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text-generation | transformers |
# Camp Buddy - Yoshinori - DialoGPTSmall Model | {"tags": ["conversational"]} | lonewanderer27/YoshinoriBot | null | [
"transformers",
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"gpt2",
"text-generation",
"conversational",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Camp Buddy - Yoshinori - DialoGPTSmall Model | [
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] |
text-generation | transformers |
# Camp Buddy - Yuri - DialoGPTMedium Model | {"tags": ["conversational"]} | lonewanderer27/YuriBot | null | [
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"pytorch",
"gpt2",
"text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Camp Buddy - Yuri - DialoGPTMedium Model | [
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] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | longnhit07/distilbert-base-uncased-finetuned-imdb | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4722
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
null | transformers |
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).... | {"language": "tr"} | loodos/albert-base-turkish-uncased | null | [
"transformers",
"pytorch",
"tf",
"albert",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #albert #tr #endpoints_compatible #region-us
|
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).
# Turkish ALBERT-Base (uncased)
This is ALBERT-Bas... | [
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).",
"# Turkish ALBERT-Base (uncased)\n\nThis i... | [
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null | transformers |
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-model... | {"language": "tr"} | loodos/bert-base-turkish-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #bert #tr #endpoints_compatible #region-us
|
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).
# Turkish BERT-Base (cased)
This is BERT-Ba... | [
"# Turkish Language Models with Huggingface's Transformers\r\n\r\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).",
"# Turkish BERT-Base (cased)\r\n\r\nTh... | [
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null | transformers |
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).... | {"language": "tr"} | loodos/bert-base-turkish-uncased | null | [
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"pytorch",
"tf",
"jax",
"bert",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #bert #tr #endpoints_compatible #region-us
|
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).
# Turkish BERT-Base (uncased)
This is BERT-Base mo... | [
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).",
"# Turkish BERT-Base (uncased)\n\nThis is ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #tr #endpoints_compatible #region-us \n",
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null | transformers |
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).... | {"language": "tr"} | loodos/electra-base-turkish-64k-uncased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #tr #endpoints_compatible #region-us
|
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).
# Turkish ELECTRA-Base-discriminator (uncased/64k)
... | [
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).",
"# Turkish ELECTRA-Base-discriminator (unc... | [
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null | transformers |
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).... | {"language": "tr"} | loodos/electra-base-turkish-uncased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #tr #endpoints_compatible #region-us
|
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).
# Turkish ELECTRA-Base-discriminator (uncased)
Thi... | [
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).",
"# Turkish ELECTRA-Base-discriminator (unc... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #tr #endpoints_compatible #region-us \n",
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null | transformers |
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).... | {"language": "tr"} | loodos/electra-small-turkish-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #tr #endpoints_compatible #region-us
|
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).
# Turkish ELECTRA-Small-discriminator (cased)
This... | [
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).",
"# Turkish ELECTRA-Small-discriminator (ca... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #tr #endpoints_compatible #region-us \n",
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluati... |
null | transformers |
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).... | {"language": "tr"} | loodos/electra-small-turkish-uncased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #tr #endpoints_compatible #region-us
|
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).
# Turkish ELECTRA-Small-discriminator (uncased)
Th... | [
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found here (our repo).",
"# Turkish ELECTRA-Small-discriminator (un... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #tr #endpoints_compatible #region-us \n",
"# Turkish Language Models with Huggingface's Transformers\n\nAs R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluati... |
fill-mask | transformers |
## COVID-SciBERT: A small language modelling expansion of SciBERT, a BERT model trained on scientific text.
### Details of SciBERT
The **SciBERT** model was presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://arxiv.org/abs/1903.10676) by *Iz Beltagy, Kyle Lo, Arman Cohan* and here is the ... | {"language": "en", "inference": false} | lordtt13/COVID-SciBERT | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"arxiv:1903.10676",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1903.10676"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #en #arxiv-1903.10676 #autotrain_compatible #has_space #region-us
|
## COVID-SciBERT: A small language modelling expansion of SciBERT, a BERT model trained on scientific text.
### Details of SciBERT
The SciBERT model was presented in SciBERT: A Pretrained Language Model for Scientific Text by *Iz Beltagy, Kyle Lo, Arman Cohan* and here is the abstract:
Obtaining large-scale annotat... | [
"## COVID-SciBERT: A small language modelling expansion of SciBERT, a BERT model trained on scientific text.",
"### Details of SciBERT\n\nThe SciBERT model was presented in SciBERT: A Pretrained Language Model for Scientific Text by *Iz Beltagy, Kyle Lo, Arman Cohan* and here is the abstract:\n\nObtaining large-s... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #en #arxiv-1903.10676 #autotrain_compatible #has_space #region-us \n",
"## COVID-SciBERT: A small language modelling expansion of SciBERT, a BERT model trained on scientific text.",
"### Details of SciBERT\n\nThe SciBERT model was presented in SciBERT: A P... |
text2text-generation | transformers |
## BlenderBotSmall-News: Small version of a state-of-the-art open source chatbot, trained on custom summaries
### Details of BlenderBotSmall
The **BlenderBotSmall** model was presented in [A state-of-the-art open source chatbot](https://ai.facebook.com/blog/state-of-the-art-open-source-chatbot/) by *Facebook AI* and... | {"language": "en"} | lordtt13/blenderbot_small-news | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"blenderbot-small",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #blenderbot-small #text2text-generation #en #autotrain_compatible #endpoints_compatible #region-us
|
## BlenderBotSmall-News: Small version of a state-of-the-art open source chatbot, trained on custom summaries
### Details of BlenderBotSmall
The BlenderBotSmall model was presented in A state-of-the-art open source chatbot by *Facebook AI* and here are it's details:
- Facebook AI has built and open-sourced BlenderB... | [
"## BlenderBotSmall-News: Small version of a state-of-the-art open source chatbot, trained on custom summaries",
"### Details of BlenderBotSmall\n\nThe BlenderBotSmall model was presented in A state-of-the-art open source chatbot by *Facebook AI* and here are it's details:\n\n- Facebook AI has built and open-sour... | [
"TAGS\n#transformers #pytorch #tf #safetensors #blenderbot-small #text2text-generation #en #autotrain_compatible #endpoints_compatible #region-us \n",
"## BlenderBotSmall-News: Small version of a state-of-the-art open source chatbot, trained on custom summaries",
"### Details of BlenderBotSmall\n\nThe BlenderBo... |
text-classification | transformers |
## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch
### Details of MobileBERT
The **MobileBERT** model was presented in [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by *Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renj... | {"language": "en", "datasets": ["emo"]} | lordtt13/emo-mobilebert | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"mobilebert",
"text-classification",
"en",
"dataset:emo",
"arxiv:2004.02984",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02984"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #mobilebert #text-classification #en #dataset-emo #arxiv-2004.02984 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch
### Details of MobileBERT
The MobileBERT model was presented in MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by *Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zhou* and her... | [
"## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch",
"### Details of MobileBERT\n\nThe MobileBERT model was presented in MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by *Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zho... | [
"TAGS\n#transformers #pytorch #tf #safetensors #mobilebert #text-classification #en #dataset-emo #arxiv-2004.02984 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch",
"### Details of MobileBERT\... |
text2text-generation | transformers |
## T5-inshorts: T5 model trained on inshorts data
### Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi... | {"language": "en", "inference": false} | lordtt13/t5-inshorts | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"arxiv:1910.10683",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #arxiv-1910.10683 #autotrain_compatible #text-generation-inference #region-us
|
## T5-inshorts: T5 model trained on inshorts data
### Details of T5
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* and here is... | [
"## T5-inshorts: T5 model trained on inshorts data",
"### Details of T5\n\nThe T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* a... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #arxiv-1910.10683 #autotrain_compatible #text-generation-inference #region-us \n",
"## T5-inshorts: T5 model trained on inshorts data",
"### Details of T5\n\nThe T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Tex... |
text-generation | transformers |
# Johnny DialoGPT Model | {"tags": ["conversational"]} | lovellyweather/DialoGPT-medium-johnny | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Johnny DialoGPT Model | [
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"# Johnny DialoGPT Model"
] |
null | null | # 512x512 diffusion (unconditional ImageNet)
Modality: Images
Intended Use: Generation of images with or without classifier guidance
## Detailed description
A 512x512 unconditional ImageNet diffusion model, fine-tuned for 8100 steps from the OpenAI trained 512x512 class-conditional ImageNet diffusion model. It was... | {} | lowlevelware/512x512_diffusion_unconditional_ImageNet | null | [
"arxiv:2105.05233",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.05233"
] | [] | TAGS
#arxiv-2105.05233 #region-us
| # 512x512 diffusion (unconditional ImageNet)
Modality: Images
Intended Use: Generation of images with or without classifier guidance
## Detailed description
A 512x512 unconditional ImageNet diffusion model, fine-tuned for 8100 steps from the OpenAI trained 512x512 class-conditional ImageNet diffusion model. It was... | [
"# 512x512 diffusion (unconditional ImageNet)\n\nModality: Images \nIntended Use: Generation of images with or without classifier guidance",
"## Detailed description\n\nA 512x512 unconditional ImageNet diffusion model, fine-tuned for 8100 steps from the OpenAI trained 512x512 class-conditional ImageNet diffusion... | [
"TAGS\n#arxiv-2105.05233 #region-us \n",
"# 512x512 diffusion (unconditional ImageNet)\n\nModality: Images \nIntended Use: Generation of images with or without classifier guidance",
"## Detailed description\n\nA 512x512 unconditional ImageNet diffusion model, fine-tuned for 8100 steps from the OpenAI trained 5... |
text2text-generation | transformers |
# AI2 SciTLDR
Fairseq checkpoints from CATTS XSUM to Transformers BART (Abtract Only)
Original repository: [https://github.com/allenai/scitldr](https://github.com/allenai/scitldr)
## Demo
A running demo of AI2 model can be found [here](https://scitldr.apps.allenai.org).
### Citing
If you use code, dataset, or model... | {"language": ["en"], "license": "apache-2.0", "datasets": ["xsum", "scitldr"], "widget": [{"text": "We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-spec... | lrakotoson/scitldr-catts-xsum-ao | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:xsum",
"dataset:scitldr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #bart #text2text-generation #en #dataset-xsum #dataset-scitldr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# AI2 SciTLDR
Fairseq checkpoints from CATTS XSUM to Transformers BART (Abtract Only)
Original repository: URL
## Demo
A running demo of AI2 model can be found here.
### Citing
If you use code, dataset, or model weights in your research, please cite "TLDR: Extreme Summarization of Scientific Documents."
SciTLDR ... | [
"# AI2 SciTLDR\nFairseq checkpoints from CATTS XSUM to Transformers BART (Abtract Only)\n\nOriginal repository: URL",
"## Demo\nA running demo of AI2 model can be found here.",
"### Citing\nIf you use code, dataset, or model weights in your research, please cite \"TLDR: Extreme Summarization of Scientific Docum... | [
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"# AI2 SciTLDR\nFairseq checkpoints from CATTS XSUM to Transformers BART (Abtract Only)\n\nOriginal repository: URL",
"## ... |
null | null | This sentiment analyzer is used for analyzing the comments of restaurant's review | {} | lrsowmya/SentimentAnalyer | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This sentiment analyzer is used for analyzing the comments of restaurant's review | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers | ---
# wav2vec2-base-it-latin
This model is a fine-tuned version of [wav2vec2-base-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-base-it-voxpopuli)
The dataset used is the [poetaexmachina-mp3-recitations](https://github.com/lsb/poetaexmachina-mp3-recitations),
all of the 2-series texts (vergil) and every ten... | {"language": ["la"], "license": "agpl-3.0", "tags": ["robust-speech-event", "hf-asr-leaderboard"], "datasets": ["lsb/poetaexmachina-mp3-recitations"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-base-it-latin", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "data... | lsb/wav2vec2-base-it-latin | null | [
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"la",
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"license:agpl-3.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"la"
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #la #dataset-lsb/poetaexmachina-mp3-recitations #license-agpl-3.0 #model-index #endpoints_compatible #region-us
| ---
# wav2vec2-base-it-latin
This model is a fine-tuned version of wav2vec2-base-it-voxpopuli
The dataset used is the poetaexmachina-mp3-recitations,
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's database of words with scansions).
It achieves the following results o... | [
"# wav2vec2-base-it-latin\n\nThis model is a fine-tuned version of wav2vec2-base-it-voxpopuli\n\nThe dataset used is the poetaexmachina-mp3-recitations,\nall of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's database of words with scansions).\n\nIt achieves the following re... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #la #dataset-lsb/poetaexmachina-mp3-recitations #license-agpl-3.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-base-it-latin\n\nThis model is a fine-tuned version of wav2vec2-base-it-vo... |
question-answering | transformers |
# bert-turkish-question-answering
## Usage
```python
from transformers import pipeline
nlp = pipeline('question-answering', model='lserinol/bert-turkish-question-answering', tokenizer='lserinol/bert-turkish-question-answering')
nlp({
'question': "Ankara'da kaç ilçe vardır?",
'context': r"""Türkiye'nin başken... | {"language": "tr"} | lserinol/bert-turkish-question-answering | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"tr",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #tr #endpoints_compatible #has_space #region-us
|
# bert-turkish-question-answering
## Usage
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fill-mask | transformers |
## Quickstart
**Release 1.1** (February 13, 2021)
Please check also our newer model: [NorBERT 2](https://huggingface.co/ltgoslo/norbert2), trained on a much larger corpus.
Download the model here:
* Cased Norwegian BERT Base: [216.zip](http://vectors.nlpl.eu/repository/20/216.zip)
More about NorBERT training corp... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "pipeline_tag": "fill-mask", "thumbnail": "https://raw.githubusercontent.com/ltgoslo/NorBERT/main/Norbert.png"} | ltg/norbert | null | [
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"tf",
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"no",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06546"
] | [
"no"
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#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #norwegian #no #arxiv-2104.06546 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Quickstart
Release 1.1 (February 13, 2021)
Please check also our newer model: NorBERT 2, trained on a much larger corpus.
Download the model here:
* Cased Norwegian BERT Base: URL
More about NorBERT training corpora and training procedure: URL
Associated code: URL
Check this paper for more details:
_Andrey ... | [
"## Quickstart\n\nRelease 1.1 (February 13, 2021)\n\nPlease check also our newer model: NorBERT 2, trained on a much larger corpus.\n\nDownload the model here:\n\n* Cased Norwegian BERT Base: URL\n\nMore about NorBERT training corpora and training procedure: URL\n\nAssociated code: URL\n\nCheck this paper for more ... | [
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"## Quickstart\n\nRelease 1.1 (February 13, 2021)\n\nPlease check also our newer model: NorBERT 2, trained on a much large... |
fill-mask | transformers | ## Quickstart
**Release 2.0** (February 7, 2022)
Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.
Download the model here:
* Cased Norwegian BERT Base 2.0 (NorBERT 2): [221.zip](http://vectors.nlpl.eu/r... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "pipeline_tag": "fill-mask", "thumbnail": "https://raw.githubusercontent.com/ltgoslo/NorBERT/main/Norbert.png", "widget": [{"text": "N\u00e5 \u00f8nsker de seg en [MASK] bolig. "}]} | ltg/norbert2 | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Quickstart
Release 2.0 (February 7, 2022)
Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.
Download the model here:
* Cased Norwegian BERT Base 2.0 (NorBERT 2): URL
More about NorBERT training corpo... | [
"## Quickstart\nRelease 2.0 (February 7, 2022)\n\nTrained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).\nFeatures a 50 000 words vocabulary and was trained using Whole Word Masking.\n\nDownload the model here:\n* Cased Norwegian BERT Base 2.0 (NorBERT 2): URL\n\nMore about NorBERT ... | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Quickstart\nRelease 2.0 (February 7, 2022)\n\nTrained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).\nFeatur... |
fill-mask | transformers | hello
| {} | ltrctelugu/ltrc-albert | null | [
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"pytorch",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
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fill-mask | transformers | hello
| {} | ltrctelugu/ltrc-distilbert | null | [
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"distilbert",
"fill-mask",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
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fill-mask | transformers | RoBERTa trained on 8.8 Million Telugu Sentences
| {} | ltrctelugu/ltrc-roberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa trained on 8.8 Million Telugu Sentences
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# DialoGPT-Elon: Chat with Elon Musk
This is an attempt to create an AI replica of Elon Musk. The bot's conversation abilities come from Microsoft's [DialoGPT conversational model](https://huggingface.co/microsoft/DialoGPT-medium) fine-tuned on conversation transcripts of Elon's interviews on [Clubhouse](https://zame... | {"tags": ["conversational"]} | luca-martial/DialoGPT-Elon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DialoGPT-Elon: Chat with Elon Musk
This is an attempt to create an AI replica of Elon Musk. The bot's conversation abilities come from Microsoft's DialoGPT conversational model fine-tuned on conversation transcripts of Elon's interviews on Clubhouse, the Lex Fridman podcast and the Joe Rogan Experience.
I also bui... | [
"# DialoGPT-Elon: Chat with Elon Musk\n\nThis is an attempt to create an AI replica of Elon Musk. The bot's conversation abilities come from Microsoft's DialoGPT conversational model fine-tuned on conversation transcripts of Elon's interviews on Clubhouse, the Lex Fridman podcast and the Joe Rogan Experience.\n\nI ... | [
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"# DialoGPT-Elon: Chat with Elon Musk\n\nThis is an attempt to create an AI replica of Elon Musk. The bot's conversation abilities come from Micros... |
text-generation | transformers |
# Yoda DiaglogGPT model | {"tags": ["conversational"]} | lucas-bo/DialogGPT-small-yoda | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Yoda DiaglogGPT model | [
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"# Yoda DiaglogGPT model"
] |
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"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | lucasmtz/distilbert-base-uncased-finetuned-ner | null | [
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0610
* Precision: 0.9252
* Recall: 0.9370
* F1: 0.9311
* Accuracy: 0.9834
Model des... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers |
# bert-base-cased-ag-news
BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.
### How to use
Here is how to use this model to classify a given text:
```python
from transformers import AutoTokenizer, BertForSequenceClassification
to... | {"language": ["en"], "license": "mit", "tags": ["bert", "classification"], "datasets": ["ag_news"], "metrics": ["accuracy", "f1", "recall", "precision"], "widget": [{"text": "Is it soccer or football?", "example_title": "Sports"}, {"text": "A new version of Ubuntu was released.", "example_title": "Sci/Tech"}]} | lucasresck/bert-base-cased-ag-news | null | [
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"bert",
"text-classification",
"classification",
"en",
"dataset:ag_news",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #classification #en #dataset-ag_news #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-cased-ag-news
BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.
### How to use
Here is how to use this model to classify a given text:
### Limitations and bias
Bias were not assessed in this model, but, considering... | [
"# bert-base-cased-ag-news\n\nBERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.",
"### How to use\n\nHere is how to use this model to classify a given text:",
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"# bert-base-cased-ag-news\n\nBERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0... |
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": []}]} | lucasresck/distilbert-base-uncased-finetuned-squad | null | [
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"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
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"## Model description... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624
## Validation Metrics
- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.702830916179100... | {"language": "en", "tags": "autonlp", "datasets": ["lucianpopa/autonlp-data-SST1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 49.618294309910624} | lucianpopa/autonlp-SST1-529214890 | null | [
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"roberta",
"text-classification",
"autonlp",
"en",
"dataset:lucianpopa/autonlp-data-SST1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-lucianpopa/autonlp-data-SST1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624
## Validation Metrics
- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.702830916179100... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 529214890\n- CO2 Emissions (in gram... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 551215591
- CO2 Emissions (in grams): 8.883161797287569
## Validation Metrics
- Loss: 0.08821876347064972
- Accuracy: 0.969531605275125
- Precision: 0.9734313841774404
- Recall: 0.9710127780407004
- AUC: 0.9949152422763072
- F1: 0.97222... | {"language": "en", "tags": "autonlp", "datasets": ["lucianpopa/autonlp-data-SST2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 8.883161797287569} | lucianpopa/autonlp-SST2-551215591 | null | [
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] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 551215591
- CO2 Emissions (in grams): 8.883161797287569
## Validation Metrics
- Loss: 0.08821876347064972
- Accuracy: 0.969531605275125
- Precision: 0.9734313841774404
- Recall: 0.9710127780407004
- AUC: 0.9949152422763072
- F1: 0.97222... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 551215591\n- CO2 Emissions (in grams)... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715
## Validation Metrics
- Loss: 0.24612033367156982
- Accuracy: 0.9643183897529735
- Macro F1: 0.9493690949638435
- Micro F1: 0.9643183897529735
- Weighted F1: 0.96423841628372... | {"language": "en", "tags": "autonlp", "datasets": ["lucianpopa/autonlp-data-TREC-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 15.186006626915715} | lucianpopa/autonlp-TREC-classification-522314623 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715
## Validation Metrics
- Loss: 0.24612033367156982
- Accuracy: 0.9643183897529735
- Macro F1: 0.9493690949638435
- Micro F1: 0.9643183897529735
- Weighted F1: 0.96423841628372... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 522314623\n- CO2 Emi... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-rw
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kinyarwanda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using about 25% of the training data (limited to utterances without downvotes and shorter with... | {"language": "rw", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large Kinyarwanda with apostrophes", "results": [{"task": {"type": "automatic-speech-recognition", "... | lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied | null | [
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"rw",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"rw"
] | TAGS
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|
# Wav2Vec2-Large-XLSR-53-rw
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kinyarwanda using the Common Voice dataset, using about 25% of the training data (limited to utterances without downvotes and shorter with 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the lucio/wav2vec2... | [
"# Wav2Vec2-Large-XLSR-53-rw\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kinyarwanda using the Common Voice dataset, using about 25% of the training data (limited to utterances without downvotes and shorter with 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the lucio/wa... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #rw #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-rw\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kinyarwanda u... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-rw
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kinyarwanda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using about 20% of the training data (limited to utterances without downvotes and shorter than... | {"language": "rw", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large Kinyarwanda no punctuation", "results": [{"task": {"type": "automatic-speech-recognition", "na... | lucio/wav2vec2-large-xlsr-kinyarwanda | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"rw"
] | TAGS
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|
# Wav2Vec2-Large-XLSR-53-rw
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kinyarwanda using the Common Voice dataset, using about 20% of the training data (limited to utterances without downvotes and shorter than 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the lucio/wav2vec2... | [
"# Wav2Vec2-Large-XLSR-53-rw\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kinyarwanda using the Common Voice dataset, using about 20% of the training data (limited to utterances without downvotes and shorter than 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the lucio/wa... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #rw #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-rw\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kinyarwanda u... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-lg
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Luganda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using train, validation and other (excluding voices that are in the test set), and taking the test... | {"language": "lg", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large Luganda by Lucio", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spee... | lucio/wav2vec2-large-xlsr-luganda | null | [
"transformers",
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"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lg",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lg"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lg #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-lg
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Luganda using the Common Voice dataset, using train, validation and other (excluding voices that are in the test set), and taking the test data for validation as well as test.
When using this model, make sure that your speech input is sampled a... | [
"# Wav2Vec2-Large-XLSR-53-lg\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Luganda using the Common Voice dataset, using train, validation and other (excluding voices that are in the test set), and taking the test data for validation as well as test.\nWhen using this model, make sure that your speech input is sa... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lg #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-lg\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Luganda using the Common V... |
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. -->
# XLS-R-300M Kyrgiz CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"language": ["ky"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M Kyrgiz CV8", "results": [{"task"... | lucio/xls-r-kyrgiz-cv8 | null | [
"transformers",
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"tensorboard",
"safetensors",
"wav2vec2",
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"generated_from_trainer",
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"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"ky",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"mod... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ky"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #ky #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLS-R-300M Kyrgiz CV8
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - KY dataset.
It achieves the following results on the validation set:
* Loss: 0.5497
* Wer: 0.2945
* Cer: 0.0791
Model description
-----------------
For a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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 #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #ky #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"... |
automatic-speech-recognition | transformers |
# XLS-R-300M Uyghur CV7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1772
- Wer: 0.2589
## Model description
For a ... | {"language": ["ug"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ug", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{... | lucio/xls-r-uyghur-cv7 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ug",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"base_model:facebook/wav2vec... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ug"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ug #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #... | XLS-R-300M Uyghur CV7
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - UG dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1772
* Wer: 0.2589
Model description
-----------------
For a description o... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ug #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-i... |
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. -->
# XLS-R-300M Uyghur CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"language": ["ug"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "ug"], "datasets": ["mozilla-foundation/common_voice_8_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{... | lucio/xls-r-uyghur-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"ug",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ug"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #ug #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #... | XLS-R-300M Uyghur CV8
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UG dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2026
* Wer: 0.3248
Model description
-----------------
For a description o... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #ug #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-i... |
automatic-speech-recognition | transformers |
# XLS-R-300M Uzbek CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ dataset.
It achieves the following results on the validation set:
- Loss: 0.3063
- Wer: 0.3852
- Cer: 0.0777
## Model descri... | {"language": ["uz"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name"... | lucio/xls-r-uzbek-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"uz",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec... | null | 2022-03-02T23:29:05+00:00 | [] | [
"uz"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uz #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #... | XLS-R-300M Uzbek CV8
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UZ dataset.
It achieves the following results on the validation set:
* Loss: 0.3063
* Wer: 0.3852
* Cer: 0.0777
Model description
-----------------
For a d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uz #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-i... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | lucius/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
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 #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | lucius/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8340
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-generation | transformers | ## Model description
**DeepMetal** is a model capable of generating lyrics taylored for heavy metal songs.
The model is based on the [OpenAI GPT-2](https://huggingface.co/gpt2) and has been finetuned on a dataset of 141,718 heavy metal songs lyrics.
More info about the project can be found in the [official GitHub repo... | {} | lucone83/deep-metal | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Model description
DeepMetal is a model capable of generating lyrics taylored for heavy metal songs.
The model is based on the OpenAI GPT-2 and has been finetuned on a dataset of 141,718 heavy metal songs lyrics.
More info about the project can be found in the official GitHub repository and in the related articles o... | [
"## Model description\n\nDeepMetal is a model capable of generating lyrics taylored for heavy metal songs.\nThe model is based on the OpenAI GPT-2 and has been finetuned on a dataset of 141,718 heavy metal songs lyrics.\nMore info about the project can be found in the official GitHub repository and in the related a... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model description\n\nDeepMetal is a model capable of generating lyrics taylored for heavy metal songs.\nThe model is based on the OpenAI GPT-2 and has been finet... |
text-generation | transformers |
# Kujou Sara bot
| {"tags": ["conversational"]} | ludowoods/KujouSara | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Kujou Sara bot
| [
"# Kujou Sara bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kujou Sara bot"
] |
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. -->
# luheng/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknow... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "luheng/bert-finetuned-ner", "results": []}]} | luheng/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| luheng/bert-finetuned-ner
=========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0280
* Validation Loss: 0.0569
* Epoch: 2
Model description
-----------------
More information needed
Intend... | [
"### 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': 2634, '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\\_... |
question-answering | transformers |
## Chinese MRC macbert-large
* 使用大量中文MRC数据训练的macbert-large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/>
（已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁）
| 模型/数据集 | Dureader-2021 | tencentmedical |
| -----------------------... | {"language": ["zh"], "license": "apache-2.0"} | luhua/chinese_pretrain_mrc_macbert_large | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #question-answering #zh #license-apache-2.0 #endpoints_compatible #region-us
| Chinese MRC macbert-large
-------------------------
* 使用大量中文MRC数据训练的macbert-large模型，详情可查看：URL
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高
（已有多位小伙伴在Dureader-2021等多个比赛中取得top5的成绩）
模型/数据集: , Dureader-2021: F1-score, tencentmedical: Accuracy
模型/数据集: , Dureader-2021: dev / A榜, tencentmedical: test-1
模型/数据集: macbert-large (哈工大预训... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #zh #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
## Chinese MRC roberta_wwm_ext_large
* 使用大量中文MRC数据训练的roberta_wwm_ext_large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/>
（已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁）
| 模型/数据集 | Dureader-2021 | tencentmedical |
| -------... | {"language": ["zh"], "license": "apache-2.0"} | luhua/chinese_pretrain_mrc_roberta_wwm_ext_large | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #question-answering #zh #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Chinese MRC roberta\_wwm\_ext\_large
------------------------------------
* 使用大量中文MRC数据训练的roberta\_wwm\_ext\_large模型，详情可查看：URL
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高
（已有多位小伙伴在Dureader-2021等多个比赛中取得top5的成绩）
模型/数据集: , Dureader-2021: F1-score, tencentmedical: Accuracy
模型/数据集: , Dureader-2021: dev / A榜, tencentmedical: te... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #zh #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
null | null | Testing | {} | luisperez123/test2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Testing | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | This model was made for a project in the NLP group of the Technology and Artificial Intelligence League (TAIL).
We try to predict a music genre from the lyrics. | {} | luiz826/roberta-to-music-genre | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model was made for a project in the NLP group of the Technology and Artificial Intelligence League (TAIL).
We try to predict a music genre from the lyrics. | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Generate the conclusion of an argument
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of ... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-conclusion-bias-only | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate the conclusion of an argument
This model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne research project evaluat... | [
"# Generate the conclusion of an argument\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne research project e... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate the conclusion of an argument\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been... |
text-generation | transformers |
# Generate the conclusion of an argument
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-conclusion-full-finetune | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate the conclusion of an argument
This model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne research project evaluating how large l... | [
"# Generate the conclusion of an argument\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne research project evaluating how l... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate the conclusion of an argument\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on t... |
text-generation | transformers |
# Generate the conclusion of an argument
This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating the conclusion of an argument given its premises. It was trained as part of a Uni... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-conclusion-soft | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate the conclusion of an argument
This model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne research project evaluating h... | [
"# Generate the conclusion of an argument\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne research project evalua... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate the conclusion of an argument\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been opti... |
text-generation | transformers |
# Generate a chain of reasoning from one claim to another
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins on... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-intermediary-claims-bias-only | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate a chain of reasoning from one claim to another
This model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a Un... | [
"# Generate a chain of reasoning from one claim to another\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part o... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate a chain of reasoning from one claim to another\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not w... |
text-generation | transformers |
# Generate a chain of reasoning from one claim to another
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to anot... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-intermediary-claims-full-finetune | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate a chain of reasoning from one claim to another
This model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a University of Mel... | [
"# Generate a chain of reasoning from one claim to another\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a University ... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate a chain of reasoning from one claim to another\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have be... |
text-generation | transformers |
# Generate a chain of reasoning from one claim to another
This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating a sequence of claims (a 'chain of reasoning') that joins one cla... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-intermediary-claims-soft | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate a chain of reasoning from one claim to another
This model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a Univers... | [
"# Generate a chain of reasoning from one claim to another\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a U... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate a chain of reasoning from one claim to another\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt wh... |
text-generation | transformers |
# Generate objections to a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. I... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-objections-bias-only | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate objections to a claim
This model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne... | [
"# Generate objections to a claim\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Mel... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate objections to a claim\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetun... |
text-generation | transformers |
# Generate objections to a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained a... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-objections-full-finetune | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate objections to a claim
This model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne research proje... | [
"# Generate objections to a claim\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne research... | [
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"# Generate objections to a claim\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task ... |
text-generation | transformers |
# Generate objections to a claim
This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating the objections to a claim, optionally given some example objections to that claim. It was... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-objections-soft | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate objections to a claim
This model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne rese... | [
"# Generate objections to a claim\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourn... | [
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"# Generate objections to a claim\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on... |
text-generation | transformers |
# Generate reasons that support a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was t... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-reasons-bias-only | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate reasons that support a claim
This model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne resear... | [
"# Generate reasons that support a claim\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne ... | [
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"# Generate reasons that support a claim\n\nThis model is a version of 'gpt-neo-2.7B', where some parameters (only the bias parameters, not weights) have been ... |
text-generation | transformers |
# Generate reasons that support a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part ... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-reasons-full-finetune | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate reasons that support a claim
This model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne research project eval... | [
"# Generate reasons that support a claim\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne research projec... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate reasons that support a claim\n\nThis model is a version of 'gpt-neo-2.7B', where all parameters (both weights and biases) have been finetuned on th... |
text-generation | transformers |
# Generate reasons that support a claim
This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating reasons that support a claim, optionally given some example reasons. It was traine... | {"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]} | luke-thorburn/suggest-reasons-soft | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Generate reasons that support a claim
This model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne research pr... | [
"# Generate reasons that support a claim\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optimized on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne resea... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Generate reasons that support a claim\n\nThis model has the same model parameters as 'gpt-neo-2.7B', but with an additional soft prompt which has been optim... |
text-generation | transformers |
# Kokkoro DialoGPT Model | {"tags": ["conversational"]} | lulueve3/DialoGPT-medium-Kokkoro | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Kokkoro DialoGPT Model | [
"# Kokkoro DialoGPT Model"
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kokkoro DialoGPT Model"
] |
text-generation | transformers |
# Kokkoro DialoGPT Model | {"tags": ["conversational"]} | lulueve3/DialoGPT-medium-Kokkoro2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Kokkoro DialoGPT Model | [
"# Kokkoro DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kokkoro DialoGPT Model"
] |
text-classification | transformers | # Vent-roBERTa-emotion
This is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see https://arxiv.org/abs/1901.04856). The Vent dataset contains 33 million posts annotated with one emotion by the user themselves. <br/>
The model was trained to recognize ... | {} | lumalik/vent-roberta-emotion | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:1901.04856",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1901.04856"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #arxiv-1901.04856 #autotrain_compatible #endpoints_compatible #region-us
| # Vent-roBERTa-emotion
This is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see URL The Vent dataset contains 33 million posts annotated with one emotion by the user themselves. <br/>
The model was trained to recognize 5 emotions ("Affection", "Anger... | [
"# Vent-roBERTa-emotion\n\nThis is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see URL The Vent dataset contains 33 million posts annotated with one emotion by the user themselves. <br/>\n \nThe model was trained to recognize 5 emotions (\"Affectio... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #arxiv-1901.04856 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Vent-roBERTa-emotion\n\nThis is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see URL The Vent dataset contai... |
null | null | # Pre-trained AudioNet-CTC models for the GRID audio dataset with icefall.
The model was trained on full [GRID](https://zenodo.org/record/3625687#.Ybn7HagzY2w) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/grid/AVSR/audionet_ctc_asr) for more ... | {} | luomingshuang/icefall_asr_grid_audionet_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Pre-trained AudioNet-CTC models for the GRID audio dataset with icefall.
========================================================================
The model was trained on full GRID with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training procedure
--------... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Pre-trained TDNN-LiGRU-CTC models for the TIMIT dataset with icefall.
The model was trained on full [TIMIT](https://data.deepai.org/timit.zip) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/timit/ASR/tdnn_ligru_ctc) for more details of this m... | {} | luomingshuang/icefall_asr_timit_tdnn_ligru_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Pre-trained TDNN-LiGRU-CTC models for the TIMIT dataset with icefall.
=====================================================================
The model was trained on full TIMIT with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training procedure
-------------... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Pre-trained TDNN-LSTM-CTC models for the TIMIT dataset with icefall.
The model was trained on full [TIMIT](https://data.deepai.org/timit.zip) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/timit/ASR/tdnn_lstm_ctc) for more details of this mod... | {} | luomingshuang/icefall_asr_timit_tdnn_lstm_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Pre-trained TDNN-LSTM-CTC models for the TIMIT dataset with icefall.
====================================================================
The model was trained on full TIMIT with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training procedure
---------------... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Pre-trained CombineNet-CTC models for the GRID audio-visual dataset with icefall.
The model was trained on full [GRID](https://zenodo.org/record/3625687#.Ybn7HagzY2w) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/grid/AVSR/combinenet_ctc_avs... | {} | luomingshuang/icefall_avsr_grid_combinenet_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Pre-trained CombineNet-CTC models for the GRID audio-visual dataset with icefall.
=================================================================================
The model was trained on full GRID with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training ... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Pre-trained VisualNet2-CTC models for the GRID visual dataset with icefall.
The model was trained on full [GRID](https://zenodo.org/record/3625687#.Ybn7HagzY2w) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/grid/AVSR/visualnet2_ctc_asr) for ... | {} | luomingshuang/icefall_vsr_grid_visualnet2_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Pre-trained VisualNet2-CTC models for the GRID visual dataset with icefall.
===========================================================================
The model was trained on full GRID with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training procedure
--... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Pre-trained VisualNet-CTC models for the GRID visual dataset with icefall.
The model was trained on full [GRID](https://zenodo.org/record/3625687#.Ybn7HagzY2w) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/grid/AVSR/visualnet_ctc_asr) for mo... | {} | luomingshuang/icefall_vsr_grid_visualnet_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Pre-trained VisualNet-CTC models for the GRID visual dataset with icefall.
==========================================================================
The model was trained on full GRID with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training procedure
----... | [] | [
"TAGS\n#region-us \n"
] |
null | null | This is Contract Token Classification Model | {} | luoren000/Token_Classification | null | [
"tensorboard",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #region-us
| This is Contract Token Classification Model | [] | [
"TAGS\n#tensorboard #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-emotion2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotio... | lvargas/distilbert-base-uncased-finetuned-emotion2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion2
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3623
* Accuracy: 0.903
* F1: 0.9003
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers | # BERT-IMDB
## What is it?
BERT (`bert-large-cased`) trained for sentiment classification on the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
## Training setting
The model was trained on 80% of the IMDB dataset for sentiment classification for three epochs with a learning... | {} | lvwerra/bert-imdb | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # BERT-IMDB
## What is it?
BERT ('bert-large-cased') trained for sentiment classification on the IMDB dataset.
## Training setting
The model was trained on 80% of the IMDB dataset for sentiment classification for three epochs with a learning rate of '1e-5' with the 'simpletransformers' library. The library uses a le... | [
"# BERT-IMDB",
"## What is it?\nBERT ('bert-large-cased') trained for sentiment classification on the IMDB dataset.",
"## Training setting\n\nThe model was trained on 80% of the IMDB dataset for sentiment classification for three epochs with a learning rate of '1e-5' with the 'simpletransformers' library. The l... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-IMDB",
"## What is it?\nBERT ('bert-large-cased') trained for sentiment classification on the IMDB dataset.",
"## Training setting\n\nThe model was trained on 80% of the IMDB datas... |
text-generation | transformers |
# CodeParrot 🦜 (small)
CodeParrot 🦜 is a GPT-2 model (110M parameters) trained to generate Python code.
## Usage
You can load the CodeParrot model and tokenizer directly in `transformers`:
```Python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("codeparr... | {"language": ["code"], "license": "apache-2.0", "tags": ["code", "gpt2", "generation"], "datasets": ["codeparrot/codeparrot-clean", "openai_humaneval"], "metrics": ["evaluate-metric/code_eval"]} | codeparrot/codeparrot-small | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"code",
"generation",
"dataset:codeparrot/codeparrot-clean",
"dataset:openai_humaneval",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean #dataset-openai_humaneval #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| CodeParrot (small)
==================
CodeParrot is a GPT-2 model (110M parameters) trained to generate Python code.
Usage
-----
You can load the CodeParrot model and tokenizer directly in 'transformers':
or with a 'pipeline':
Training
--------
The model was trained on the cleaned CodeParrot dataset with th... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean #dataset-openai_humaneval #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# CodeParrot 🦜
CodeParrot 🦜 is a GPT-2 model (1.5B parameters) trained to generate Python code. After the initial training and release of v1.0 we trained the model some more and released v1.1 (see below for details).
## Usage
You can load the CodeParrot model and tokenizer directly in `transformers`:
```Python
... | {"language": "code", "tags": ["code", "gpt2", "generation"], "datasets": ["codeparrot/codeparrot-clean-train"], "widget": [{"text": "from transformer import", "example_title": "Transformers"}, {"text": "def print_hello_world():\n\t", "example_title": "Hello World!"}, {"text": "def get_file_size(filepath):", "example_ti... | codeparrot/codeparrot | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"code",
"generation",
"dataset:codeparrot/codeparrot-clean-train",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean-train #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| CodeParrot
==========
CodeParrot is a GPT-2 model (1.5B parameters) trained to generate Python code. After the initial training and release of v1.0 we trained the model some more and released v1.1 (see below for details).
Usage
-----
You can load the CodeParrot model and tokenizer directly in 'transformers':
or... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean-train #model-index #autotrain_compatible #endpoints_compatible #has_space #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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"t... | lvwerra/distilbert-imdb | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-imdb
===============
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset (training notebook is here).
It achieves the following results on the evaluation set:
* Loss: 0.1903
* Accuracy: 0.928
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: 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 #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\... |
null | transformers | # GPT2-IMDB-ctrl
## What is it?
A small GPT2 (`lvwerra/gpt2-imdb`) language model fine-tuned to produce controlled movie reviews based the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). The model is trained with rewards from a BERT sentiment classifier (`lvwerra/bert-imdb`) v... | {} | lvwerra/gpt2-imdb-ctrl | null | [
"transformers",
"pytorch",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us
| GPT2-IMDB-ctrl
==============
What is it?
-----------
A small GPT2 ('lvwerra/gpt2-imdb') language model fine-tuned to produce controlled movie reviews based the IMDB dataset. The model is trained with rewards from a BERT sentiment classifier ('lvwerra/bert-imdb') via PPO.
Training setting
----------------
The m... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | # GPT2-IMDB-pos
## What is it?
A small GPT2 (`lvwerra/gpt2-imdb`) language model fine-tuned to produce positive movie reviews based the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). The model is trained with rewards from a BERT sentiment classifier (`lvwerra/gpt2-imdb`) via ... | {} | lvwerra/gpt2-imdb-pos | null | [
"transformers",
"pytorch",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us
| GPT2-IMDB-pos
=============
What is it?
-----------
A small GPT2 ('lvwerra/gpt2-imdb') language model fine-tuned to produce positive movie reviews based the IMDB dataset. The model is trained with rewards from a BERT sentiment classifier ('lvwerra/gpt2-imdb') via PPO.
Training setting
----------------
The model... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # GPT2-IMDB
## What is it?
A GPT2 (`gpt2`) language model fine-tuned on the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
## Training setting
The GPT2 language model was fine-tuned for 1 epoch on the IMDB dataset. All comments were joined into a single text file separated ... | {} | lvwerra/gpt2-imdb | null | [
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"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # GPT2-IMDB
## What is it?
A GPT2 ('gpt2') language model fine-tuned on the IMDB dataset.
## Training setting
The GPT2 language model was fine-tuned for 1 epoch on the IMDB dataset. All comments were joined into a single text file separated by the EOS token:
To train the model the 'run_language_modeling.py' scrip... | [
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"## What is it?\nA GPT2 ('gpt2') language model fine-tuned on the IMDB dataset.",
"## Training setting\n\nThe GPT2 language model was fine-... |
text-generation | transformers | # GPT-2 (medium) Taboo
## What is it?
A fine-tuned GPT-2 version for Taboo cards generation.
## Training setting
The model was trained on ~900 Taboo cards in the following format for 100 epochs:
```
Describe the word Glitch without using the words Problem, Unexpected, Technology, Minor, Outage.
````
| {} | lvwerra/gpt2-medium-taboo | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT-2 (medium) Taboo
## What is it?
A fine-tuned GPT-2 version for Taboo cards generation.
## Training setting
The model was trained on ~900 Taboo cards in the following format for 100 epochs:
'
| [
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"## What is it?\nA fine-tuned GPT-2 version for Taboo cards generation.",
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | lvwerra/pegasus-samsum | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4177
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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* lr\\_scheduler\\_warmup\\_steps: ... | [
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token-classification | transformers |
# Multilingual-Metaphor-Detection
This page provides a fine-tuned multilingual language model [XLM-RoBERTa](https://arxiv.org/pdf/1911.02116.pdf) for metaphor detection on a token-level using the [Huggingface token-classification approach](https://huggingface.co/tasks/token-classification). Label 1 corresponds to met... | {"license": "cc-by-nc-sa-3.0", "metrics": ["f1", "accuracy"], "widget": [{"text": "We are at a relationship crossroad", "example_title": "Metaphoric1"}, {"text": "The car waits at a crossroad", "example_title": "Literal1"}, {"text": "I win the argument", "example_title": "Metaphoric2"}, {"text": "I win the game", "exam... | lwachowiak/Metaphor-Detection-XLMR | null | [
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"token-classification",
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"license:cc-by-nc-sa-3.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.02116"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #arxiv-1911.02116 #license-cc-by-nc-sa-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Multilingual-Metaphor-Detection
This page provides a fine-tuned multilingual language model XLM-RoBERTa for metaphor detection on a token-level using the Huggingface token-classification approach. Label 1 corresponds to metaphoric usage.
# Dataset
The dataset the model is trained on is the VU Amsterdam Metaphor Co... | [
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"# Multilingual-Metaphor-Detection\n\nThis page provides a fine-tuned multilingual language model XLM-RoBERTa for metaphor detection on a token-le... |
text-generation | transformers | # ArXiv-NLP GPT-2 checkpoint
This is a GPT-2 small checkpoint for PyTorch. It is the official `gpt2-small` fine-tuned to ArXiv paper on the computational linguistics field.
## Training data
This model was trained on a subset of ArXiv papers that were parsed from PDF to txt. The resulting data is made of 80MB of text... | {"language": "en"} | lysandre/arxiv-nlp | null | [
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"pytorch",
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"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # ArXiv-NLP GPT-2 checkpoint
This is a GPT-2 small checkpoint for PyTorch. It is the official 'gpt2-small' fine-tuned to ArXiv paper on the computational linguistics field.
## Training data
This model was trained on a subset of ArXiv papers that were parsed from PDF to txt. The resulting data is made of 80MB of text... | [
"# ArXiv-NLP GPT-2 checkpoint\n\nThis is a GPT-2 small checkpoint for PyTorch. It is the official 'gpt2-small' fine-tuned to ArXiv paper on the computational linguistics field.",
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null | transformers | # ArXiv GPT-2 checkpoint
This is a GPT-2 small checkpoint for PyTorch. It is the official `gpt2-small` finetuned to ArXiv paper on physics fields.
## Training data
This model was trained on a subset of ArXiv papers that were parsed from PDF to txt. The resulting data is made of 130MB of text, mostly from quantum phy... | {} | lysandre/arxiv | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #endpoints_compatible #text-generation-inference #region-us
| # ArXiv GPT-2 checkpoint
This is a GPT-2 small checkpoint for PyTorch. It is the official 'gpt2-small' finetuned to ArXiv paper on physics fields.
## Training data
This model was trained on a subset of ArXiv papers that were parsed from PDF to txt. The resulting data is made of 130MB of text, mostly from quantum phy... | [
"# ArXiv GPT-2 checkpoint\n\nThis is a GPT-2 small checkpoint for PyTorch. It is the official 'gpt2-small' finetuned to ArXiv paper on physics fields.",
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"## Training data\n\nThis model was trained on a subs... |
question-answering | allennlp |
Example of AllenNLP question answering model. | {"tags": ["allennlp", "question-answering"]} | lysandre/bidaf-elmo-model-2020.03.19 | null | [
"allennlp",
"question-answering",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#allennlp #question-answering #has_space #region-us
|
Example of AllenNLP question answering model. | [] | [
"TAGS\n#allennlp #question-answering #has_space #region-us \n"
] |
null | null | 4
| {"fastai-version": "v2", "torch-version": "v4"} | lysandre/brand-new-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| 4
| [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# Sentiment Analysis
This is a BERT model fine-tuned for sentiment analysis. | {"language": "en", "license": "apache-2.0", "tags": ["OpenCLIP"], "datasets": ["sst2"]} | lysandre/dum | null | [
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"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #OpenCLIP #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Sentiment Analysis
This is a BERT model fine-tuned for sentiment analysis. | [
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] |
null | null | Files are only in the master branch.
| {} | lysandre/dummy-hf-hub | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Files are only in the master branch.
| [] | [
"TAGS\n#region-us \n"
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text-classification | transformers | # My dummy model
Welcome to my model page!
Central definition, reproducibility tips, code samples below! | {} | lysandre/dummy | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # My dummy model
Welcome to my model page!
Central definition, reproducibility tips, code samples below! | [
"# My dummy model\n\nWelcome to my model page!\n\nCentral definition, reproducibility tips, code samples below!"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# My dummy model\n\nWelcome to my model page!\n\nCentral definition, reproducibility tips, code samples below!"
] |
null | null | # My BERT model :) | {} | lysandre/my-bert-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # My BERT model :) | [
"# My BERT model :)"
] | [
"TAGS\n#region-us \n",
"# My BERT model :)"
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text-classification | transformers | # Dummy model
This is a dummy model. | {} | lysandre/new-dummy-model | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Dummy model
This is a dummy model. | [
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table-question-answering | transformers |
# TAPAS base model fine-tuned on Sequential Question Answering (SQA)
This model has 4 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model wa... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["sqa"]} | lysandre/tapas-temporary-repo | null | [
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"en",
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"arxiv:2004.02349",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
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"en"
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#transformers #pytorch #tapas #table-question-answering #en #dataset-sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS base model fine-tuned on Sequential Question Answering (SQA)
This model has 4 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_sqa_inter_masklm_base_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step ... | [
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"# TAPAS base model fine-tuned on Sequential Question Answering (SQA)\n\nThis model has 4 versions which can be used. The latest version, wh... |
null | null | atesta
| {} | lysandre/test-model | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| atesta
| [] | [
"TAGS\n#has_space #region-us \n"
] |
token-classification | transformers |
# satellite-instrument-bert-NER
For details, please visit the [GitHub link](https://github.com/THU-EarthInformationScienceLab/Satellite-Instrument-NER).
## Citation
Our [paper](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2107098) has been published in the International Journal of Digital Earth :
```bi... | {"language": "pt", "widget": [{"text": "Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering th... | m-lin20/satellite-instrument-bert-NER | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #autotrain_compatible #endpoints_compatible #region-us
|
# satellite-instrument-bert-NER
For details, please visit the GitHub link.
Our paper has been published in the International Journal of Digital Earth :
| [
"# satellite-instrument-bert-NER\nFor details, please visit the GitHub link.\n\nOur paper has been published in the International Journal of Digital Earth :"
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"TAGS\n#transformers #pytorch #bert #token-classification #pt #autotrain_compatible #endpoints_compatible #region-us \n",
"# satellite-instrument-bert-NER\nFor details, please visit the GitHub link.\n\nOur paper has been published in the International Journal of Digital Earth :"
] |
token-classification | transformers |
# satellite-instrument-roberta-NER
For details, please visit the [GitHub link](https://github.com/THU-EarthInformationScienceLab/Satellite-Instrument-NER).
## Citation
Our [paper](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2107098) has been published in the International Journal of Digital Earth :
``... | {"language": "pt", "widget": [{"text": "Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering th... | m-lin20/satellite-instrument-roberta-NER | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #roberta #token-classification #pt #autotrain_compatible #endpoints_compatible #region-us
|
# satellite-instrument-roberta-NER
For details, please visit the GitHub link.
Our paper has been published in the International Journal of Digital Earth :
| [
"# satellite-instrument-roberta-NER\nFor details, please visit the GitHub link.\n\nOur paper has been published in the International Journal of Digital Earth :"
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #pt #autotrain_compatible #endpoints_compatible #region-us \n",
"# satellite-instrument-roberta-NER\nFor details, please visit the GitHub link.\n\nOur paper has been published in the International Journal of Digital Earth :"
] |
text-classification | transformers |
# distilbert-political-tweets 🗣 🇺🇸
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [m-newhauser/senator-tweets](https://huggingface.co/datasets/m-newhauser/senator-tweets) dataset, which contains all tweets made by United States senators during... | {"language": ["en"], "license": "lgpl-3.0", "library_name": "transformers", "tags": ["text-classification", "transformers", "pytorch", "generated_from_keras_callback"], "datasets": ["m-newhauser/senator-tweets"], "metrics": ["accuracy", "f1"], "widget": [{"text": "This pandemic has shown us clearly the vulgarity of our... | m-newhauser/distilbert-political-tweets | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"en",
"dataset:m-newhauser/senator-tweets",
"license:lgpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #generated_from_keras_callback #en #dataset-m-newhauser/senator-tweets #license-lgpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# distilbert-political-tweets 🇺🇸
This model is a fine-tuned version of distilbert-base-uncased on the m-newhauser/senator-tweets dataset, which contains all tweets made by United States senators during the first year of the Biden Administration.
It achieves the following results on the evaluation set:
* Accuracy: ... | [
"# distilbert-political-tweets 🇺🇸\n\nThis model is a fine-tuned version of distilbert-base-uncased on the m-newhauser/senator-tweets dataset, which contains all tweets made by United States senators during the first year of the Biden Administration.\nIt achieves the following results on the evaluation set:\n* Ac... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #generated_from_keras_callback #en #dataset-m-newhauser/senator-tweets #license-lgpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# distilbert-political-tweets 🇺🇸\n\nThis model is a fine-tuned version of distilber... |
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-clf-digimag | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### DigiMag\n\n\nA total of 8,515 articles scraped from Digikala Online Magazine. This dataset includes seven different classes.\n\n\n1. Video Games\n2. Shopping Guide\n3. Health Beauty\n4. Science Technology\n5. General\n6. Art Cinema\n7. Books Literature\n\n\n\nDownload\nYou can download the dataset from here\n\... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### DigiMag\n\n\nA total of 8,515 articles scraped from Digikala Online Magazine. This dataset includes seven different classes.\n\n\n1. Video Games\n2. Shopping Guid... |
text-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-clf-persiannews | null | [
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### Persian News\n\n\nA dataset of various news articles scraped from different online news agencies' websites. The total number of articles is 16,438, spread over eight different classes.\n\n\n1. Economic\n2. International\n3. Political\n4. Science Technology\n5. Cultural Art\n6. Sport\n7. Medical\n\n\n\nDownload... | [
"TAGS\n#transformers #pytorch #tf #albert #text-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Persian News\n\n\nA dataset of various news articles scraped from different online news agencies' websites. The total number of articles is 16,438, spread over ei... |
token-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-ner-arman | null | [
"transformers",
"pytorch",
"tf",
"albert",
"token-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event\n5. Product\n6. Person\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n\n\nThe following table summarizes the F1 score... | [
"TAGS\n#transformers #pytorch #tf #albert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### ARMAN\n\n\nARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.\n\n\n1. Organization\n2. Location\n3. Facility\n4. Event... |
token-classification | transformers |
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Versio... | {"language": "fa", "license": "apache-2.0"} | m3hrdadfi/albert-fa-base-v2-ner-peyma | null | [
"transformers",
"pytorch",
"tf",
"albert",
"token-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #albert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Persian
==============
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
>
> میتونی بهش بگی برت\_کوچولو
>
>
>
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various... | [
"### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organization\n2. Money\n3. Location\n4. Date\n5. Time\n6. Person\n7. Percent\n\n\n\nDownload\nYou can download the dataset from here\n\n\nResults\n-------\n... | [
"TAGS\n#transformers #pytorch #tf #albert #token-classification #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### PEYMA\n\n\nPEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.\n\n\n1. Organi... |
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