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text-generation | transformers | # GPT-Rapgenerator
The Rapgenerator is trained for [nullsechsroy](https://genius.com/artists/Nullsechsroy) on an english [GPT2](https://huggingface.co/transformers/model_doc/gpt2.html) that is converted to a german [GerPT2](https://github.com/bminixhofer/gerpt2).
# Usage
```
from transformers import pipeline, AutoToke... | {"language": "de", "license": "mit", "tags": ["Text Generation"], "datasets": ["genius lyrics"], "widget": [{"text": "[Title_nullsechsroy feat. YFG Pave_"}]} | Bachstelze/Rapgenerator | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"Text Generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T20:37:36+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #Text Generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT-Rapgenerator
The Rapgenerator is trained for nullsechsroy on an english GPT2 that is converted to a german GerPT2.
# Usage
We used the genius songlyrics from the following artists:
['Ace Tee', 'Aligatoah', 'AnnenMayKantereit', 'Apache 207', 'Azad', 'Badmómzjay', 'Bausa', 'Blumentopf', 'Blumio', 'Capital Bra',... | [
"# GPT-Rapgenerator\nThe Rapgenerator is trained for nullsechsroy on an english GPT2 that is converted to a german GerPT2.",
"# Usage\n\n\nWe used the genius songlyrics from the following artists:\n\n['Ace Tee', 'Aligatoah', 'AnnenMayKantereit', 'Apache 207', 'Azad', 'Badmómzjay', 'Bausa', 'Blumentopf', 'Blumio',... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #Text Generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-Rapgenerator\nThe Rapgenerator is trained for nullsechsroy on an english GPT2 that is converted to a german GerPT2.",
"# Usage\n\n\... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | RaphaelReinauer/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-15T20:37:38+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
null | fastai |
# Model card
## Model description
`convnext_tiny_in22k` fine tuned on 80% of the dataset (20% used for validation)
## Intended uses & limitations
For reference only. Should **not** be used for medical diagnosis.
## Training and evaluation data
Dataset: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-b... | {"tags": ["fastai"]} | hugginglearners/brain-tumor-detection-mri | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-07-15T20:51:21+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Model card
## Model description
'convnext_tiny_in22k' fine tuned on 80% of the dataset (20% used for validation)
## Intended uses & limitations
For reference only. Should not be used for medical diagnosis.
## Training and evaluation data
Dataset: URL
Link to W&B report: URL
| [
"# Model card",
"## Model description\n'convnext_tiny_in22k' fine tuned on 80% of the dataset (20% used for validation)",
"## Intended uses & limitations\nFor reference only. Should not be used for medical diagnosis.",
"## Training and evaluation data\nDataset: URL\nLink to W&B report: URL"
] | [
"TAGS\n#fastai #has_space #region-us \n",
"# Model card",
"## Model description\n'convnext_tiny_in22k' fine tuned on 80% of the dataset (20% used for validation)",
"## Intended uses & limitations\nFor reference only. Should not be used for medical diagnosis.",
"## Training and evaluation data\nDataset: URL\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-modelo-becas0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv3"], "model-index": [{"name": "distilbert-base-uncased-modelo-becas0", "results": []}]} | Evelyn18/distilbert-base-uncased-modelo-becas0 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T21:03:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv3 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-modelo-becas0
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv3 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1182
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv3 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
null | null | 256x256 Diffusion model trained on 1000+ NSFW gay furry pics (with same composition)
'attention_resolutions': '16',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'timestep_respacing': 'ddim100',
'image_size': 256,
'learn_... | {"license": "mit"} | CuteBlack/gfp_guided_diffusion_200k | null | [
"license:mit",
"region:us"
] | null | 2022-07-15T21:24:34+00:00 | [] | [] | TAGS
#license-mit #region-us
| 256x256 Diffusion model trained on 1000+ NSFW gay furry pics (with same composition)
'attention_resolutions': '16',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'timestep_respacing': 'ddim100',
'image_size': 256,
'learn_... | [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-generation | transformers | #####
## Bloom2.5B Zen ##
#####
Bloom (2.5 B) Scientific Model fine-tuned on Zen knowledge
#####
## Usage ##
#####
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
model = AutoModelForCausalLM.from_pretra... | {} | MultiTrickFox/bloom-2b5_Zen | null | [
"transformers",
"pytorch",
"bloom",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T23:37:54+00:00 | [] | [] | TAGS
#transformers #pytorch #bloom #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #####
## Bloom2.5B Zen ##
#####
Bloom (2.5 B) Scientific Model fine-tuned on Zen knowledge
#####
## Usage ##
#####
| [
"## Bloom2.5B Zen ##\n #####\n\n\nBloom (2.5 B) Scientific Model fine-tuned on Zen knowledge\n\n\n #####",
"## Usage ##\n #####"
] | [
"TAGS\n#transformers #pytorch #bloom #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Bloom2.5B Zen ##\n #####\n\n\nBloom (2.5 B) Scientific Model fine-tuned on Zen knowledge\n\n\n #####",
"## Usage ##\n #####"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from hugg... | {"library_name": "stable-baselines3", "tags": ["Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "ty... | trtd56/ppo-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-15T23:38:25+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Walker2DBulletEnv-v0
This is a trained model of a PPO agent playing Walker2DBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baseline... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v2
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_w... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v2", "results": []}]} | gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T00:44:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53-v2
=======================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53-v2 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* ... |
null | null | 123 | {} | m9810223/md | null | [
"region:us"
] | null | 2022-07-16T01:15:24+00:00 | [] | [] | TAGS
#region-us
| 123 | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Someman/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T03:51:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1426
* F1: 0.8640
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# deberta-finetuned-ner
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-ba... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "deberta-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll... | baptiste/deberta-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T03:51:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #deberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-finetuned-ner
=====================
This model is a fine-tuned version of microsoft/deberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0515
* Precision: 0.9577
* Recall: 0.9652
* F1: 0.9614
* Accuracy: 0.9907
Model description
-----------------
More... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #deberta #token-classification #generated_from_trainer #dataset-conll2003 #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*... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It ac... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]} | SiraH/dummy-model | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T05:20:36+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-model
This model is a fine-tuned version of camembert-base 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
## Tr... | [
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base 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 inf... | [
"TAGS\n#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Crawler**
This is a trained model of a **ppo** agent playing **Crawler** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Crawler"]} | infinitejoy/MLAgents-Crawler | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Crawler",
"region:us"
] | null | 2022-07-16T05:40:45+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Crawler #region-us
|
# ppo Agent playing Crawler
This is a trained model of a ppo agent playing Crawler using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
... | [
"# ppo Agent playing Crawler\n This is a trained model of a ppo agent playing Crawler using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the tra... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Crawler #region-us \n",
"# ppo Agent playing Crawler\n This is a trained model of a ppo agent playing Crawler using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentat... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | Someman/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T05:56:10+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1717
* F1: 0.8601
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
null | null |
This repo contains weights for some models from https://github.com/open-mmlab/mmocr | {"license": "apache-2.0"} | xianbao/mmocr | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-07-16T06:11:22+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
This repo contains weights for some models from URL | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Hadjer/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T06:31:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1564
- eval_runtime: 147.0781
- eval_samples_per_second: 73.322
- eval_steps_per_second: 4.583
- epoch: 1.0
- step: 55... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1564\n- eval_runtime: 147.0781\n- eval_samples_per_second: 73.322\n- eval_steps_per_second: 4.583\n- epoch: 1.0\... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves... |
text-generation | transformers |
# Dirk Strider DialoGPT Model | {"tags": ["conversational"]} | BoxCrab/DialoGPT-small-Strider | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-16T06:33:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dirk Strider DialoGPT Model | [
"# Dirk Strider DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dirk Strider DialoGPT Model"
] |
null | null | test | {} | pkt/pkt | null | [
"region:us"
] | null | 2022-07-16T07:12:52+00:00 | [] | [] | TAGS
#region-us
| test | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Vulnerability-detection
This model is a fine-tuned version of [mrm8488/codebert-base-finetuned-detect-insecure-code](https://hug... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Vulnerability-detection", "results": []}]} | Zaib/Vulnerability-detection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-16T08:16:45+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vulnerability-detection
This model is a fine-tuned version of mrm8488/codebert-base-finetuned-detect-insecure-code on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5778
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# Vulnerability-detection\n\nThis model is a fine-tuned version of mrm8488/codebert-base-finetuned-detect-insecure-code on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5778",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore i... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vulnerability-detection\n\nThis model is a fine-tuned version of mrm8488/codebert-base-finetuned-detect-insecure-code on an unknown dataset.\nIt achieves th... |
text2text-generation | transformers | chinese-simile-generative 是一个将句子A改写成带有修辞手法(主要为比喻，明喻)的句子B的seq2seq模型。
A： 想当初对你的定级是很高的，现在我很伤心，看到你的科研进度这么慢。
B： 想当初对你的定级是很高的，现在我很伤心，看到你的科研进度像蜗牛一样慢。
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/chinese-simile-generation")
model ... | {} | figurative-nlp/Chinese-Simile-Generation | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T08:48:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| chinese-simile-generative 是一个将句子A改写成带有修辞手法(主要为比喻，明喻)的句子B的seq2seq模型。
A： 想当初对你的定级是很高的，现在我很伤心，看到你的科研进度这么慢。
B： 想当初对你的定级是很高的，现在我很伤心，看到你的科研进度像蜗牛一样慢。
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/chinese-simile-generation")
model ... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #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. -->
# pegasus-paraphrase-finetuned-parasci
This model is a fine-tuned version of [domenicrosati/pegasus-paraphrase-finetuned-parasci](... | {"tags": ["paraphrasing", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-paraphrase-finetuned-parasci", "results": []}]} | domenicrosati/pegasus-paraphrase-finetuned-parasci | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"paraphrasing",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T09:34:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #paraphrasing #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| pegasus-paraphrase-finetuned-parasci
====================================
This model is a fine-tuned version of domenicrosati/pegasus-paraphrase-finetuned-parasci on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 0.0
* Rouge2: 0.0
* Rougel: 0.0
* Rougelsum: 0.0
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #paraphrasing #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: 5.6e-05\n* train\\_batch\\... |
question-answering | transformers |
# deberta-base-thai-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from [deberta-base-thai](https://huggingface.co/KoichiYasuoka/deberta-base-thai). Use [MASK] inside `context... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u0e01\u0e27\u0e48\u0e32", "context": "\u0e2b\u0e25\u0e32... | KoichiYasuoka/deberta-base-thai-ud-head | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"thai",
"dependency-parsing",
"th",
"dataset:universal_dependencies",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T09:35:07+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #thai #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #endpoints_compatible #region-us
|
# deberta-base-thai-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from deberta-base-thai. Use [MASK] inside 'context' to avoid ambiguity when specifying a multiple-used word ... | [
"# deberta-base-thai-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from deberta-base-thai. Use [MASK] inside 'context' to avoid ambiguity when specifying a multiple-... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #thai #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #endpoints_compatible #region-us \n",
"# deberta-base-thai-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for depende... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **GridFoodCollector**
This is a trained model of a **ppo** agent playing **GridFoodCollector** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-GridFoodCollector"]} | infinitejoy/MLAgents-GridFoodCollector | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-GridFoodCollector",
"region:us"
] | null | 2022-07-16T09:36:42+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-GridFoodCollector #region-us
|
# ppo Agent playing GridFoodCollector
This is a trained model of a ppo agent playing GridFoodCollector using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### ... | [
"# ppo Agent playing GridFoodCollector\n This is a trained model of a ppo agent playing GridFoodCollector using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-GridFoodCollector #region-us \n",
"# ppo Agent playing GridFoodCollector\n This is a trained model of a ppo agent playing GridFoodCollector using the Unity ML-Agents Library.\n \n ## Usage (wit... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | ipvikas/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T09:59:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4119
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
null | fastai |
# മലയാളം - English ULMFit translationmodel. (Working in Progress)
[](https://www.kaggle.com/code/rajeshradhakrishnan/ml-ulmfit-seq2seq-translation)
---
# malayalam-ULMFit-Seq2Seq (Traslation model)
malayalam-ULMFit-Seq2Seq model... | {"language": "ml", "tags": ["fastai", "text-translation"], "widget": [{"text": "\u0d15\u0d47\u0d7e\u0d15\u0d4d\u0d15\u0d41\u0d28\u0d4d\u0d28 \u0d0e\u0d32\u0d4d\u0d32\u0d3e \u0d15\u0d3e\u0d30\u0d4d\u0d2f\u0d19\u0d4d\u0d19\u0d33\u0d41\u0d02 \u0d0e\u0d28\u0d3f\u0d15\u0d4d\u0d15\u0d41 \u0d2e\u0d28\u0d38\u0d3f\u0d32\u0d3e\u... | hugginglearners/malayalam-ULMFit-Seq2Seq | null | [
"fastai",
"text-translation",
"ml",
"region:us"
] | null | 2022-07-16T10:01:26+00:00 | [] | [
"ml"
] | TAGS
#fastai #text-translation #ml #region-us
|
# മലയാളം - English ULMFit translationmodel. (Working in Progress)
malayalam-ULMFit-Seq2Seq model is pre-trained on Malyalam_Language_Model_ULMFiT using fastai Language Model using fastai
Tokenized using Sentencepiece with a vocab... | [
"# മലയാളം - English ULMFit translationmodel. (Working in Progress)\n\n\n![മലയാളം: kaggle notebook](URL\n\n\n---",
"# malayalam-ULMFit-Seq2Seq (Traslation model)\n\nmalayalam-ULMFit-Seq2Seq model is pre-trained on Malyalam_Language_Model_ULMFiT using fastai Language Model using fastai \n\nTokenized using Sentence... | [
"TAGS\n#fastai #text-translation #ml #region-us \n",
"# മലയാളം - English ULMFit translationmodel. (Working in Progress)\n\n\n![മലയാളം: kaggle notebook](URL\n\n\n---",
"# malayalam-ULMFit-Seq2Seq (Traslation model)\n\nmalayalam-ULMFit-Seq2Seq model is pre-trained on Malyalam_Language_Model_ULMFiT using fastai L... |
null | null | Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
| {"license": "apache-2.0", "title": "TimeMachine-Visual_Question_Answering", "emoji": "\ud83c\udf93", "colorFrom": "blue", "colorTo": "pink", "sdk": "gradio", "app_file": "app.py", "pinned": false} | yuanzf/timemachine | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-07-16T10:11:44+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Check out the configuration reference at URL
| [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | roykoand/ppo-LunarLander-v2.1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-16T10:25:51+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
translation | transformers |
# opus-mt-finetuned-hi-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on [HindiEnglish Corpora](https://www.clarin.eu/resource-families/parallel-corpora)
## Model description
The model is a transformer model similar to the [Transformer](http... | {"language": ["en", "hi", "multilingual"], "license": "apache-2.0", "tags": ["translation", "Hindi", "generated_from_keras_callback"], "datasets": ["HindiEnglishCorpora"]} | AbhirupGhosh/opus-mt-finetuned-en-hi | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"Hindi",
"generated_from_keras_callback",
"en",
"hi",
"multilingual",
"dataset:HindiEnglishCorpora",
"arxiv:1706.03762",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatibl... | null | 2022-07-16T10:27:09+00:00 | [
"1706.03762"
] | [
"en",
"hi",
"multilingual"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #Hindi #generated_from_keras_callback #en #hi #multilingual #dataset-HindiEnglishCorpora #arxiv-1706.03762 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-finetuned-hi-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-hi-en on HindiEnglish Corpora
## Model description
The model is a transformer model similar to the Transformer as defined in Attention Is All You Need by Vaswani et al
## Training and evaluation data
More information needed
## ... | [
"# opus-mt-finetuned-hi-en\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-hi-en on HindiEnglish Corpora",
"## Model description\n\nThe model is a transformer model similar to the Transformer as defined in Attention Is All You Need by Vaswani et al",
"## Training and evaluation data\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #Hindi #generated_from_keras_callback #en #hi #multilingual #dataset-HindiEnglishCorpora #arxiv-1706.03762 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-finetuned-hi-en\n\nThis mo... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-large-samsum
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) o... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-large-samsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "samsum", "type": "samsum", "args": "samsum"}, "metrics": [{"ty... | RobertoFont/pegasus-large-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T10:45:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-large-samsum
====================
This model is a fine-tuned version of google/pegasus-large on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4109
* Rouge1: 48.0968
* Rouge2: 24.6663
* Rougel: 40.2569
* Rougelsum: 44.0137
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
text2text-generation | transformers | This is mt5-base model [google/mt5-base](https://huggingface.co/google/mt5-base) in which only Russian and English tokens are left
The model has been fine-tuned for several tasks:
* translation (opus100 dataset)
* dialog (daily dialog dataset)
How to use:
```
# !pip install transformers sentencepiece
from transform... | {"language": ["ru", "en"], "license": "mit", "tags": ["russian"], "widget": [{"text": "translate en-ru: I'm afraid that I won't finish the report on time."}]} | artemnech/enrut5-base | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"russian",
"ru",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-16T11:02:28+00:00 | [] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #russian #ru #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is mt5-base model google/mt5-base in which only Russian and English tokens are left
The model has been fine-tuned for several tasks:
* translation (opus100 dataset)
* dialog (daily dialog dataset)
How to use:
| [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #russian #ru #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-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. -->
# t5-small-finetuned-tf-xsum
This model was trained from scratch on xsum dataset.
It achieves the following results on the evaluation se... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-small-finetuned-tf-xsum", "results": []}]} | mohammedbriman/t5-small-finetuned-tf-xsum | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-16T13:46:07+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-tf-xsum
==========================
This model was trained from scratch on xsum dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.3494
* Validation Loss: 2.1933
* Train Rouge1: 32.0241
* Train Rouge2: 10.1025
* Train Rougel: 25.8834
* Train Rougelsum: 25.9662
* Trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDe... |
image-segmentation | 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. -->
# segformer-finetuned-Maize-10k-steps-sem
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-... | {"license": "apache-2.0", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "model-index": [{"name": "segformer-finetuned-Maize-10k-steps-sem", "results": []}]} | koushikn/segformer-finetuned-Maize-10k-steps-sem | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"image-segmentation",
"vision",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T13:59:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-finetuned-Maize-10k-steps-sem
=======================================
This model is a fine-tuned version of nvidia/mit-b5 on the koushikn/Maize\_sem\_seg dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0756
* Mean Iou: 0.9172
* Mean Accuracy: 0.9711
* Overall Accuracy: 0.9804
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* training\\_steps: 10000",... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\... |
question-answering | transformers |
# roberta-base-thai-spm-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from [roberta-base-thai-spm](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm). Use [MASK] ins... | {"language": ["th"], "license": "apache-2.0", "tags": ["thai", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u0e01\u0e27\u0e48\u0e32", "context": "\u0e2b\u0e25\u0e32... | KoichiYasuoka/roberta-base-thai-spm-ud-head | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"thai",
"dependency-parsing",
"th",
"dataset:universal_dependencies",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T14:00:05+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #roberta #question-answering #thai #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #endpoints_compatible #region-us
|
# roberta-base-thai-spm-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from roberta-base-thai-spm. Use [MASK] inside 'context' to avoid ambiguity when specifying a multiple-us... | [
"# roberta-base-thai-spm-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from roberta-base-thai-spm. Use [MASK] inside 'context' to avoid ambiguity when specifying a m... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #thai #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #endpoints_compatible #region-us \n",
"# roberta-base-thai-spm-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for depend... |
automatic-speech-recognition | transformers | Try this. | {} | jens-simon/xls-r-300m-sv-2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T14:18:02+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Try this. | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
translation | transformers |
# opus-mt-finetuned-hi-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on [HindiEnglish Corpora](https://www.clarin.eu/resource-families/parallel-corpora)
## Model description
The model is a transformer model similar to the [Transformer](http... | {"language": ["hi", "en", "multilingual"], "license": "apache-2.0", "tags": ["translation", "Hindi", "generated_from_keras_callback"], "model-index": [{"name": "opus-mt-finetuned-hi-en", "results": []}]} | AbhirupGhosh/opus-mt-finetuned-hi-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"Hindi",
"generated_from_keras_callback",
"hi",
"en",
"multilingual",
"arxiv:1706.03762",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T14:34:05+00:00 | [
"1706.03762"
] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #Hindi #generated_from_keras_callback #hi #en #multilingual #arxiv-1706.03762 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-finetuned-hi-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-hi-en on HindiEnglish Corpora
## Model description
The model is a transformer model similar to the Transformer as defined in Attention Is All You Need et al
## Training and evaluation data
More information needed
## Training pr... | [
"# opus-mt-finetuned-hi-en\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-hi-en on HindiEnglish Corpora",
"## Model description\n\nThe model is a transformer model similar to the Transformer as defined in Attention Is All You Need et al",
"## Training and evaluation data\n\nMore information neede... | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #Hindi #generated_from_keras_callback #hi #en #multilingual #arxiv-1706.03762 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-finetuned-hi-en\n\nThis model is a fine-tuned version o... |
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... | 6001k1d/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-16T14:51:34+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 |
<img src="https://huggingface.co/dlicari/Italian-Legal-BERT/resolve/main/ITALIAN_LEGAL_BERT.jpg" width="600"/>
<h1> ITALIAN-LEGAL-BERT:A pre-trained Transformer Language Model for Italian Law </h1>
ITALIAN-LEGAL-BERT is based on <a href="https://huggingface.co/dbmdz/bert-base-italian-xxl-cased">bert-base-italian-xx... | {"language": "it", "license": "afl-3.0", "widget": [{"text": "Il [MASK] ha chiesto revocarsi l'obbligo di pagamento"}]} | dlicari/Italian-Legal-BERT | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"it",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T15:51:39+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #it #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL width="600"/>
<h1> ITALIAN-LEGAL-BERT:A pre-trained Transformer Language Model for Italian Law </h1>
ITALIAN-LEGAL-BERT is based on <a href="URL with additional pre-training of the Italian BERT model on Italian civil law corpora.
It achieves better results than the ‘general-purpose’ Italian BERT in d... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #it #license-afl-3.0 #autotrain_compatible #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. -->
# bart-pizza
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-pizza", "results": []}]} | Konstantine4096/bart-pizza | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T16:07:17+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bart-pizza
This model is a fine-tuned version of facebook/bart-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# bart-pizza\n\nThis model is a fine-tuned version of facebook/bart-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-pizza\n\nThis model is a fine-tuned version of facebook/bart-base on the None dataset.",
"## Model description\n\nMore information needed",
... |
text-generation | transformers |
# The Wise DialoGPT Model | {"tags": ["conversational"]} | AbdalK25/DialoGPT-small-TheWiseBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-16T19:11:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# The Wise DialoGPT Model | [
"# The Wise DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# The Wise DialoGPT Model"
] |
null | null | Hola cómo estás
Hace un tiempo extra
Sí me gustaría | {"license": "afl-3.0"} | Monix/Pruebaaa | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-07-16T19:20:51+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| Hola cómo estás
Hace un tiempo extra
Sí me gustaría | [] | [
"TAGS\n#license-afl-3.0 #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. -->
# bart-pizza-5K
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-pizza-5K", "results": []}]} | Konstantine4096/bart-pizza-5K | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T19:55:57+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-pizza-5K
=============
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1688
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: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #bart #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: 5e-05\n* train\\_batch\\_size: 1\n* ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | csalcedo/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-16T19:58:29+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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... | Tstarshak/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-16T20:07:00+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 |
# Uriel Dot DialoGT Model | {"tags": ["conversational"]} | casperthegazer/DialoGT-gandalf-urdot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-16T21:04:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Uriel Dot DialoGT Model | [
"# Uriel Dot DialoGT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Uriel Dot DialoGT Model"
] |
token-classification | transformers |
# POS-Tagger Portuguese
We fine-tuned the [BERTimbau](https://github.com/neuralmind-ai/portuguese-bert/) model with the [MacMorpho](http://nilc.icmc.usp.br/macmorpho/) corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.9826.
Metrics:
```
Precision Recall F1 Suport
... | {"language": "pt", "datasets": ["MacMorpho"], "widget": [{"text": "Tinha uma pedra no meio do caminho."}, {"text": "Vamos tomar um caf\u00e9 quentinho?"}, {"text": "Como voc\u00ea se chama?"}]} | lisaterumi/postagger-portuguese | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"pt",
"dataset:MacMorpho",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-16T23:44:11+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #pt #dataset-MacMorpho #autotrain_compatible #endpoints_compatible #region-us
| POS-Tagger Portuguese
=====================
We fine-tuned the BERTimbau model with the MacMorpho corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.9826.
Metrics:
Parameters:
Tags:
How to cite
-----------
Questions?
----------
Please, post a Github issue on the NLP Portugu... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #pt #dataset-MacMorpho #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo1.3BInformalToFormal")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BInformalToForma")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tr... | {} | BigSalmon/GPTNeo1.3BInformalToFormal | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-17T00:52:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
make longer
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-base-ruquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gener... | {"language": "ru", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_ruquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "\u041d\u0435\u043b\u0438\u0448\u043d\u0438\u043c \u0431\u0443\u0434\u0435\u0442 \... | lmqg/mt5-base-ruquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"ru",
"dataset:lmqg/qg_ruquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T01:04:05+00:00 | [
"2210.03992"
] | [
"ru"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ru #dataset-lmqg/qg_ruquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-base-ruquad-qg'
=======================================
This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg\_ruquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-base
* Language: ru
* Training data: lmqg/qg\_ruqua... | [
"### Overview\n\n\n* Language model: google/mt5-base\n* Language: ru\n* Training data: lmqg/qg\\_ruquad (default)\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\n\n\n* ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #ru #dataset-lmqg/qg_ruquad #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: google/mt5-base\n* Language: ru\n*... |
fill-mask | transformers |
## RoBERTa Basque small model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of RoBERTa base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of [CC-100/eu](https://data.statmt.org/c... | {"language": "eu", "license": "cc-by-sa-4.0", "datasets": ["cc100", "oscar"], "widget": [{"text": "Euria egingo <mask> gaur ?"}, {"text": "<mask> umeari liburua eman dio."}, {"text": "Zein da zure <mask> ?"}]} | ClassCat/roberta-small-basque | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"eu",
"dataset:cc100",
"dataset:oscar",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T01:32:50+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #roberta #fill-mask #eu #dataset-cc100 #dataset-oscar #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## RoBERTa Basque small model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of RoBERTa base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of CC-100/eu : Monolingual Datasets from... | [
"## RoBERTa Basque small model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses approximately half the size of RoBERTa base model parameters.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data \n\n* Subset of CC-100/eu... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #eu #dataset-cc100 #dataset-oscar #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## RoBERTa Basque small model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses approximately... |
text-generation | transformers |
# 707 DialoGPT Model
Chatbot for the character 707 from Mystic Messenger. | {"tags": ["conversational"]} | pineappleSoup/DialoGPT-medium-707 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T01:47:25+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 707 DialoGPT Model
Chatbot for the character 707 from Mystic Messenger. | [
"# 707 DialoGPT Model\nChatbot for the character 707 from Mystic Messenger."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 707 DialoGPT Model\nChatbot for the character 707 from Mystic Messenger."
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | micheljperez/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-17T02:09:37+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Retrial9842/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-17T02:38:41+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
feature-extraction | transformers |
# koenbert-base
최근 다양한 한국어 언어 모델들이 개발 및 공유되고 있습니다. 하지만 이러한 모델들은 한국어만 지원하기 때문에 Dialog system, Information retrieval 등 다양한 도메인에서 제작되는 영어 데이터를 활용하기 어렵다는 한계점이 있습니다. Multilingual 모델의 경우 지원하는 언어의 수가 많아 모델 크기가 크고 한국어 성능이 떨어진다는 단점이 있습니다. 이러한 한계점을 해소하고 한국어 모델의 활용도를 높이기 위해 한국어 언어 모델에 영어를 학습하는 프로젝트를 진행하고 있습니다. 모델에 대한 자세한 정보는 [Gi... | {"language": "ko"} | yongsun-yoon/koenbert-base | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T03:30:02+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #feature-extraction #ko #endpoints_compatible #region-us
|
# koenbert-base
최근 다양한 한국어 언어 모델들이 개발 및 공유되고 있습니다. 하지만 이러한 모델들은 한국어만 지원하기 때문에 Dialog system, Information retrieval 등 다양한 도메인에서 제작되는 영어 데이터를 활용하기 어렵다는 한계점이 있습니다. Multilingual 모델의 경우 지원하는 언어의 수가 많아 모델 크기가 크고 한국어 성능이 떨어진다는 단점이 있습니다. 이러한 한계점을 해소하고 한국어 모델의 활용도를 높이기 위해 한국어 언어 모델에 영어를 학습하는 프로젝트를 진행하고 있습니다. 모델에 대한 자세한 정보는 Git... | [
"# koenbert-base\n최근 다양한 한국어 언어 모델들이 개발 및 공유되고 있습니다. 하지만 이러한 모델들은 한국어만 지원하기 때문에 Dialog system, Information retrieval 등 다양한 도메인에서 제작되는 영어 데이터를 활용하기 어렵다는 한계점이 있습니다. Multilingual 모델의 경우 지원하는 언어의 수가 많아 모델 크기가 크고 한국어 성능이 떨어진다는 단점이 있습니다. 이러한 한계점을 해소하고 한국어 모델의 활용도를 높이기 위해 한국어 언어 모델에 영어를 학습하는 프로젝트를 진행하고 있습니다. 모델에 대한 자세한 정보... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #ko #endpoints_compatible #region-us \n",
"# koenbert-base\n최근 다양한 한국어 언어 모델들이 개발 및 공유되고 있습니다. 하지만 이러한 모델들은 한국어만 지원하기 때문에 Dialog system, Information retrieval 등 다양한 도메인에서 제작되는 영어 데이터를 활용하기 어렵다는 한계점이 있습니다. Multilingual 모델의 경우 지원하는 언어의 수가 많아 모델 크기가 크고 한국어 성능이 ... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | micheljperez/testpyramidsrnd2 | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-17T03:30:19+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
text-generation | transformers |
## GPT2 Basque small model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of GPT2 base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of [CC-100/eu](https://data.statmt.... | {"language": "eu", "license": "cc-by-sa-4.0", "datasets": ["cc100", "oscar"], "widget": [{"text": "Zein da zure"}, {"text": "Euria egingo"}, {"text": "Nola dakizu ?"}]} | ClassCat/gpt2-small-basque-v2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"eu",
"dataset:cc100",
"dataset:oscar",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T03:43:31+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #eu #dataset-cc100 #dataset-oscar #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## GPT2 Basque small model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of GPT2 base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of CC-100/eu : Monolingual Datasets... | [
"## GPT2 Basque small model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses approximately half the size of GPT2 base model parameters.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data \n\n* Subset of CC-10... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #eu #dataset-cc100 #dataset-oscar #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## GPT2 Basque small model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architec... |
text2text-generation | transformers |
# cT5-small left-to-right
Github: https://github.com/mtreviso/chunked-t5
This is a variant of [cT5](https://huggingface.co/mtreviso/ct5-small-en-wiki) that was trained with a left-to-right autoregressive decoding mask. As a consequence, it does not support parallel decoding, but it still predicts the end-of-chunk to... | {"language": "en", "license": "afl-3.0", "tags": ["t5"], "datasets": ["wikipedia"]} | mtreviso/ct5-small-en-wiki-l2r | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"en",
"dataset:wikipedia",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T03:48:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #en #dataset-wikipedia #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# cT5-small left-to-right
Github: URL
This is a variant of cT5 that was trained with a left-to-right autoregressive decoding mask. As a consequence, it does not support parallel decoding, but it still predicts the end-of-chunk token '</c>' at the end of each chunk. | [
"# cT5-small left-to-right\n\nGithub: URL\n\nThis is a variant of cT5 that was trained with a left-to-right autoregressive decoding mask. As a consequence, it does not support parallel decoding, but it still predicts the end-of-chunk token '</c>' at the end of each chunk."
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #en #dataset-wikipedia #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# cT5-small left-to-right\n\nGithub: URL\n\nThis is a variant of cT5 that was trained with a left-to-right autor... |
null | transformers | Sidekick is a collaborative project aimed at testing and extending the capabilities of the Bloom model in code generation inspired by github copilot and AlphaCode sidekick wants to be more responsible because it is open source | {"license": "bigscience-bloom-rail-1.0", "tags": ["Text Generation", "PyTorch", "transformers", "Bloom", "NLP", "Code Generation"]} | OpenScience/Sidekick | null | [
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"Text Generation",
"PyTorch",
"Bloom",
"NLP",
"Code Generation",
"license:bigscience-bloom-rail-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T04:11:05+00:00 | [] | [] | TAGS
#transformers #Text Generation #PyTorch #Bloom #NLP #Code Generation #license-bigscience-bloom-rail-1.0 #endpoints_compatible #region-us
| Sidekick is a collaborative project aimed at testing and extending the capabilities of the Bloom model in code generation inspired by github copilot and AlphaCode sidekick wants to be more responsible because it is open source | [] | [
"TAGS\n#transformers #Text Generation #PyTorch #Bloom #NLP #Code Generation #license-bigscience-bloom-rail-1.0 #endpoints_compatible #region-us \n"
] |
question-answering | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
We
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remov... | {} | Duplets/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T05:26:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #endpoints_compatible #region-us
|
---
license: apache-2.0
tags:
* generated\_from\_trainer
datasets:
* squad
model-index:
* name: distilbert-base-uncased-finetuned-squad
results: []
We
distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad 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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #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\\_size: 16\n* seed: 42\n* optimizer... |
token-classification | transformers |
Cause-effect span detection for causal sequences:
```label_to_id = {'B-C': 0, 'B-E': 1, 'I-C': 2, 'I-E': 3, 'O': 4}```
* LABEL_0 = B-C
* LABEL_1 = B-E
* LABEL_2 = I-C
* LABEL_3 = I-E
* LABEL_4 = O
Trained on multiple datasets. | {"language": "en", "license": "unknown", "widget": [{"text": "She fell because he pushed her .", "example_title": "Causal Example 1"}, {"text": "He pushed her , causing her to fall.", "example_title": "Causal Example 2"}]} | tanfiona/unicausal-tok-baseline | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"en",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T06:06:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us
|
Cause-effect span detection for causal sequences:
* LABEL_0 = B-C
* LABEL_1 = B-E
* LABEL_2 = I-C
* LABEL_3 = I-E
* LABEL_4 = O
Trained on multiple datasets. | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us \n"
] |
zero-shot-image-classification | transformers | # Emoji Predictor
This model is Open AI's CLIP, fine-tuned on a few samples.
Try it here: https://huggingface.co/spaces/vincentclaes/emoji-predictor
- pretrained model: https://huggingface.co/openai/clip-vit-base-patch32
- dataset: https://huggingface.co/datasets/vincentclaes/emoji-predictor
Precision for predictio... | {"datasets": "vincentclaes/emoji-predictor"} | vincentclaes/emoji-predictor | null | [
"transformers",
"pytorch",
"clip",
"zero-shot-image-classification",
"dataset:vincentclaes/emoji-predictor",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-17T06:10:39+00:00 | [] | [] | TAGS
#transformers #pytorch #clip #zero-shot-image-classification #dataset-vincentclaes/emoji-predictor #endpoints_compatible #has_space #region-us
| # Emoji Predictor
This model is Open AI's CLIP, fine-tuned on a few samples.
Try it here: URL
- pretrained model: URL
- dataset: URL
Precision for predictions and suggestions for a range of samples per emoji:
| [
"# Emoji Predictor\n\nThis model is Open AI's CLIP, fine-tuned on a few samples.\n\nTry it here: URL\n\n- pretrained model: URL\n- dataset: URL\n\nPrecision for predictions and suggestions for a range of samples per emoji:"
] | [
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"# Emoji Predictor\n\nThis model is Open AI's CLIP, fine-tuned on a few samples.\n\nTry it here: URL\n\n- pretrained model: URL\n- dataset: URL\n\nPrecision fo... |
text-classification | transformers |
Binary causal sentence classification with argument prompts:
* LABEL_0 = Non-causal
* LABEL_1 = Causal (ARG0 causes ARG1)
Trained on multiple datasets.
For Causal sequences, try swapping the arguments to observe the prediction results. | {"language": "en", "license": "unknown", "widget": [{"text": "<ARG1>She fell</ARG1> because <ARG0>he pushed her</ARG0> .", "example_title": "Causal Example 1"}, {"text": "<ARG0>He pushed her</ARG0> , <ARG1>causing her to fall</ARG1>.", "example_title": "Causal Example 2"}, {"text": "<ARG0>She fell</ARG0> because <ARG1>... | tanfiona/unicausal-pair-baseline | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T06:11:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us
|
Binary causal sentence classification with argument prompts:
* LABEL_0 = Non-causal
* LABEL_1 = Causal (ARG0 causes ARG1)
Trained on multiple datasets.
For Causal sequences, try swapping the arguments to observe the prediction results. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | gorkemozkaya/keras_nmt_en_tr | null | [
"keras",
"region:us"
] | null | 2022-07-17T06:13:19+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from hugg... | {"library_name": "stable-baselines3", "tags": ["Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "ty... | workRL/ppo-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-17T07:36:04+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Walker2DBulletEnv-v0
This is a trained model of a PPO agent playing Walker2DBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baseline... |
text-generation | transformers |
# Hello | {"tags": ["conversational"]} | Nakul24/AD_ChatBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T07:45:19+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Hello | [
"# Hello"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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] |
null | null | FeiArt Handpainted CG Diffusion
is a custom diffusion model trained by @FeiArt_AiArt.
It can be used to create Handpainted CG style images.
To use it，you can use [FeiArt_Handpainted CG Diffusion](https://colab.research.google.com/drive/1u9ompOlBZMgIZc_KZvxIa3V6UD4Ch3dT?usp=sharing)
If you create a fun image with thi... | {"license": "cc"} | Feiart/FeiArt-Handpainted-CG-Diffusion | null | [
"license:cc",
"region:us"
] | null | 2022-07-17T08:18:45+00:00 | [] | [] | TAGS
#license-cc #region-us
| FeiArt Handpainted CG Diffusion
is a custom diffusion model trained by @FeiArt_AiArt.
It can be used to create Handpainted CG style images.
To use it，you can use FeiArt_Handpainted CG Diffusion
If you create a fun image with this model, please show your result and @FeiArt_AiArt
Or you can join the FeiArt Diffusion ... | [] | [
"TAGS\n#license-cc #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# xtremedistil-l6-h256-uncased-future-time-references-D2
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](... | {"license": "mit", "tags": ["generated_from_keras_callback"], "datasets": ["jonaskoenig/trump_administration_statement", "jonaskoenig/future-time-refernces-static-filter-D2"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-future-time-references-D2", "results": []}]} | jonaskoenig/xtremedistil-l6-h256-uncased-future-time-references-D2 | null | [
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"text-classification",
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"dataset:jonaskoenig/future-time-refernces-static-filter-D2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T08:33:05+00:00 | [] | [] | TAGS
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| xtremedistil-l6-h256-uncased-future-time-references-D2
======================================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on jonaskoenig/future-time-refernces-static-filter-D2 and jonaskoenig/trump\_administration\_statement.
It achieves the following r... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results\n\n\n\nThe test ac... | [
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"### Training hyperparameters\n\n\nThe followi... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | fadhilarkn/distilbert-base-uncased-finetuned-ner | null | [
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"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T08:36:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0574
* Precision: 0.9277
* Recall: 0.9387
* F1: 0.9332
* Accuracy: 0.9843
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-classification | transformers | # Label - Emotion Table
| Emotion | LABEL |
| -------------- |:-------------: |
| Anger | LABEL_0 |
| Boredom | LABEL_1 |
| Empty | LABEL_2 |
| Enthusiasm | LABEL_3 |
| Fear | LABEL_4 |
| Fun | LABEL_5 ... | {"license": "mit"} | Supreeth/DeBERTa-Twitter-Emotion-Classification | null | [
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T09:06:33+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Label - Emotion Table
=====================
| [] | [
"TAGS\n#transformers #pytorch #safetensors #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | 引自<https://github.com/yangjianxin1/CPM#model_share> | {"license": "mit"} | lewiswu1209/gpt2-chinese-composition | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T09:13:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| 引自<URL | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers | ### Quantized bigscience/bloom 6B3 with 8-bit weights
Heavily inspired by [Hivemind's GPT-J-6B with 8-bit weights](https://huggingface.co/hivemind/gpt-j-6B-8bit), this is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom-6b3) a ~6 billion parameters language model that you run and fine-tune with ... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zu"], "license": "bigscience-bloom-rai... | mrm8488/bloom-6b3-8bit | null | [
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Heavily inspired by Hivemind's GPT-J-6B with 8-bit weights, this is a version of bigscience/bloom a ~6 billion parameters language model that you run and fine-tune with less memory.
Here, we also apply LoRA (Low Rank Adaptation) to reduce model size.
### How to f... | [
"### Quantized bigscience/bloom 6B3 with 8-bit weights\n\nHeavily inspired by Hivemind's GPT-J-6B with 8-bit weights, this is a version of bigscience/bloom a ~6 billion parameters language model that you run and fine-tune with less memory.\n\nHere, we also apply LoRA (Low Rank Adaptation) to reduce model size.",
... | [
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text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1142742075
- CO2 Emissions (in grams): 3.973071565343572
## Validation Metrics
- Loss: 0.6098461151123047
- Accuracy: 0.7227722772277227
- Precision: 0.6805555555555556
- Recall: 0.9074074074074074
- AUC: 0.7480299448384554
- F1: 0.77... | {"language": "en", "tags": "autotrain", "datasets": ["Johny201/autotrain-data-article_pred"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.973071565343572} | Johny201/autotrain-article_pred-1142742075 | null | [
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"en",
"dataset:Johny201/autotrain-data-article_pred",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T09:28:59+00:00 | [] | [
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] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-Johny201/autotrain-data-article_pred #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1142742075
- CO2 Emissions (in grams): 3.973071565343572
## Validation Metrics
- Loss: 0.6098461151123047
- Accuracy: 0.7227722772277227
- Precision: 0.6805555555555556
- Recall: 0.9074074074074074
- AUC: 0.7480299448384554
- F1: 0.77... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1142742075\n- CO2 Emissions (in grams): 3.973071565343572",
"## Validation Metrics\n\n- Loss: 0.6098461151123047\n- Accuracy: 0.7227722772277227\n- Precision: 0.6805555555555556\n- Recall: 0.9074074074074074\n- AUC: 0.748029944... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-Johny201/autotrain-data-article_pred #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1142742075\n- CO2 Emissions ... |
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-ch02
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion-ch02", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "em... | pardeep/distilbert-base-uncased-finetuned-emotion-ch02 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T09:31:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion-ch02
==============================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1703
* Accuracy: 0.934
* F1: 0.9342
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers | ### Quantized bigscience/bloom 1B3 with 8-bit weights
Heavily inspired by [Hivemind's GPT-J-6B with 8-bit weights](https://huggingface.co/hivemind/gpt-j-6B-8bit), this is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom-1b3) a ~1 billion parameters language model that you run and fine-tune with ... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zu"], "license": "bigscience-bloom-rai... | mrm8488/bloom-1b3-8bit | null | [
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"sw... | null | 2022-07-17T09:49:29+00:00 | [
"2106.09685"
] | [
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"... | TAGS
#transformers #pytorch #bloom #text-generation #ak #ar #as #bm #bn #ca #en #es #eu #fon #fr #gu #hi #id #ig #ki #kn #lg #ln #ml #mr #ne #nso #ny #or #pa #pt #rn #rw #sn #st #sw #ta #te #tn #ts #tum #tw #ur #vi #wo #xh #yo #zh #zu #arxiv-2106.09685 #license-bigscience-bloom-rail-1.0 #autotrain_compatible #text-gene... | ### Quantized bigscience/bloom 1B3 with 8-bit weights
Heavily inspired by Hivemind's GPT-J-6B with 8-bit weights, this is a version of bigscience/bloom a ~1 billion parameters language model that you run and fine-tune with less memory.
Here, we also apply LoRA (Low Rank Adaptation) to reduce model size.
### How to f... | [
"### Quantized bigscience/bloom 1B3 with 8-bit weights\n\nHeavily inspired by Hivemind's GPT-J-6B with 8-bit weights, this is a version of bigscience/bloom a ~1 billion parameters language model that you run and fine-tune with less memory.\n\nHere, we also apply LoRA (Low Rank Adaptation) to reduce model size.",
... | [
"TAGS\n#transformers #pytorch #bloom #text-generation #ak #ar #as #bm #bn #ca #en #es #eu #fon #fr #gu #hi #id #ig #ki #kn #lg #ln #ml #mr #ne #nso #ny #or #pa #pt #rn #rw #sn #st #sw #ta #te #tn #ts #tum #tw #ur #vi #wo #xh #yo #zh #zu #arxiv-2106.09685 #license-bigscience-bloom-rail-1.0 #autotrain_compatible #tex... |
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="roykoand/q-FrozenLake-v1-4x4-noSlippery-v01", filename="q-learning.pkl")
# Don't forget to check if you need to add additional... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery-v01", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "typ... | roykoand/q-FrozenLake-v1-4x4-noSlippery-v01 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-17T10:50:10+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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"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": "7.56 +/... | roykoand/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-17T11:23:42+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"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | ranrinat/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T11:46:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2158
* Accuracy: 0.9245
* F1: 0.9246
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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="reachrkr/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | reachrkr/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-17T11:51:23+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="reachrkr/q-Taxi-v3-v1", 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-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54... | reachrkr/q-Taxi-v3-v1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-17T12:19:32+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"
] |
null | fastai |
# Identify This Insect
## Model description
This is a model used to differentiate between three types of insects:
- Millipede
- Centipede
- Caterpillar
It was created as part of the end-to-end Deep Learning Project learning process.
The model is a pretrained `convnext_tiny_in22k` from the [timm library](https://g... | {"tags": ["fastai"]} | Jimmie/identify-this-insect | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-07-17T13:06:07+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Identify This Insect
## Model description
This is a model used to differentiate between three types of insects:
- Millipede
- Centipede
- Caterpillar
It was created as part of the end-to-end Deep Learning Project learning process.
The model is a pretrained 'convnext_tiny_in22k' from the timm library fine-tuned ... | [
"# Identify This Insect",
"## Model description\n\nThis is a model used to differentiate between three types of insects:\n\n- Millipede\n- Centipede\n- Caterpillar\n\nIt was created as part of the end-to-end Deep Learning Project learning process.\n\nThe model is a pretrained 'convnext_tiny_in22k' from the timm l... | [
"TAGS\n#fastai #has_space #region-us \n",
"# Identify This Insect",
"## Model description\n\nThis is a model used to differentiate between three types of insects:\n\n- Millipede\n- Centipede\n- Caterpillar\n\nIt was created as part of the end-to-end Deep Learning Project learning process.\n\nThe model is a pret... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | meln1k/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-17T13:59:41+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
text2text-generation | transformers |
This is the [rut5-base](https://huggingface.co/cointegrated/rut5-base) model, with the decoder fine-tuned to recover (approximately) Russian sentences from their [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) embeddings. Details are [here](https://habr.com/ru/post/677618/) (in Russian).
It can be used, f... | {"language": ["ru"], "license": "mit", "tags": ["russian"]} | cointegrated/rut5-base-labse-decoder | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"russian",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T15:38:34+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is the rut5-base model, with the decoder fine-tuned to recover (approximately) Russian sentences from their LaBSE embeddings. Details are here (in Russian).
It can be used, for example, for:
- Paraphrasing Russian sentences;
- Translating from the 109 LaBSE languages to Russian;
- Summarizing a collection of sen... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | # t5-base-qa-ner-conll
Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
t5-base model tuned on conll2003 dataset.
https://github.com/ovbystrova/InstructionNER
## Inference
```shell
git clone https://github.com/ovbystrova/InstructionNER
cd InstructionNER
```
```python
from in... | {"language": ["en"], "license": "mit", "tags": ["pytorch", "ner", "text generation", "seq2seq"], "datasets": ["conll2003"], "metrics": ["f1"], "inference": false} | olgaduchovny/t5-base-ner-conll | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ner",
"text generation",
"seq2seq",
"en",
"dataset:conll2003",
"arxiv:2203.03903",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T15:38:40+00:00 | [
"2203.03903"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ner #text generation #seq2seq #en #dataset-conll2003 #arxiv-2203.03903 #license-mit #autotrain_compatible #text-generation-inference #region-us
| # t5-base-qa-ner-conll
Unofficial implementation of InstructionNER.
t5-base model tuned on conll2003 dataset.
URL
## Inference
## Prediction Sample
| [
"# t5-base-qa-ner-conll\n\nUnofficial implementation of InstructionNER.\nt5-base model tuned on conll2003 dataset.\n\nURL",
"## Inference",
"## Prediction Sample"
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"# t5-base-qa-ner-conll\n\nUnofficial implementation of InstructionNER.\nt5-base model tuned on conll2003 da... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-xsum-finetuned-paws
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xs... | {"tags": ["paraphrasing", "generated_from_trainer"], "datasets": ["paws"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-xsum-finetuned-paws", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "paws", "type": "paws", "args": "labeled_f... | domenicrosati/pegasus-xsum-finetuned-paws | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"paraphrasing",
"generated_from_trainer",
"dataset:paws",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T15:58:16+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #paraphrasing #generated_from_trainer #dataset-paws #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-xsum-finetuned-paws
===========================
This model is a fine-tuned version of google/pegasus-xsum on the paws dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1199
* Rouge1: 92.4371
* Rouge2: 75.4061
* Rougel: 84.1519
* Rougelsum: 84.1958
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #paraphrasing #generated_from_trainer #dataset-paws #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: 0.0001\n* tr... |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset in Russian
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Russian using a single-speaker dataset.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForC... | {"language": "ru", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "Russian-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-russian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"Russian-speech-corpus",
"PyTorch",
"ru",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T15:58:50+00:00 | [
"2204.00618"
] | [
"ru"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #Russian-speech-corpus #PyTorch #ru #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset in Russian
Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using a single-speaker dataset.
# Use this model
# Results
For the results check the paper
# Example test with Common Voice Dataset
| [
"# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset in Russian \n\nWav2vec2 Large 100k Voxpopuli fine-tuned in Russian using a single-speaker dataset.",
"# Use this model",
"# Results\nFor the results check the paper",
"# Example test with Common Voice Dataset"
] | [
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"# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset in Russian \n\nWav2vec2 Large ... |
automatic-speech-recognition | transformers |
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using a single-speaker dataset (TTS-Portuguese Corpus).
# Use this model
```python
... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["Common Voice"], "metrics": ["wer"]} | Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T16:18:45+00:00 | [
"2204.00618"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
Wav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using a single-speaker dataset (TTS-Portuguese Corpus).
# Use this model
# Results
For the results check the paper
# Example test with Common Voic... | [
"# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese \n\nWav2vec2 Large 100k Voxpopuli fine-tuned in Portuguese using a single-speaker dataset (TTS-Portuguese Corpus).",
"# Use this model",
"# Results\nFor the results check the paper",
"# Example test ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #arxiv-2204.00618 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in P... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-pubmed-finetuned-paws
This model is a fine-tuned version of [google/pegasus-pubmed](https://huggingface.co/google/pegasu... | {"tags": ["paraphrasing", "generated_from_trainer"], "datasets": ["paws"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-pubmed-finetuned-paws", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "paws", "type": "paws", "args": "labeled... | domenicrosati/pegasus-pubmed-finetuned-paws | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"paraphrasing",
"generated_from_trainer",
"dataset:paws",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T16:29:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #paraphrasing #generated_from_trainer #dataset-paws #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-pubmed-finetuned-paws
=============================
This model is a fine-tuned version of google/pegasus-pubmed on the paws dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5012
* Rouge1: 56.8108
* Rouge2: 36.2576
* Rougel: 51.1666
* Rougelsum: 51.2193
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #paraphrasing #generated_from_trainer #dataset-paws #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:... |
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="crdealme/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | crdealme/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-17T17:09:46+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"
] |
text2text-generation | transformers |
# grammar-synthesis-base (beta)
a fine-tuned version of [google/t5-base-lm-adapt](https://huggingface.co/google/t5-base-lm-adapt) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset. Check out a [demo notebook on Colab here](https://colab.research.google.com... | {"license": "cc-by-nc-sa-4.0", "tags": ["grammar", "spelling", "punctuation", "error-correction", "grammar synthesis"], "datasets": ["jfleg"], "widget": [{"text": "i can has cheezburger", "example_title": "cheezburger"}, {"text": "There car broke down so their hitching a ride to they're class.", "example_title": "compo... | pszemraj/grammar-synthesis-base | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"grammar",
"spelling",
"punctuation",
"error-correction",
"grammar synthesis",
"dataset:jfleg",
"arxiv:2107.06751",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-ge... | null | 2022-07-17T18:32:21+00:00 | [
"2107.06751"
] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #grammar #spelling #punctuation #error-correction #grammar synthesis #dataset-jfleg #arxiv-2107.06751 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# grammar-synthesis-base (beta)
a fine-tuned version of google/t5-base-lm-adapt for grammar correction on an expanded version of the JFLEG dataset. Check out a demo notebook on Colab here.
usage in Python (after 'pip install transformers'):
## Model description
The intent is to create a text2text language model... | [
"# grammar-synthesis-base (beta)\n\na fine-tuned version of google/t5-base-lm-adapt for grammar correction on an expanded version of the JFLEG dataset. Check out a demo notebook on Colab here.\n\nusage in Python (after 'pip install transformers'):",
"## Model description\n\nThe intent is to create a text2text lan... | [
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"# grammar-synthesis... |
null | pysentimiento |
# Named Entity Recognition model for Spanish/English
## robertuito-ner
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with the Spanish/English split of the [LinCE NER corpus](https://ritual.uh.edu/lince/), a code-switched benchmark . Bas... | {"language": ["es"], "library_name": "pysentimiento", "tags": ["twitter", "named-entity-recognition", "ner"], "datasets": ["lince"]} | pysentimiento/robertuito-ner | null | [
"pysentimiento",
"pytorch",
"roberta",
"twitter",
"named-entity-recognition",
"ner",
"es",
"dataset:lince",
"arxiv:2106.09462",
"region:us"
] | null | 2022-07-17T19:29:58+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#pysentimiento #pytorch #roberta #twitter #named-entity-recognition #ner #es #dataset-lince #arxiv-2106.09462 #region-us
| Named Entity Recognition model for Spanish/English
==================================================
robertuito-ner
--------------
Repository: URL
Model trained with the Spanish/English split of the LinCE NER corpus, a code-switched benchmark . Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.... | [] | [
"TAGS\n#pysentimiento #pytorch #roberta #twitter #named-entity-recognition #ner #es #dataset-lince #arxiv-2106.09462 #region-us \n"
] |
image-classification | transformers |
# ReXNet-1.3x model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks ... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["pyronear/openfire"]} | pyronear/rexnet1_3x | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T19:30:22+00:00 | [
"2007.00992"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us
|
# ReXNet-1.3x model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this paper.
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
... | [
"# ReXNet-1.3x model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redu... | [
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"# ReXNet-1.3x model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this pa... |
image-classification | transformers |
# ReXNet-1.5x model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks ... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["pyronear/openfire"]} | pyronear/rexnet1_5x | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T19:30:57+00:00 | [
"2007.00992"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us
|
# ReXNet-1.5x model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this paper.
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
... | [
"# ReXNet-1.5x model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redu... | [
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"# ReXNet-1.5x model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this pa... |
image-classification | transformers |
# MobileNet V3 - Large model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in [this paper](https://arxiv.org/pdf/1905.02244.pdf).
## Model description
The core idea of the author is to simplify the final stage, while using SiLU as acti... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["pyronear/openfire"]} | pyronear/mobilenet_v3_large | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:1905.02244",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T19:31:41+00:00 | [
"1905.02244"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1905.02244 #license-apache-2.0 #endpoints_compatible #region-us
|
# MobileNet V3 - Large model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in this paper.
## Model description
The core idea of the author is to simplify the final stage, while using SiLU as activations and making Squeeze-and-Excite bl... | [
"# MobileNet V3 - Large model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to simplify the final stage, while using SiLU as activations and making Squeeze-and... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1905.02244 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MobileNet V3 - Large model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introd... |
image-classification | transformers |
# ResNet-18 model
Pretrained on a dataset for wildfire binary classification (soon to be shared).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["pyronear/openfire"]} | pyronear/resnet18 | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:1512.03385",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T20:06:58+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us
|
# ResNet-18 model
Pretrained on a dataset for wildfire binary classification (soon to be shared).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and pip/conda are ... | [
"# ResNet-18 model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared).",
"## Model description\n\nThe core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.",
"## Installation",
"### Prerequisites\n\nPython 3.6 (or higher... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ResNet-18 model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared).",
"## Model description\n\nThe core idea of the a... |
image-classification | transformers |
# ResNet-34 model
Pretrained on a dataset for wildfire binary classification (soon to be shared).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["pyronear/openfire"]} | pyronear/resnet34 | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:1512.03385",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T20:07:12+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us
|
# ResNet-34 model
Pretrained on a dataset for wildfire binary classification (soon to be shared).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and pip/conda are ... | [
"# ResNet-34 model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared).",
"## Model description\n\nThe core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.",
"## Installation",
"### Prerequisites\n\nPython 3.6 (or higher... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-pyronear/openfire #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ResNet-34 model\n\nPretrained on a dataset for wildfire binary classification (soon to be shared).",
"## Model description\n\nThe core idea of the a... |
token-classification | transformers | # POS Tagging model for Spanish/English
## robertuito-pos
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with the Spanish/English split of the [LinCE NER corpus](https://ritual.uh.edu/lince/), a code-switched benchmark . Base model is [Ro... | {"language": ["es"], "tags": ["twitter", "pos-tagging"]} | pysentimiento/robertuito-pos | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"twitter",
"pos-tagging",
"es",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T20:22:11+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #twitter #pos-tagging #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #region-us
| POS Tagging model for Spanish/English
=====================================
robertuito-pos
--------------
Repository: URL
Model trained with the Spanish/English split of the LinCE NER corpus, a code-switched benchmark . Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.
Usage
-----
If you wa... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #twitter #pos-tagging #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | NikitaErmolaev/Reinforce-PixelCopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-17T21:26:55+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
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... | yixi/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 | 2022-07-17T22:09:39+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: 0.0573
* Precision: 0.9343
* Recall: 0.9495
* F1: 0.9418
* Accuracy: 0.9868
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... |
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. -->
# roberta-base-spanish-squades-modelo-robertav1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-modelo-robertav1", "results": []}]} | Evelyn18/BECASV4.1 | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T22:12:15+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-modelo-robertav1
=============================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7840
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-base-frquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gener... | {"language": "fr", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_frquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Cr\u00e9ateur \u00bb (Maker), lui aussi au singulier, \u00ab <hl> le Supr\u00eame... | lmqg/mt5-base-frquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"fr",
"dataset:lmqg/qg_frquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-17T22:33:30+00:00 | [
"2210.03992"
] | [
"fr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #fr #dataset-lmqg/qg_frquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-base-frquad-qg'
=======================================
This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg\_frquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-base
* Language: fr
* Training data: lmqg/qg\_frqua... | [
"### Overview\n\n\n* Language model: google/mt5-base\n* Language: fr\n* Training data: lmqg/qg\\_frquad (default)\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\n\n\n* ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #fr #dataset-lmqg/qg_frquad #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: google/mt5-base\n* Language: fr\n*... |
text-classification | transformers |
To use our fine-tuned BioBERT model to predict whether a sentence from a radiology reports makes reference to priors, run the following:
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
modelname = "rajpurkarlab/filbert"
tokenizer = AutoTokenizer.from_pretrained(modelname)
model = Au... | {"language": ["py"], "metrics": ["f1"]} | rajpurkarlab/filbert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"py",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T23:13:36+00:00 | [] | [
"py"
] | TAGS
#transformers #pytorch #bert #text-classification #py #autotrain_compatible #endpoints_compatible #region-us
|
To use our fine-tuned BioBERT model to predict whether a sentence from a radiology reports makes reference to priors, run the following:
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #py #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# namwoo/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "namwoo/distilbert-base-uncased-finetuned-ner", "results": []}]} | namwoo/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-17T23:35:17+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| namwoo/distilbert-base-uncased-finetuned-ner
============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0339
* Validation Loss: 0.0623
* Train Precision: 0.9239
* Train Rec... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightD... |
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