
A Maltese monolingual model pre-trained from scratch on the Korpus Malti v4.0 using the BERT (base) architecture.
## License
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
Permissions beyond the scope of this license may be avai... | [
"# BERTu\n\nA Maltese monolingual model pre-trained from scratch on the Korpus Malti v4.0 using the BERT (base) architecture.",
"## License\n\nThis work is licensed under a\n[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].\nPermissions beyond the scope of this licens... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #mt #dataset-MLRS/korpus_malti #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERTu\n\nA Maltese monolingual model pre-trained from scratch on the Korpus Malti v4.0 using the BERT (base) architecture.... |
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