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null | transformers |
# Cour de Cassation semi-automatic *titrage* prediction model
Model for the semi-automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases).
The models are similar to the automatic models described in [this paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-... | {"language": "fr", "license": "cc-by-4.0"} | rbawden/CCASS-semi-auto-titrages-base | null | [
"transformers",
"pytorch",
"fsmt",
"fr",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-16T08:32:27+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #fsmt #fr #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
|
# Cour de Cassation semi-automatic *titrage* prediction model
Model for the semi-automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases).
The models are similar to the automatic models described in this paper and to the model available here. If you use this semi-automatic m... | [
"# Cour de Cassation semi-automatic *titrage* prediction model\n\nModel for the semi-automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). \n\nThe models are similar to the automatic models described in this paper and to the model available here. If you use this semi-aut... | [
"TAGS\n#transformers #pytorch #fsmt #fr #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n",
"# Cour de Cassation semi-automatic *titrage* prediction model\n\nModel for the semi-automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). \n\nThe models are sim... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 80.4 | 80.4 |
| test | 80.6 | 80.6 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-repnum_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T08:37:43+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 80.4, F1macro: 80.4
Set: test, F1micro: 80.6, F1macro: 80.6
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | Model for API: https://github.com/eleldar/Punctuation | {} | eleldar/repunct-model_ft | null | [
"region:us"
] | null | 2022-06-16T08:38:08+00:00 | [] | [] | TAGS
#region-us
| Model for API: URL | [] | [
"TAGS\n#region-us \n"
] |
null | null |
# Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Data
## Overview
**Authors**
[Sungwon Kim](ksw0306@snu.ac.kr)
[Heeseung Kim](gmltmd789@snu.ac.kr)
[Sungroh Yoon](sryoon@snu.ac.kr)
**Abstract**
*We propose Guided-TTS 2, a diffusion-based generative model for high-qualit... | {"languages": ["en"], "extra_gated_prompt": "Guided-TTS-2 is intended for research purposes only. By clicking on \"Request access\", the user agrees to the following terms and conditions.\nTerms of Access:\nThe \"Researcher\" has requested permission to use Guided-TTS (\"the Model\"). In exchange for such permission, R... | snu-ai/guided-tts2 | null | [
"region:us"
] | null | 2022-06-16T09:10:11+00:00 | [] | [] | TAGS
#region-us
|
# Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Data
## Overview
Authors
Sungwon Kim
Heeseung Kim
Sungroh Yoon
Abstract
*We propose Guided-TTS 2, a diffusion-based generative model for high-quality adaptive TTS using untranscribed data. Guided-TTS 2 combines a speaker... | [
"# Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Data",
"## Overview\n\nAuthors\n\nSungwon Kim\nHeeseung Kim\nSungroh Yoon\n\nAbstract\n\n*We propose Guided-TTS 2, a diffusion-based generative model for high-quality adaptive TTS using untranscribed data. Guided-TTS 2 ... | [
"TAGS\n#region-us \n",
"# Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Data",
"## Overview\n\nAuthors\n\nSungwon Kim\nHeeseung Kim\nSungroh Yoon\n\nAbstract\n\n*We propose Guided-TTS 2, a diffusion-based generative model for high-quality adaptive TTS using untransc... |
feature-extraction | transformers | Model for API: https://github.com/eleldar/Punctuation | {} | eleldar/rubert-base-cased-sentence | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T09:30:20+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| Model for API: URL | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "results", "results": []}]} | anantoj/T5-summarizer-simple-wiki | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T09:35:32+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| results
=======
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0868
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmroberta-finetuned-Spanish
This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset.
## M... | {"tags": ["summarization", "xlmroberta", "encoder-decoder", "es", "abstractive summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "xlmroberta-finetuned-Spanish", "results": []}]} | ahmeddbahaa/xlmroberta-finetuned-Spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"xlmroberta",
"es",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T10:04:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #xlmroberta #es #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us
|
# xlmroberta-finetuned-Spanish
This model is a fine-tuned version of [](URL on the wiki_lingua dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamete... | [
"# xlmroberta-finetuned-Spanish\n\nThis model is a fine-tuned version of [](URL on the wiki_lingua dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #xlmroberta #es #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlmroberta-finetuned-Spanish\n\nThis model is a fine-tuned version... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 74.8 | 74.5 |
| test | 74.8 | 74.6 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-rua_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T10:58:20+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 74.8, F1macro: 74.5
Set: test, F1micro: 74.8, F1macro: 74.6
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | git lfs install
git clone https://huggingface.co/dalle-mini/dalle-mini | {} | Abeljones/Ye | null | [
"region:us"
] | null | 2022-06-16T11:07:26+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyroberta-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/tinyroberta-squad2](https://huggingface.co/dee... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "tinyroberta-squad2-finetuned-squad", "results": []}]} | janeel/tinyroberta-squad2-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T11:51:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
| tinyroberta-squad2-finetuned-squad
==================================
This model is a fine-tuned version of deepset/tinyroberta-squad2 on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1592
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-cifar10-32 | null | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-06-16T11:53:22+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #safetensors #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
"TAGS\n#diffusers #safetensors #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-quc
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["quc"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-quc | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"quc",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-16T11:53:33+00:00 | [] | [
"quc"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #quc #dataset-bloom_speech #license-other #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-bloom-speech-quc
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - QUC (K’ich... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #quc #dataset-bloom_speech #license-other #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
null | keras |
## Tensorflow Keras implementation of Learning to tokenize in Vision Transformers
Full credits to [Sayak Paul](https://twitter.com/RisingSayak) and [Aritra Roy Gosthipaty](https://twitter.com/ariG23498) for this work.
## Intended uses & limitations
Vision Transformers ([Dosovitskiy et al.](https://arxiv.org/abs/2... | {"library_name": "keras", "tags": ["tokenization"]} | halice/token-learner | null | [
"keras",
"tensorboard",
"tokenization",
"arxiv:2010.11929",
"arxiv:2103.14030",
"arxiv:2101.11986",
"arxiv:1706.03762",
"has_space",
"region:us"
] | null | 2022-06-16T11:58:56+00:00 | [
"2010.11929",
"2103.14030",
"2101.11986",
"1706.03762"
] | [] | TAGS
#keras #tensorboard #tokenization #arxiv-2010.11929 #arxiv-2103.14030 #arxiv-2101.11986 #arxiv-1706.03762 #has_space #region-us
| Tensorflow Keras implementation of Learning to tokenize in Vision Transformers
------------------------------------------------------------------------------
Full credits to Sayak Paul and Aritra Roy Gosthipaty for this work.
Intended uses & limitations
---------------------------
Vision Transformers (Dosovitskiy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:"
] | [
"TAGS\n#keras #tensorboard #tokenization #arxiv-2010.11929 #arxiv-2103.14030 #arxiv-2101.11986 #arxiv-1706.03762 #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Nonzerophilip/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T12:10:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1286
* Precision: 0.7979
* Recall: 0.8601
* F1: 0.8278
* Accuracy: 0.9614
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
feature-extraction | 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. -->
# roberta-large-sharded
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation s... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "roberta-large-sharded", "results": []}]} | ArthurZ/roberta-large-sharded | null | [
"transformers",
"tf",
"roberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T12:18:24+00:00 | [] | [] | TAGS
#transformers #tf #roberta #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us
|
# roberta-large-sharded
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training ... | [
"# roberta-large-sharded\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informatio... | [
"TAGS\n#transformers #tf #roberta #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# roberta-large-sharded\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information ... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/aishell2_att_ctc_espnet2`
This model was trained by jctian98 using aishell2 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 04803559d6dcde718638cfbd98139a9ddad1da72
pip install -e .
cd egs2/aishell2/asr1
./run.s... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell2"]} | espnet/aishell2_att_ctc_espnet2 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell2",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-16T12:20:34+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell2 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/aishell2\_att\_ctc\_espnet2'
This model was trained by jctian98 using aishell2 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Jun 16 16:51:22 CST 2022'
* python version: '3.8.13 (default, Mar 28 2022, 1... | [
"### 'espnet/aishell2\\_att\\_ctc\\_espnet2'\n\n\nThis model was trained by jctian98 using aishell2 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Jun 16 16:51:22 CST 2022'\n* python version: '3.8.13 (default, Mar 28 2022, 11:38:47) [GC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell2 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/aishell2\\_att\\_ctc\\_espnet2'\n\n\nThis model was trained by jctian98 using aishell2 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvir... |
text-generation | 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. -->
# opt-30b-sharded
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "opt-30b-sharded", "results": []}]} | ArthurZ/opt-30b-sharded | null | [
"transformers",
"tf",
"opt",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T12:28:09+00:00 | [] | [] | TAGS
#transformers #tf #opt #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# opt-30b-sharded
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# opt-30b-sharded\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #tf #opt #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-30b-sharded\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mo... |
text2text-generation | transformers |
# Introduction
UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), apre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is a... | {"language": ["en"], "license": "apache-2.0", "datasets": ["c4"]} | google/ul2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2205.05131",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T12:50:06+00:00 | [
"2205.05131"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #arxiv-2205.05131 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Introduction
UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), apre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is a... | [
"# Introduction\n\nUL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), apre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tunin... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #arxiv-2205.05131 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Introduction\n\nUL2 is a unified framework for pretraining models that are universally effective across... |
text-generation | transformers |
# Peppa Pig DialoGPT Model | {"tags": ["conversational"]} | Bman/DialoGPT-medium-peppapig | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T13:02:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peppa Pig DialoGPT Model | [
"# Peppa Pig DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peppa Pig DialoGPT Model"
] |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-cak
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["cak"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-cak | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"cak",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T13:12:20+00:00 | [] | [
"cak"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #cak #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-cak
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - CAK (Kaqch... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #cak #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Bas... | {"library_name": "stable-baselines3", "tags": ["QbertNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type... | Corianas/PPO-QbertNoFrameskip-v4_2 | null | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-16T13:22:03+00:00 | [] | [] | TAGS
#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a PPO agent playing QbertNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## U... | [
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents incl... | [
"TAGS\n#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a tra... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="aleks0309/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | aleks0309/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-16T13:36:53+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="aleks0309/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | aleks0309/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-16T13:47:53+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# M1_MLM
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "M1_MLM", "results": []}]} | S2312dal/M1_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T13:48:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M1\_MLM
=======
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2887
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Tr... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #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: 1... |
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. -->
# hyunwoongko-kobart-eb-finetuned-papers-meetings
This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "hyunwoongko-kobart-eb-finetuned-papers-meetings", "results": []}]} | eunbeee/hyunwoongko-kobart-eb-finetuned-papers-meetings | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T14:04:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| hyunwoongko-kobart-eb-finetuned-papers-meetings
===============================================
This model is a fine-tuned version of hyunwoongko/kobart on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3136
* Rouge1: 18.3166
* Rouge2: 8.0509
* Rougel: 18.3332
* Rougelsum: 18.3... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | AlexChe/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-16T14:23:16+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbert-finetune-en-cnn
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-larg... | {"tags": ["summarization", "en", "seq2seq", "mbart", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "mbert-finetune-en-cnn", "results": []}]} | eslamxm/mbart-finetune-en-cnn | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"summarization",
"en",
"seq2seq",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T14:48:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #en #seq2seq #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# mbert-finetune-en-cnn
This model is a fine-tuned version of facebook/mbart-large-50 on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5577
- Rouge-1: 37.69
- Rouge-2: 16.47
- Rouge-l: 35.53
- Gen Len: 79.93
- Bertscore: 74.92
## Model description
More information n... | [
"# mbert-finetune-en-cnn\n\nThis model is a fine-tuned version of facebook/mbart-large-50 on the cnn_dailymail dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5577\n- Rouge-1: 37.69\n- Rouge-2: 16.47\n- Rouge-l: 35.53\n- Gen Len: 79.93\n- Bertscore: 74.92",
"## Model description\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #en #seq2seq #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbert-finetune-en-cnn\n\nThis model is a fine-tuned version of facebook/mbart... |
text2text-generation | transformers |
# bart-large-finetuned-filtered-spotify-podcast-summ
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on on the [Spotify Podcast Dataset](https://arxiv.org/abs/2004.04270). Take a look to the [github repository](https://github.com/TheOnesThatWereAbroad/Po... | {"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-large-finetuned-filtered-spotify-podcast-summ", "results": []}]} | gmurro/bart-large-finetuned-filtered-spotify-podcast-summ | null | [
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"arxiv:2004.04270",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-16T15:04:16+00:00 | [
"2004.04270"
] | [] | TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #arxiv-2004.04270 #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| bart-large-finetuned-filtered-spotify-podcast-summ
==================================================
This model is a fine-tuned version of facebook/bart-large-cnn on on the Spotify Podcast Dataset. Take a look to the github repository of this project.
It achieves the following results during training:
* Train Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n--------------------------------------------------------\n\n\n*",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.19.4\n* TensorFlow 2.9.1\n* Datasets 2.3.1\n* Tokenizers 0.12.1\n\n\nAuthors\n-----... | [
"TAGS\n#transformers #tf #bart #text2text-generation #generated_from_keras_callback #arxiv-2004.04270 #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
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. -->
# modelo_lm_financial
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "modelo_lm_financial", "results": []}]} | anablasi/lm_financial_v2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T15:05:40+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# modelo_lm_financial
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# modelo_lm_financial\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# modelo_lm_financial\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1522164949904248832/IdAM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/unknownco123/1655396407192/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/unknownco123 | null | [
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"text-generation-inference",
"region:us"
] | null | 2022-06-16T15:18:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
UnknownCollector 🇺🇦
@unknownco123
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training da... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers | # 🔑 Keyphrase Extraction Model: KBIR-OpenKP
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done ... | {"language": "en", "license": "mit", "tags": ["keyphrase-extraction"], "datasets": ["midas/openkp"], "metrics": ["seqeval"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content ... | ml6team/keyphrase-extraction-kbir-openkp | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"keyphrase-extraction",
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"dataset:midas/openkp",
"arxiv:2112.08547",
"arxiv:1911.02671",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T15:25:02+00:00 | [
"2112.08547",
"1911.02671"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #keyphrase-extraction #en #dataset-midas/openkp #arxiv-2112.08547 #arxiv-1911.02671 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| Keyphrase Extraction Model: KBIR-OpenKP
=======================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely.... | [
"### Limitations\n\n\n* Limited amount of predicted keyphrases.\n* Only works for English documents.",
"### How To Use\n\n\nTraining Dataset\n----------------\n\n\nOpenKP is a large-scale, open-domain keyphrase extraction dataset with 148,124 real-world web documents along with 1-3 most relevant human-annotated k... | [
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"### Limitations\n\n\n* Limited amount of predicted keyphrases.\n* Only works for E... |
image-classification | transformers | # Efficientnetv2 (61 channels)
| {"language": ["Python 3.7+"], "license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet", "imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample... | chlab/efficientnet_61_planet_detection | null | [
"transformers",
"pytorch",
"efficientnet_61_planet_detection",
"vision",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T15:28:06+00:00 | [] | [
"Python 3.7+"
] | TAGS
#transformers #pytorch #efficientnet_61_planet_detection #vision #image-classification #dataset-imagenet #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #region-us
| # Efficientnetv2 (61 channels)
| [
"# Efficientnetv2 (61 channels)"
] | [
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"# Efficientnetv2 (61 channels)"
] |
token-classification | transformers | # 🔑 Keyphrase Extraction Model: KBIR-KPTimes
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done... | {"language": "en", "license": "mit", "tags": ["keyphrase-extraction"], "datasets": ["midas/kptimes"], "metrics": ["seqeval"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content... | ml6team/keyphrase-extraction-kbir-kptimes | null | [
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"token-classification",
"keyphrase-extraction",
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"dataset:midas/kptimes",
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"arxiv:1911.12559",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T15:35:30+00:00 | [
"2112.08547",
"1911.12559"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #keyphrase-extraction #en #dataset-midas/kptimes #arxiv-2112.08547 #arxiv-1911.12559 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| Keyphrase Extraction Model: KBIR-KPTimes
========================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completel... | [
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and will perform very well on news articles from NY Times. It's not recommended to use this model for other domains, but you are free to test it out.\n* Limited amount of predicted keyphrases.\n* Only works for English documents.",
"#... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #keyphrase-extraction #en #dataset-midas/kptimes #arxiv-2112.08547 #arxiv-1911.12559 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific an... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# T5-summarizer-simple-wiki-v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "T5-summarizer-simple-wiki-v2", "results": []}]} | anantoj/T5-summarizer-simple-wiki-v2 | null | [
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"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T15:35:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-summarizer-simple-wiki-v2
============================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0866
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1483290763056320512/oILN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/basilhalperin-ben_golub-tylercowen/1655399323629/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/basilhalperin-ben_golub-tylercowen | null | [
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"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T16:03:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
tylercowen & Basil Halperin & Ben Golub 🇺🇦
@basilhalperin-ben\_golub-tylercowen
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-webis-touche2020-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T16:57:58+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-trec-news-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T16:59:11+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
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"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-trec-covid-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:00:26+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
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"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-signal1m-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:02:22+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
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"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-scifact-base-msmarco-distilbert-tas-b | null | [
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"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:05:17+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
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"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-scidocs-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:07:01+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
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token-classification | transformers | # 🔑 Keyphrase Extraction Model: KBIR-semeval2017
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first ... | {"language": "en", "license": "mit", "tags": ["keyphrase-extraction"], "datasets": ["midas/semeval2017"], "metrics": ["seqeval"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the con... | ml6team/keyphrase-extraction-kbir-semeval2017 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"keyphrase-extraction",
"en",
"dataset:midas/semeval2017",
"arxiv:2112.08547",
"arxiv:1704.02853",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:08:12+00:00 | [
"2112.08547",
"1704.02853"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #keyphrase-extraction #en #dataset-midas/semeval2017 #arxiv-2112.08547 #arxiv-1704.02853 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| Keyphrase Extraction Model: KBIR-semeval2017
============================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it c... | [
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific articles. It's not recommended to use this model for other domains, but you are free to test it out.\n* Limited amount of predicted keyphrases.\n* Only works for English documents.",... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #keyphrase-extraction #en #dataset-midas/semeval2017 #arxiv-2112.08547 #arxiv-1704.02853 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specifi... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-robust04-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:09:55+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417287754434727936/38RR... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/netflixinator/1660606212293/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/netflixinator | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T17:11:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Fourtoffee #FourtoffeeHype #NewDeal4Animation
@netflixinator
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-quora-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:14:22+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-nq-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:15:15+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-nfcorpus-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:17:25+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-hotpotqa-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:19:43+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-fiqa-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:21:18+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-fever-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:22:11+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-dbpedia-entity-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:23:42+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
token-classification | transformers | Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings.
These model weights are the recommended ones among all available deidentifier weights... | {"language": ["en"], "license": "mit", "tags": ["token-classification", "sequence-tagger-model", "pytorch", "transformers", "pubmedbert", "uncased", "radiology", "biomedical"], "datasets": ["radreports"], "widget": [{"text": "PROCEDURE: Chest xray. COMPARISON: last seen on 1/1/2020 and also record dated of March 1st, 2... | StanfordAIMI/stanford-deidentifier-base | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"sequence-tagger-model",
"pubmedbert",
"uncased",
"radiology",
"biomedical",
"en",
"dataset:radreports",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-16T17:24:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #sequence-tagger-model #pubmedbert #uncased #radiology #biomedical #en #dataset-radreports #license-mit #endpoints_compatible #has_space #region-us
| Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings.
These model weights are the recommended ones among all available deidentifier weights... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #sequence-tagger-model #pubmedbert #uncased #radiology #biomedical #en #dataset-radreports #license-mit #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-climate-fever-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:25:09+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-bioasq-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:26:10+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-gpl-arguana-base-msmarco-distilbert-tas-b | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T17:27:56+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Chinese-zh-CN-aishell1
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the [AISHELL-1](https://github.com/kaldi-asr/kaldi/tree/master/egs/aishell) dataset.
When using this model, make sure that your speech input is sampled ... | {"language": "zh", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["aishell1"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 - Chinese (zh-CN), by Yue Qin", "results": [{"task": {"type": "automatic-speech-recognition", "... | qinyue/wav2vec2-large-xlsr-53-chinese-zn-cn-aishell1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"zh",
"dataset:aishell1",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-16T17:39:15+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #zh #dataset-aishell1 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large-XLSR-53-Chinese-zh-CN-aishell1
=============================================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chinese using the AISHELL-1 dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
Usage
-----
The model can be used directly (without a language... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #zh #dataset-aishell1 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# M2_MLM
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achie... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "M2_MLM", "results": []}]} | S2312dal/M2_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T18:10:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| M2\_MLM
=======
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3686
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* e... |
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. -->
# M3_MLM
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased)... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "M3_MLM", "results": []}]} | S2312dal/M3_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T18:22:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| M3\_MLM
=======
This model is a fine-tuned version of SpanBERT/spanbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.8186
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informati... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_si... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521992020977348609/RrM3... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/alanrmacleod-karl_was_right-yaboihakim | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T18:28:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Michael Parenti’s Stache & Alan MacLeod & Hakim
@alanrmacleod-karl\_was\_right-yaboihakim
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the mod... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# M4_MLM
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unk... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "M4_MLM", "results": []}]} | S2312dal/M4_MLM | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T18:32:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M4\_MLM
=======
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 7.3456
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information ne... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
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. -->
# scibert_scivocab_uncased_finetuned_leukaemia
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://hu... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "scibert_scivocab_uncased_finetuned_leukaemia", "results": []}]} | eplatas/scibert_scivocab_uncased_finetuned_leukaemia | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T18:41:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| scibert\_scivocab\_uncased\_finetuned\_leukaemia
================================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4985
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 995132940
- CO2 Emissions (in grams): 2.4722651844547827
## Validation Metrics
- Loss: 3.5972988605499268
- Rouge1: 16.1218
- Rouge2: 2.9195
- RougeL: 13.0085
- RougeLsum: 13.2975
- Gen Len: 19.9962
## Usage
You can use cURL to access this ... | {"language": "unk", "tags": "autotrain", "datasets": ["ouiame/autotrain-data-Robertatogpt2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.4722651844547827} | ouiame/bertGpt2Summ | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain",
"unk",
"dataset:ouiame/autotrain-data-Robertatogpt2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T19:13:43+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #autotrain #unk #dataset-ouiame/autotrain-data-Robertatogpt2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 995132940
- CO2 Emissions (in grams): 2.4722651844547827
## Validation Metrics
- Loss: 3.5972988605499268
- Rouge1: 16.1218
- Rouge2: 2.9195
- RougeL: 13.0085
- RougeLsum: 13.2975
- Gen Len: 19.9962
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 995132940\n- CO2 Emissions (in grams): 2.4722651844547827",
"## Validation Metrics\n\n- Loss: 3.5972988605499268\n- Rouge1: 16.1218\n- Rouge2: 2.9195\n- RougeL: 13.0085\n- RougeLsum: 13.2975\n- Gen Len: 19.9962",
"## Usage\n\nYou can... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #autotrain #unk #dataset-ouiame/autotrain-data-Robertatogpt2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 995132940\n- CO2 Emissions ... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 995132944
- CO2 Emissions (in grams): 611.0958349328379
## Validation Metrics
- Loss: 3.8850467205047607
- Rouge1: 16.6344
- Rouge2: 2.9899
- RougeL: 13.5872
- RougeLsum: 13.9042
- Gen Len: 20.0
## Usage
You can use cURL to access this mode... | {"language": "unk", "tags": "autotrain", "datasets": ["ouiame/autotrain-data-Robertatogpt2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 611.0958349328379} | ouiame/autotrain-Robertatogpt2-995132944 | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain",
"unk",
"dataset:ouiame/autotrain-data-Robertatogpt2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T19:14:06+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #autotrain #unk #dataset-ouiame/autotrain-data-Robertatogpt2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 995132944
- CO2 Emissions (in grams): 611.0958349328379
## Validation Metrics
- Loss: 3.8850467205047607
- Rouge1: 16.6344
- Rouge2: 2.9899
- RougeL: 13.5872
- RougeLsum: 13.9042
- Gen Len: 20.0
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 995132944\n- CO2 Emissions (in grams): 611.0958349328379",
"## Validation Metrics\n\n- Loss: 3.8850467205047607\n- Rouge1: 16.6344\n- Rouge2: 2.9899\n- RougeL: 13.5872\n- RougeLsum: 13.9042\n- Gen Len: 20.0",
"## Usage\n\nYou can use... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #autotrain #unk #dataset-ouiame/autotrain-data-Robertatogpt2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 995132944\n- CO2 Emissions ... |
image-classification | transformers | # Efficientnetv2 (47 channels) | {"language": ["Python 3.7+"], "license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet", "imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample... | chlab/efficientnet_47_planet_detection | null | [
"transformers",
"pytorch",
"vision",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T19:15:11+00:00 | [] | [
"Python 3.7+"
] | TAGS
#transformers #pytorch #vision #image-classification #dataset-imagenet #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #region-us
| # Efficientnetv2 (47 channels) | [
"# Efficientnetv2 (47 channels)"
] | [
"TAGS\n#transformers #pytorch #vision #image-classification #dataset-imagenet #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Efficientnetv2 (47 channels)"
] |
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. -->
# ztranslate
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-sw](https://huggingface.co/Helsinki-NLP/opus-mt-en-sw)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ztranslate", "results": []}]} | usaf/ztranslate | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T19:17:16+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ztranslate
==========
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-sw on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\... |
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. -->
# inquisitive-full
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unk... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "inquisitive-full", "results": []}]} | kcarnold/inquisitive-full | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T19:18:43+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# inquisitive-full
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5594
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More in... | [
"# inquisitive-full\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.5594",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ev... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# inquisitive-full\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown dataset.\nIt achieves the following results on the evaluat... |
image-classification | transformers | # Efficientnetv2 (75 channels) | {"language": ["Python 3.7+"], "license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet", "imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample... | chlab/efficientnet_75_planet_detection | null | [
"transformers",
"pytorch",
"vision",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T19:20:32+00:00 | [] | [
"Python 3.7+"
] | TAGS
#transformers #pytorch #vision #image-classification #dataset-imagenet #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #region-us
| # Efficientnetv2 (75 channels) | [
"# Efficientnetv2 (75 channels)"
] | [
"TAGS\n#transformers #pytorch #vision #image-classification #dataset-imagenet #dataset-imagenet-21k #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Efficientnetv2 (75 channels)"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1247482752351588352/EgHo... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chrishemsworth-deadpoolmovie/1655421962384/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chrishemsworth-deadpoolmovie | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T19:28:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Chris Hemsworth & Deadpool Movie
@chrishemsworth-deadpoolmovie
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1353806309397655553/0zEt... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chrisevans-robertdowneyjr/1655411636421/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chrisevans-robertdowneyjr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T19:32:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Robert Downey Jr & Chris Evans
@chrisevans-robertdowneyjr
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | SimingSiming/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-16T20:18:41+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1601201593/Screen_shot_2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/leisha_hailey/1655417283179/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/leisha_hailey | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T21:04:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Leisha Hailey
@leisha\_hailey
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # The world machine DialoGPT model | {"tags": ["conversational"]} | ZipperXYZ/DialoGPT-medium-TheWorldMachine | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T21:07:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # The world machine DialoGPT model | [
"# The world machine DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# The world machine DialoGPT model"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/817874051146412032/rPvqT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/jbsalvagno | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T21:41:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Javier Bustos
@jbsalvagno
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# AgedBlaine DialoGPT Model 2 | {"tags": ["conversational"]} | AlyxTheKitten/DialoGPT-medium-AgedBlaine-2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T22:04:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# AgedBlaine DialoGPT Model 2 | [
"# AgedBlaine DialoGPT Model 2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# AgedBlaine DialoGPT Model 2"
] |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | mindwrapped/aiw-generator-awd-lstm | null | [
"fastai",
"region:us"
] | null | 2022-06-16T22:09:24+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1133109643734130688/Bwio... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rihanna/1655745706641/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rihanna | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T22:10:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rihanna
@rihanna
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | mindwrapped/bible-generator-awd-lstm | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-06-16T22:34:56+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #has_space #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (d... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MBart-finetuned-ur-xlsum
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-l... | {"tags": ["summarization", "ur", "seq2seq", "mbart", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "MBart-finetuned-ur-xlsum", "results": []}]} | eslamxm/MBart-finetuned-ur-xlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"summarization",
"ur",
"seq2seq",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T22:41:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #ur #seq2seq #Abstractive Summarization #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us
|
# MBart-finetuned-ur-xlsum
This model is a fine-tuned version of facebook/mbart-large-50 on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2663
- Rouge-1: 40.6
- Rouge-2: 18.9
- Rouge-l: 34.39
- Gen Len: 37.88
- Bertscore: 77.06
## Model description
More information needed
... | [
"# MBart-finetuned-ur-xlsum\n\nThis model is a fine-tuned version of facebook/mbart-large-50 on the xlsum dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.2663\n- Rouge-1: 40.6\n- Rouge-2: 18.9\n- Rouge-l: 34.39\n- Gen Len: 37.88\n- Bertscore: 77.06",
"## Model description\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #ur #seq2seq #Abstractive Summarization #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# MBart-finetuned-ur-xlsum\n\nThis model is a fine-tuned version of facebook/mbart-larg... |
text-generation | transformers |
# Miles Prower DialoGPT Model | {"tags": ["conversational"]} | Averium/DialoGPT-medium-TailsBot1.1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T23:00:50+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Miles Prower DialoGPT Model | [
"# Miles Prower DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Miles Prower DialoGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-xnli-finetuned-mnli-SJP
This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["swiss_judgment_prediction"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-large-xnli-finetuned-mnli-SJP", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "swiss_judgment_predic... | tuni/xlm-roberta-large-xnli-finetuned-mnli-SJP | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:swiss_judgment_prediction",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T23:28:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-large-xnli-finetuned-mnli-SJP
=========================================
This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the swiss\_judgment\_prediction dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3456
* Accuracy: 0.7957
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1520110813209665538/-4Gu... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/fawfulthgreat64/1655425906757/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/fawfulthgreat64 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T23:31:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jamey Viv ️️ 🇺🇦 #Toaster4DisneyPlus
@fawfulthgreat64
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B r... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-finetuned-fa
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-5... | {"tags": ["summarization", "fa", "mbart", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["pn_summary"], "model-index": [{"name": "mbart-finetuned-fa", "results": []}]} | eslamxm/mbart-finetuned-fa | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"summarization",
"fa",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:pn_summary",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T23:40:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #region-us
|
# mbart-finetuned-fa
This model is a fine-tuned version of facebook/mbart-large-50 on the pn_summary dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2877
- Rouge-1: 44.07
- Rouge-2: 25.81
- Rouge-l: 38.96
- Gen Len: 41.7
- Bertscore: 78.95
## Model description
More information needed
... | [
"# mbart-finetuned-fa\n\nThis model is a fine-tuned version of facebook/mbart-large-50 on the pn_summary dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.2877\n- Rouge-1: 44.07\n- Rouge-2: 25.81\n- Rouge-l: 38.96\n- Gen Len: 41.7\n- Bertscore: 78.95",
"## Model description\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart-finetuned-fa\n\nThis model is a fine-tuned version of facebook/mbart-large-50 on th... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/603269306026106880/42CwE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/tomcruise | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T23:59:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tom Cruise
@tomcruise
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1193951507026075648/Ot3G... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/tomhanks | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T00:00:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tom Hanks
@tomhanks
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | null | # The world machine DialoGPT model | {"tags": ["conversational"]} | ZipperXYZ/DialoGPT-medium-TheWorldMachine2 | null | [
"conversational",
"region:us"
] | null | 2022-06-17T00:20:18+00:00 | [] | [] | TAGS
#conversational #region-us
| # The world machine DialoGPT model | [
"# The world machine DialoGPT model"
] | [
"TAGS\n#conversational #region-us \n",
"# The world machine DialoGPT model"
] |
text-generation | transformers |
# stolen personalities | {"tags": ["conversational"]} | damianruel/DialoGPT-medium-MySon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T00:30:02+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# stolen personalities | [
"# stolen personalities"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# stolen personalities"
] |
automatic-speech-recognition | transformers | R4 checkpoint-30000 LM parlamento europeo
| {} | gciaffoni/modelLM | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T00:30:13+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| R4 checkpoint-30000 LM parlamento europeo
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/603269306026106880/42CwE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/mcdonaldsuk-potus-tomcruise | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T00:44:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Tom Cruise & McDonald's UK & President Biden
@mcdonaldsuk-potus-tomcruise
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | CGRE is a generation-based relation extraction model
·a SOTA chinese end-to-end relation extraction model,using bart as backbone.
·using the Distant-supervised data from cndbpedia,pretrained from the checkpoint of fnlp/bart-base-chinese.
·can perform SOTA in many chinese relation extraction dataset,such as... | {} | fanxiao/CGRE_CNDBPedia-Generative-Relation-Extraction | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T01:12:15+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| CGRE is a generation-based relation extraction model
·a SOTA chinese end-to-end relation extraction model,using bart as backbone.
·using the Distant-supervised data from cndbpedia,pretrained from the checkpoint of fnlp/bart-base-chinese.
·can perform SOTA in many chinese relation extraction dataset,such as... | [
"# if cannot see tokens in model card please open readme file\n\ntokenizer = BertTokenizer.from_pretrained(model_name, tokenizer_kwargs)\n\nmodel = BartForConditionalGeneration.from_pretrained('./CGRE_CNDBPedia-Generative-Relation-Extraction')\n\ninputs = tokenizer(sent, max_length=max_source_length, padding=\"max_... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# if cannot see tokens in model card please open readme file\n\ntokenizer = BertTokenizer.from_pretrained(model_name, tokenizer_kwargs)\n\nmodel = BartForConditionalGeneration.from_pretrained('./... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-squadshifts-amazon-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.c... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-large-squadshifts-amazon-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T01:29:17+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-squadshifts-amazon-qg'
===================================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Languag... |
reinforcement-learning | null |
# 使用**Q-Learning**智能体来玩**FrozenLake-v1**
这是一个使用**Q-Learning**训练有素的模型玩**FrozenLake-v1**.
## 用法
```python
model = load_from_hub(repo_id='sun1638650145/q-FrozenLake-v1-4x4-noSlippery', filename='q-learning.pkl')
# 不要忘记检查是否需要添加额外的参数(例如is_slippery=False)
env = gym.make(mode... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | sun1638650145/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-17T02:02:00+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# 使用Q-Learning智能体来玩FrozenLake-v1
这是一个使用Q-Learning训练有素的模型玩FrozenLake-v1.
## 用法
| [
"# 使用Q-Learning智能体来玩FrozenLake-v1\n 这是一个使用Q-Learning训练有素的模型玩FrozenLake-v1.\n \n ## 用法"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# 使用Q-Learning智能体来玩FrozenLake-v1\n 这是一个使用Q-Learning训练有素的模型玩FrozenLake-v1.\n \n ## 用法"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Corianas/dqn-SpaceInvadersNoFrameskip-v4_2 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-17T02:25:04+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# wikitext_roberta-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the wikitext ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikitext"], "metrics": ["accuracy"], "model-index": [{"name": "wikitext_roberta-base", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "wikitext wikitext-2-raw-v1", "type": "wikitext", "args": "wikit... | gary109/wikitext_roberta-base | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:wikitext",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T02:50:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-wikitext #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| wikitext\_roberta-base
======================
This model is a fine-tuned version of roberta-base on the wikitext wikitext-2-raw-v1 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2143
* Accuracy: 0.7371
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-wikitext #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner_0
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner_0", "results": []}]} | mariolinml/bert-finetuned-ner_0 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T02:59:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner\_0
=====================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2298
* Precision: 0.5119
* Recall: 0.4222
* F1: 0.4627
* Accuracy: 0.9246
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* ... |
null | null | ### What's Hugging Face?!!!
https://towardsdatascience.com/whats-hugging-face-122f4e7eb11a
Hugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies!!!!!. | {} | miyoung/newProject | null | [
"region:us"
] | null | 2022-06-17T03:39:53+00:00 | [] | [] | TAGS
#region-us
| ### What's Hugging Face?!!!
URL
Hugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies!!!!!. | [
"### What's Hugging Face?!!! \n\nURL \n\nHugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies!!!!!."
] | [
"TAGS\n#region-us \n",
"### What's Hugging Face?!!! \n\nURL \n\nHugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies!!!!!."
] |
fill-mask | transformers |
# Model Card for patentdeberta_base_spec_1024_pwi
# Model Details
## Model Description
More information needed
- **Developed by:** More information needed
- **Shared by [Optional]:** tanapatentlm
- **Model type:** Fill Mask
- **Language(s) (NLP):** More information needed
- **License:** More information neede... | {"tags": ["fill-mask", "deberta"]} | tanapatentlm/patentdeberta_base_spec_1024_pwi | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T03:44:15+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for patentdeberta_base_spec_1024_pwi
# Model Details
## Model Description
More information needed
- Developed by: More information needed
- Shared by [Optional]: tanapatentlm
- Model type: Fill Mask
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Model: De... | [
"# Model Card for patentdeberta_base_spec_1024_pwi",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: More information needed\n- Shared by [Optional]: tanapatentlm\n- Model type: Fill Mask\n- Language(s) (NLP): More information needed\n- License: More information needed\n... | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for patentdeberta_base_spec_1024_pwi",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: More information needed\n- Shared by [Op... |
text2text-generation | transformers |
## Usage:
```python
abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
ha... | {"license": "mit", "datasets": ["arxiv"], "widget": [{"text": "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in... | Suva/uptag-url-model-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:arxiv",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-17T03:46:15+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-arxiv #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Usage:
### Using Transformers
| [
"## Usage:",
"### Using Transformers"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-arxiv #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Usage:",
"### Using Transformers"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | Shikenrua/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T04:16:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | aditya22/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:01:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0642
* Precision: 0.9360
* Recall: 0.9504
* F1: 0.9431
* Accuracy: 0.9860
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
feature-extraction | transformers | # MedCPT
###### LingYi system pre training medical model
###### Prease load the model from [**CPT**](https://huggingface.co/fnlp/cpt-large)
## Usage
```python
>>> from modeling_cpt import CPTForConditionalGeneration
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("WENGSYX/Me... | {} | WENGSYX/MedCPT | null | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-06-17T06:21:31+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #feature-extraction #endpoints_compatible #region-us
| # MedCPT
###### LingYi system pre training medical model
###### Prease load the model from CPT
## Usage
| [
"# MedCPT",
"###### LingYi system pre training medical model",
"###### Prease load the model from CPT",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #bart #feature-extraction #endpoints_compatible #region-us \n",
"# MedCPT",
"###### LingYi system pre training medical model",
"###### Prease load the model from CPT",
"## Usage"
] |
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