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values | library_name stringclasses 198
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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... | Kuro96/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T20:20:36+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/596705203358801920/mQ6ZG... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bicyclingmag-bike24net-planetcyclery/1658612826681/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bicyclingmag-bike24net-planetcyclery | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-23T20:38:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Bicycling Magazine & BIKE24 & Planet Cyclery
@bicyclingmag-bike24net-planetcyclery
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CarPole1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | SusBioRes-UBC/Reinforce-CarPole1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-23T21:08:22+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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 fram... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | Chris1/qrdqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T21:19:33+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN 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 ag... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN 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... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **A2C** 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": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Chris1/a2c-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T21:22:54+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a A2C 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... | [
"# A2C Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a A2C 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",
"# A2C Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a A2C agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-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 huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | abcp4/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-23T21:49:48+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
text2text-generation | transformers | KoBART 기반 경상도 사투리 스타일 변경
- AI-HUB 의 경상도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다. | {} | circulus/kobart-trans-gyeongsang-v1 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T00:54:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| KoBART 기반 경상도 사투리 스타일 변경
- AI-HUB 의 경상도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | KoBART 기반 전라도 사투리 스타일 변경
- AI-HUB 의 전라도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다. | {} | circulus/kobart-trans-jeolla-v1 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T01:02:09+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| KoBART 기반 전라도 사투리 스타일 변경
- AI-HUB 의 전라도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | KoBART 기반 충청도 사투리 스타일 변경
- AI-HUB 의 충청도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다. | {} | circulus/kobart-trans-chungcheong-v1 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T01:04:57+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| KoBART 기반 충청도 사투리 스타일 변경
- AI-HUB 의 충청도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NERDE-base
This model is a fine-tuned version of [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou... | {"tags": ["generated_from_trainer"], "datasets": ["nerde"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Considerando-se os argumentos elencados pela Peticion\u00e1ria, infere-se que a CNH Industrial det\u00e9m leg\u00edtimo interesse pelo caso em ep\u00edgrafe, visto que pode ser afetada ... | Gpaiva/NERDE-base | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:nerde",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T01:13:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-nerde #model-index #autotrain_compatible #endpoints_compatible #region-us
| NERDE-base
==========
This model is a fine-tuned version of pierreguillou/bert-base-cased-pt-lenerbr on the nerde dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1246
* Precision: 0.9119
* Recall: 0.9153
* F1: 0.9136
* Accuracy: 0.9842
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-nerde #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: 2e... |
null | null | 3750 images from esa/nasa of deep space. JWST/Hubble shots mainly.
model_config.update({
'attention_resolutions': '32, 16, 8',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'timestep_respacing': 'ddim100',
'image_size': 256,
'learn_sigma': True,
'noise_sched... | {"license": "cc-by-4.0"} | laproper/diffusion-deepspace-256 | null | [
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-24T01:55:23+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
| 3750 images from esa/nasa of deep space. JWST/Hubble shots mainly.
model_config.update({
'attention_resolutions': '32, 16, 8',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'timestep_respacing': 'ddim100',
'image_size': 256,
'learn_sigma': True,
'noise_sched... | [] | [
"TAGS\n#license-cc-by-4.0 #region-us \n"
] |
text-generation | transformers | # Winnie
Winnie是基于[cambridgeltl/simctg_lccc_dialogue](https://huggingface.co/cambridgeltl/simctg_lccc_dialogue)训练的
我修改了vocab.txt, 新增了`[NAME][NICK][GENDER][YEAROFBIRTH][MONTHOFBIRTH][DAYOFBIRTH][ZODIAC][AGE]`几个special_token，然后搞了些类似
```
你是谁？
我是[NAME]。
你叫什么？
我叫[NAME]。
你多大啦？
我[AGE]岁了。
```
的语料。
第一次训练的时候起名叫Vicky，然后把Vicky... | {"license": "mit"} | lewiswu1209/Winnie | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T02:09:47+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Winnie
Winnie是基于cambridgeltl/simctg_lccc_dialogue训练的
我修改了vocab.txt, 新增了'[NAME][NICK][GENDER][YEAROFBIRTH][MONTHOFBIRTH][DAYOFBIRTH][ZODIAC][AGE]'几个special_token，然后搞了些类似
的语料。
第一次训练的时候起名叫Vicky，然后把Vicky的脑子训瓦特了，只能摸索新的办法了。
后来利用了50W闲聊语料搭配新增的语料按照大约19:1的比例进行训练，感觉效果还可以。 | [
"# Winnie\nWinnie是基于cambridgeltl/simctg_lccc_dialogue训练的\n\n我修改了vocab.txt, 新增了'[NAME][NICK][GENDER][YEAROFBIRTH][MONTHOFBIRTH][DAYOFBIRTH][ZODIAC][AGE]'几个special_token，然后搞了些类似\n\n的语料。\n\n第一次训练的时候起名叫Vicky，然后把Vicky的脑子训瓦特了，只能摸索新的办法了。\n后来利用了50W闲聊语料搭配新增的语料按照大约19:1的比例进行训练，感觉效果还可以。"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Winnie\nWinnie是基于cambridgeltl/simctg_lccc_dialogue训练的\n\n我修改了vocab.txt, 新增了'[NAME][NICK][GENDER][YEAROFBIRTH][MONTHOFBIRTH][DAYOFBIRTH][ZODIAC][AGE... |
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-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-finetuned-imdb", "results": []}]} | Sidhanttholenlp/distilbert-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T04:04:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-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: 2.4667
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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="WasuratS/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": ... | WasuratS/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-24T05:22:21+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="WasuratS/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | WasuratS/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-24T05:28:01+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 | null | He was just trying out to be the first time | {} | Hairyrice/H | null | [
"region:us"
] | null | 2022-07-24T05:33:50+00:00 | [] | [] | TAGS
#region-us
| He was just trying out to be the first time | [] | [
"TAGS\n#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... | Hrushi/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T06:14:14+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 | transformers |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'e... | {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2... | utyug1/ppo-LunarLander-v2 | null | [
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T07:09:03+00:00 | [] | [] | TAGS
#transformers #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #endpoints_compatible #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters"
] | [
"TAGS\n#transformers #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #endpoints_compatible #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\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. -->
# 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": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_... | tnavin/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T07:34:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #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 wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3202
* Precision: 0.5900
* Recall: 0.4118
* F1: 0.4850
* Accuracy: 0.9304
Model desc... | [
"### 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 #token-classification #generated_from_trainer #dataset-wnut_17 #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* lear... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0005
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0005", "results": []}]} | bigmorning/distilgpt_new3_0005 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T07:41:04+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0005
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5375
* Validation Loss: 2.4210
* Epoch: 4
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
sentence-similarity | sentence-transformers |
# sorayutmild/simcse-model-wangchanberta-finetuned-sanook-news
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Tr... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sorayutmild/simcse-model-wangchanberta-finetuned-sanook-news | null | [
"sentence-transformers",
"pytorch",
"camembert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T07:45:14+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #camembert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# sorayutmild/simcse-model-wangchanberta-finetuned-sanook-news
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have se... | [
"# sorayutmild/simcse-model-wangchanberta-finetuned-sanook-news\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when y... | [
"TAGS\n#sentence-transformers #pytorch #camembert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# sorayutmild/simcse-model-wangchanberta-finetuned-sanook-news\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector ... |
text-classification | transformers | <strong>Classifier of opinion conveyed by vaccine-related content in Italian language</strong></br>
A monolingual model for classifying the opinion conveyed through vaccine-related content in Italian language. The model was trained on 36,722 and independently tested on 9,299 social media content between Facebook posts,... | {"license": "mit"} | brema76/vaccine_opinion_it | null | [
"transformers",
"tf",
"bert",
"text-classification",
"arxiv:2207.12264",
"arxiv:2301.05961",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T07:55:34+00:00 | [
"2207.12264",
"2301.05961"
] | [] | TAGS
#transformers #tf #bert #text-classification #arxiv-2207.12264 #arxiv-2301.05961 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| <strong>Classifier of opinion conveyed by vaccine-related content in Italian language</strong></br>
A monolingual model for classifying the opinion conveyed through vaccine-related content in Italian language. The model was trained on 36,722 and independently tested on 9,299 social media content between Facebook posts,... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #arxiv-2207.12264 #arxiv-2301.05961 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# transformer-NLP
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "transformer-NLP", "results": []}]} | onon214/transformer-NLP | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T08:31:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| transformer-NLP
===============
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 8.4503
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_si... |
question-answering | 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. -->
# DMath/gelectra-finetuned-squad
This model is a fine-tuned version of [deepset/gelectra-base-germanquad](https://huggingface.co/deepset... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "DMath/gelectra-finetuned-squad", "results": []}]} | DMath/gelectra-finetuned-squad | null | [
"transformers",
"tf",
"electra",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T08:34:30+00:00 | [] | [] | TAGS
#transformers #tf #electra #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
| DMath/gelectra-finetuned-squad
==============================
This model is a fine-tuned version of deepset/gelectra-base-germanquad on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.2648
* Epoch: 0
Model description
-----------------
More information needed
Inten... | [
"### 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': 7418, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #electra #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'Pol... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0010
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0010", "results": []}]} | bigmorning/distilgpt_new3_0010 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T09:07:46+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0010
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5339
* Validation Loss: 2.4177
* Epoch: 9
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-pokemon
This model is a fine-tuned version of [ydmeira/segformer-b0-finetuned-pokemon](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-pokemon", "results": []}]} | ydmeira/segformer-b0-finetuned-pokemon | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T09:40:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-pokemon
==============================
This model is a fine-tuned version of ydmeira/segformer-b0-finetuned-pokemon on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0157
* Mean Iou: 0.4970
* Mean Accuracy: 0.9940
* Overall Accuracy: 0.9940
* Per Category... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #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: 6e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-a-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail)... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-prompt-a-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "a... | research-backup/roberta-large-semeval2012-average-prompt-a-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T09:41:09+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-prompt-a-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accu... | [
"# relbert/roberta-large-semeval2012-average-prompt-a-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-prompt-a-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository ... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-b-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail)... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-prompt-b-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "a... | research-backup/roberta-large-semeval2012-average-prompt-b-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T09:42:35+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-prompt-b-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accu... | [
"# relbert/roberta-large-semeval2012-average-prompt-b-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-prompt-b-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository ... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-c-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail)... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-prompt-c-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "a... | research-backup/roberta-large-semeval2012-average-prompt-c-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T09:43:59+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-prompt-c-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accu... | [
"# relbert/roberta-large-semeval2012-average-prompt-c-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-prompt-c-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository ... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-d-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail)... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-prompt-d-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "a... | research-backup/roberta-large-semeval2012-average-prompt-d-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T09:45:27+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-prompt-d-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accu... | [
"# relbert/roberta-large-semeval2012-average-prompt-d-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-prompt-d-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository ... |
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... | tk648/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 | 2022-07-24T09:53:32+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: 0.2221
* Accuracy: 0.9215
* F1: 0.9217
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 | 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... | ChechkovYevhen/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T10:11:19+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 | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "reinforce-cartpole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | Chris1/reinforce-cartpole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-24T10:23:31+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0015
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0015", "results": []}]} | bigmorning/distilgpt_new3_0015 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T10:34:44+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0015
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5302
* Validation Loss: 2.4153
* Epoch: 14
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# modeversion2_m7_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-b... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "modeversion2_m7_e8", "results": []}]} | sudo-s/modeversion2_m7_e8 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T11:04:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| modeversion2\_m7\_e8
====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem7 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1060
* Accuracy: 0.9761
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0020
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0020", "results": []}]} | bigmorning/distilgpt_new3_0020 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T12:01:08+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0020
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5267
* Validation Loss: 2.4110
* Epoch: 19
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-a-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
I... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-a-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accu... | research-backup/roberta-large-semeval2012-mask-prompt-a-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T12:29:44+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-a-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accurac... | [
"# relbert/roberta-large-semeval2012-mask-prompt-a-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):\n ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-a-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-b-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
I... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-b-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accu... | research-backup/roberta-large-semeval2012-mask-prompt-b-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T12:31:49+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-b-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accurac... | [
"# relbert/roberta-large-semeval2012-mask-prompt-b-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):\n ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-b-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-c-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
I... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-c-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accu... | research-backup/roberta-large-semeval2012-mask-prompt-c-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T12:34:08+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-c-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accurac... | [
"# relbert/roberta-large-semeval2012-mask-prompt-c-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):\n ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-c-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-d-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
I... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-d-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accu... | research-backup/roberta-large-semeval2012-mask-prompt-d-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T12:36:16+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-d-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accurac... | [
"# relbert/roberta-large-semeval2012-mask-prompt-d-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):\n ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-d-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-e-triplet
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[semeval2012](https://huggingface.co/datasets/semeval2012).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
I... | {"datasets": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-e-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accu... | research-backup/roberta-large-semeval2012-mask-prompt-e-triplet | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:semeval2012",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T12:38:27+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-e-triplet
RelBERT fine-tuned from roberta-large on
semeval2012.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
- Accurac... | [
"# relbert/roberta-large-semeval2012-mask-prompt-e-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset, full result):\n ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-e-triplet\n\nRelBERT fine-tuned from roberta-large on \nsemeval2012.\nFine-tuning is done via RelBERT library (see the repository for... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-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 huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | NikitaErmolaev/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T12:49:44+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
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. -->
# deberta-v3-large-finetuned-synthetic-generated-only
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://h... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-synthetic-generated-only", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-synthetic-generated-only | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T12:50:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-synthetic-generated-only
===================================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0094
* F1: 0.9839
* Precision: 0.9849
* Recall: 0.9828
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
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... | affahrizain/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T13:15:56+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1858
* Accuracy: 0.936
* F1: 0.9361
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
image-classification | transformers |
# pond_image_classification
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/na... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T13:18:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal
... | [
"# pond_image_classification\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boili... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRepor... |
null | null |
Test | {"pinned": true} | osanseviero/my-sunset | null | [
"region:us"
] | null | 2022-07-24T13:18:29+00:00 | [] | [] | TAGS
#region-us
|
Test | [] | [
"TAGS\n#region-us \n"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-llama
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/di... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "llama", "metrics": []} | osanseviero/my-llama | null | [
"diffusers",
"tensorboard",
"en",
"dataset:llama",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-24T13:26:50+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-llama #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# my-llama
## Model description
This diffusion model is trained with the Diffusers library
on the 'llama' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used t... | [
"# my-llama",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'llama' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training data\n\n[TOD... | [
"TAGS\n#diffusers #tensorboard #en #dataset-llama #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# my-llama",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'llama' dataset.",
"## Intended uses & limitations",
"#### How to use",
"####... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0025
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0025", "results": []}]} | bigmorning/distilgpt_new3_0025 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T13:28:09+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0025
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5232
* Validation Loss: 2.4072
* Epoch: 24
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
object-detection | 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. -->
# detr-resnet-50_fine_tuned_nls_chapbooks
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/f... | {"license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["biglam/nls_chapbook_illustrations"], "widget": [{"src": "https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/Chapbook_Jack_the_Giant_Killer.jpg", "example_title": "Jack the Gia... | biglam/detr-resnet-50_fine_tuned_nls_chapbooks | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:biglam/nls_chapbook_illustrations",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-24T13:29:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-biglam/nls_chapbook_illustrations #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# detr-resnet-50_fine_tuned_nls_chapbooks
This model is a fine-tuned version of facebook/detr-resnet-50 on the 'biglam/nls_chapbook_illustrations' dataset. This dataset contains images of chapbooks with bounding boxes for the illustrations contained on some of the pages.
## Model description
More information nee... | [
"# detr-resnet-50_fine_tuned_nls_chapbooks\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 on the 'biglam/nls_chapbook_illustrations' dataset. This dataset contains images of chapbooks with bounding boxes for the illustrations contained on some of the pages.",
"## Model description\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-biglam/nls_chapbook_illustrations #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# detr-resnet-50_fine_tuned_nls_chapbooks\n\nThis model is a ... |
image-classification | transformers |
# rust_image_classification
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/na... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T13:46:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRepor... |
question-answering | transformers |
# Test model for DL4NLP 2022 HW06
xtremedistil-l6-h256-uncased trained on SQuAD
## Hyper parameters
- learning rate: 1e-5
- weight decay: 0.01
- warm up steps: 0
- learning rate scheduler: linear
- epochs: 1
## Metric results on the dev set
- F1: 65.48
- EM: 51.67 | {"language": ["en"], "license": "mit", "tags": ["question-answering"], "datasets": ["SQuAD"], "metrics": ["EM", "F1"]} | SebOchs/xtremedistil-l6-h256-uncased-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"dataset:SQuAD",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T14:04:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #dataset-SQuAD #license-mit #endpoints_compatible #region-us
|
# Test model for DL4NLP 2022 HW06
xtremedistil-l6-h256-uncased trained on SQuAD
## Hyper parameters
- learning rate: 1e-5
- weight decay: 0.01
- warm up steps: 0
- learning rate scheduler: linear
- epochs: 1
## Metric results on the dev set
- F1: 65.48
- EM: 51.67 | [
"# Test model for DL4NLP 2022 HW06 \nxtremedistil-l6-h256-uncased trained on SQuAD",
"## Hyper parameters\n- learning rate: 1e-5 \n- weight decay: 0.01\n- warm up steps: 0\n- learning rate scheduler: linear \n- epochs: 1",
"## Metric results on the dev set\n- F1: 65.48\n- EM: 51.67"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-SQuAD #license-mit #endpoints_compatible #region-us \n",
"# Test model for DL4NLP 2022 HW06 \nxtremedistil-l6-h256-uncased trained on SQuAD",
"## Hyper parameters\n- learning rate: 1e-5 \n- weight decay: 0.01\n- warm up steps: 0\n- learning r... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Daveee/gpl_colbert | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T14:17:27+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
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"]} | danieladejumo/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-24T14:18:14+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 | 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="th1s1s1t/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": ... | th1s1s1t/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-24T14:20:25+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"
] |
image-classification | transformers | train this model on the Contest
the original dataset is
链接: https://pan.baidu.com/s/1pr094NZ2QMj3nLy12gfa6g 密码: kb7a | {"license": "other"} | HaoHu/vit-base-patch16-224-in21k-classify-4scence | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T14:23:48+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #license-other #autotrain_compatible #endpoints_compatible #region-us
| train this model on the Contest
the original dataset is
链接: URL 密码: kb7a | [] | [
"TAGS\n#transformers #pytorch #vit #image-classification #license-other #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | #### The luxembourgish part of my multilingual automatic speech recognition (ASR) model is the second Machine Learning (ML) STT model for Luxembourgish. The very first model has been published in May 2022 by [Pr Peter Gilles](https://infolux.uni.lu/automatic-speech-recognition-in-luxembourgish-a-very-first-model/) of t... | {"language": ["lb", "de", "fr", "en", "pt"], "license": "cc-by-nc-sa-4.0", "tags": ["STT", "ASR", "audio", "speech recognition", "coqui.ai"], "datasets": ["mbarnig/lb-2880-STT-CORPUS"]} | mbarnig/lb-de-fr-en-pt-coqui-stt-models | null | [
"tflite",
"tensorboard",
"STT",
"ASR",
"audio",
"speech recognition",
"coqui.ai",
"lb",
"de",
"fr",
"en",
"pt",
"dataset:mbarnig/lb-2880-STT-CORPUS",
"license:cc-by-nc-sa-4.0",
"has_space",
"region:us"
] | null | 2022-07-24T14:28:08+00:00 | [] | [
"lb",
"de",
"fr",
"en",
"pt"
] | TAGS
#tflite #tensorboard #STT #ASR #audio #speech recognition #coqui.ai #lb #de #fr #en #pt #dataset-mbarnig/lb-2880-STT-CORPUS #license-cc-by-nc-sa-4.0 #has_space #region-us
| #### The luxembourgish part of my multilingual automatic speech recognition (ASR) model is the second Machine Learning (ML) STT model for Luxembourgish. The very first model has been published in May 2022 by Pr Peter Gilles of the University of Luxembourg.
#### My model has been trained from scratch with my customize... | [
"#### The luxembourgish part of my multilingual automatic speech recognition (ASR) model is the second Machine Learning (ML) STT model for Luxembourgish. The very first model has been published in May 2022 by Pr Peter Gilles of the University of Luxembourg.",
"#### My model has been trained from scratch with my c... | [
"TAGS\n#tflite #tensorboard #STT #ASR #audio #speech recognition #coqui.ai #lb #de #fr #en #pt #dataset-mbarnig/lb-2880-STT-CORPUS #license-cc-by-nc-sa-4.0 #has_space #region-us \n",
"#### The luxembourgish part of my multilingual automatic speech recognition (ASR) model is the second Machine Learning (ML) STT mo... |
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... | Chris1/reinforce-pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-24T14:43:10+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 ... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0030
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0030", "results": []}]} | bigmorning/distilgpt_new3_0030 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T14:54:12+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0030
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5197
* Validation Loss: 2.4026
* Epoch: 29
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-aurora
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/d... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "aurora", "metrics": []} | osanseviero/my-aurora | null | [
"diffusers",
"tensorboard",
"en",
"dataset:aurora",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-24T14:55:45+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-aurora #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# my-aurora
## Model description
This diffusion model is trained with the Diffusers library
on the 'aurora' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used... | [
"# my-aurora",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'aurora' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training data\n\n[T... | [
"TAGS\n#diffusers #tensorboard #en #dataset-aurora #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# my-aurora",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'aurora' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#... |
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="jakka/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"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": ... | jakka/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-24T15:07:08+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="jakka/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | jakka/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-24T15:09:31+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-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. -->
# distilgpt_new3_0035
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0035", "results": []}]} | bigmorning/distilgpt_new3_0035 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T16:19:59+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0035
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5161
* Validation Loss: 2.3990
* Epoch: 34
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-fine-tuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "met... | phamvanlinh143/bert-fine-tuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T16:20:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-fine-tuned-cola
====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8760
* Matthews Correlation: 0.5676
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* l... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-aurora
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/d... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": ["\ud83e\udde8 Diffuse It"], "datasets": "aurora", "metrics": []} | nateraw/my-aurora | null | [
"diffusers",
"tensorboard",
"🧨 Diffuse It",
"en",
"dataset:aurora",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-24T16:32:57+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #🧨 Diffuse It #en #dataset-aurora #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# my-aurora
## Model description
This diffusion model is trained with the Diffusers library
on the 'aurora' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used... | [
"# my-aurora",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'aurora' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training data\n\n[T... | [
"TAGS\n#diffusers #tensorboard #🧨 Diffuse It #en #dataset-aurora #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# my-aurora",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'aurora' dataset.",
"## Intended uses & limitations",
"#### Ho... |
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... | Amiri/1_land_on_the_moon_PPO | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T17:03:08+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... |
question-answering | transformers |
## Model description
PyTorch implementation containing all the modelling needed for your NLP task. Combines a language
model and a prediction head. Allows for gradient flow back to the language model component.
## Model Type
not defined
## Model Details ##
- version: 1
- device: cuda
- number of l... | {"language": "en", "tags": "Not available", "datasets": [], "metrics": "Not available"} | Sarmila/distilbert-base-uncased-distilled-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"Not available",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T17:32:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #question-answering #Not available #en #endpoints_compatible #region-us
|
## Model description
PyTorch implementation containing all the modelling needed for your NLP task. Combines a language
model and a prediction head. Allows for gradient flow back to the language model component.
## Model Type
not defined
## Model Details ##
- version: 1
- device: cuda
- number of l... | [
"## Model description\n\n \n PyTorch implementation containing all the modelling needed for your NLP task. Combines a language\n model and a prediction head. Allows for gradient flow back to the language model component.",
"## Model Type\n\n not defined",
"## Model Details ##\n- version: 1\n- device: cuda... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #Not available #en #endpoints_compatible #region-us \n",
"## Model description\n\n \n PyTorch implementation containing all the modelling needed for your NLP task. Combines a language\n model and a prediction head. Allows for gradient flow back t... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0040
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0040", "results": []}]} | bigmorning/distilgpt_new3_0040 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T17:46:05+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0040
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5130
* Validation Loss: 2.3972
* Epoch: 39
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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... | jakka/dqn-SpaceInvadersNoFrameskip-v4_1 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T18:02:51+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... |
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. -->
# my-finetuned-t5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves t... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-finetuned-t5", "results": []}]} | sushrut58/my-finetuned-t5 | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T18:13:21+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# my-finetuned-t5
This model is a fine-tuned version of t5-small 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
## Trai... | [
"# my-finetuned-t5\n\nThis model is a fine-tuned version of t5-small 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 infor... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# my-finetuned-t5\n\nThis model is a fine-tuned version of t5-small on an unknown dataset.\nIt achieves the following result... |
null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | {"title": "CarClassification", "emoji": "\ud83d\udd25", "colorFrom": "pink", "colorTo": "red", "sdk": "gradio", "sdk_version": "3.1.1", "app_file": "app.py", "pinned": false} | ojaylet/as_4 | null | [
"region:us"
] | null | 2022-07-24T18:27:41+00:00 | [] | [] | TAGS
#region-us
|
Check out the configuration reference at URL | [] | [
"TAGS\n#region-us \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"]} | jakka/unitypyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-24T18:44:08+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-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. -->
# xlnet-base-rte-finetuned
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on th... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "xlnet-base-rte-finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type":... | vish88/xlnet-base-rte-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T18:58:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-rte-finetuned
========================
This model is a fine-tuned version of xlnet-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6688
* Accuracy: 0.7040
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-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 huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | pm390/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T19:06:13+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0045
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0045", "results": []}]} | bigmorning/distilgpt_new3_0045 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T19:14:36+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0045
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5095
* Validation Loss: 2.3923
* Epoch: 44
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-e-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi4... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-prompt-e-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"... | research-backup/roberta-large-semeval2012-average-prompt-e-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T19:44:09+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-prompt-e-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset... | [
"# relbert/roberta-large-semeval2012-average-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Questi... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine... |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Imene/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T19:56:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Abd Elmadjid Raib
!Abd Elmadjid Raib
#### Abd-Eldjalil_Safssafi
!Abd-Eldjalil_Safssafi
#### Abdelhak Seb... | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Abd Elmadjid Raib\n\n!Abd Elmadjid Raib",
"#### Abd-Eldjalil_Safssafi\n\n!Abd-Eldjalil_Sa... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-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 ...
fro... | {"library_name": "stable-baselines3", "tags": ["HalfCheetahBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetahBulletEnv-v0... | pm390/a2c-HalfCheetahBulletEnv-v0 | null | [
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T20:03:24+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing HalfCheetahBulletEnv-v0
This is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0050
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0050", "results": []}]} | bigmorning/distilgpt_new3_0050 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T20:42:19+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0050
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5062
* Validation Loss: 2.3894
* Epoch: 49
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text2text-generation | transformers | KoBART 기반 언어 스타일 변환
- Smilegate AI 의 SmileStyle 데이터 셋을 통해 훈련된 모델 입니다. (https://github.com/smilegate-ai/korean_smile_style_dataset)
- 사용방법은 곧 올리도록 하겠습니다. | {} | circulus/kobart-style-v1 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T20:44:14+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| KoBART 기반 언어 스타일 변환
- Smilegate AI 의 SmileStyle 데이터 셋을 통해 훈련된 모델 입니다. (URL
- 사용방법은 곧 올리도록 하겠습니다. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Legal_BERTimbau
## Introduction
Legal_BERTimbau Large is a fine-tuned BERT model based on [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
"BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: ... | {"language": ["pt"], "license": "mit", "tags": ["bert", "pytorch"], "datasets": ["rufimelo/PortugueseLegalSentences-v0"], "thumbnail": "Portugues BERT for the Legal Domain", "widget": [{"text": "O advogado apresentou [MASK] ao ju\u00edz."}]} | rufimelo/Legal-BERTimbau-large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"pt",
"dataset:rufimelo/PortugueseLegalSentences-v0",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T21:29:50+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #fill-mask #pt #dataset-rufimelo/PortugueseLegalSentences-v0 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Legal\_BERTimbau
================
Introduction
------------
Legal\_BERTimbau Large is a fine-tuned BERT model based on BERTimbau Large.
"BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence ... | [
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use this work, please cite BERTimbau's work:"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #pt #dataset-rufimelo/PortugueseLegalSentences-v0 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Masked language modeling prediction example",
"### For BERT embeddings\n\n\nIf you use this work, please cite BERTimbau's work:"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0055
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0055", "results": []}]} | bigmorning/distilgpt_new3_0055 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T22:08:24+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0055
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5035
* Validation Loss: 2.3859
* Epoch: 54
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-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 huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | Chris1/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T22:39:24+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-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": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "pong-reinforce", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{"type":... | Chris1/pong-reinforce | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-24T22:40:16+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1173943517
- CO2 Emissions (in grams): 0.687008092853648
## Validation Metrics
- Loss: 2.806302070617676
- Rouge1: 0.0342
- Rouge2: 0.006
- RougeL: 0.0242
- RougeLsum: 0.0283
- Gen Len: 19.9989
## Usage
You can use cURL to access this model... | {"language": "unk", "tags": "autotrain", "datasets": ["ben-yu/autotrain-data-MS2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.687008092853648} | ben-yu/autotrain-MS2-1173943517 | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"autotrain",
"unk",
"dataset:ben-yu/autotrain-data-MS2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-24T23:06:06+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #led #text2text-generation #autotrain #unk #dataset-ben-yu/autotrain-data-MS2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1173943517
- CO2 Emissions (in grams): 0.687008092853648
## Validation Metrics
- Loss: 2.806302070617676
- Rouge1: 0.0342
- Rouge2: 0.006
- RougeL: 0.0242
- RougeLsum: 0.0283
- Gen Len: 19.9989
## Usage
You can use cURL to access this model... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1173943517\n- CO2 Emissions (in grams): 0.687008092853648",
"## Validation Metrics\n\n- Loss: 2.806302070617676\n- Rouge1: 0.0342\n- Rouge2: 0.006\n- RougeL: 0.0242\n- RougeLsum: 0.0283\n- Gen Len: 19.9989",
"## Usage\n\nYou can use ... | [
"TAGS\n#transformers #pytorch #led #text2text-generation #autotrain #unk #dataset-ben-yu/autotrain-data-MS2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1173943517\n- CO2 Emissions (in grams): 0.6870080... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-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 ...
fro... | {"library_name": "stable-baselines3", "tags": ["HalfCheetahBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetahBulletEnv-v0... | Chris1/a2c-HalfCheetahBulletEnv-v0 | null | [
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T23:09:13+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing HalfCheetahBulletEnv-v0
This is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **A2C** 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": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "ty... | Chris1/a2c-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-24T23:16:11+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing Walker2DBulletEnv-v0
This is a trained model of a A2C agent playing Walker2DBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a A2C 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",
"# A2C Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a A2C agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baseline... |
text-generation | transformers |
# Shrek DialoGPT Model | {"tags": ["conversational"]} | Bman/DialoGPT-medium-shrek | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T23:32:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Shrek DialoGPT Model | [
"# Shrek DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Shrek DialoGPT Model"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0060
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0060", "results": []}]} | bigmorning/distilgpt_new3_0060 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-24T23:32:14+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0060
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5002
* Validation Loss: 2.3821
* Epoch: 59
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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. -->
# nlp-esg-scoring/bert-base-finetuned-esg-a4s-clean
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/ber... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlp-esg-scoring/bert-base-finetuned-esg-a4s-clean", "results": []}]} | nlp-esg-scoring/bert-base-finetuned-esg-a4s-clean | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T00:04:00+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nlp-esg-scoring/bert-base-finetuned-esg-a4s-clean
=================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5224
* Validation Loss: 2.2196
* Epoch: 9
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'cl... |
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. -->
# bertiny-finetuned-finer
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["finer-139", "nlpaueb/finer-139"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "google/bert_uncased_L-2_H-128_A-2", "model-index": [{"name": "bertiny-finetuned-finer", "results": [{"task": {"type": "token-classification",... | muhtasham/bert-tiny-finetuned-finer | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:finer-139",
"dataset:nlpaueb/finer-139",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region... | null | 2022-07-25T00:20:25+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #dataset-finer-139 #dataset-nlpaueb/finer-139 #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bertiny-finetuned-finer
=======================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the finer-139 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0882
* Precision: 0.5339
* Recall: 0.0360
* F1: 0.0675
* Accuracy: 0.9847
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #dataset-finer-139 #dataset-nlpaueb/finer-139 #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\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. -->
# nlp-esg-scoring/bert-base-finetuned-esg-TCFD-clean
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/be... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlp-esg-scoring/bert-base-finetuned-esg-TCFD-clean", "results": []}]} | nlp-esg-scoring/bert-base-finetuned-esg-TCFD-clean | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T00:48:03+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nlp-esg-scoring/bert-base-finetuned-esg-TCFD-clean
==================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7816
* Validation Loss: 2.3592
* Epoch: 9
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'cl... |
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. -->
# nlp-esg-scoring/bert-base-finetuned-esg-snpcsr-clean
This model was trained from scratch on an unknown dataset.
It achieves the follow... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlp-esg-scoring/bert-base-finetuned-esg-snpcsr-clean", "results": []}]} | nlp-esg-scoring/bert-base-finetuned-esg-snpcsr-clean | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T00:50:47+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| nlp-esg-scoring/bert-base-finetuned-esg-snpcsr-clean
====================================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4074
* Validation Loss: 2.2353
* Epoch: 9
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0065
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new3_0065", "results": []}]} | bigmorning/distilgpt_new3_0065 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-25T00:55:06+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new3\_0065
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4971
* Validation Loss: 2.3779
* Epoch: 64
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### 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",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertiny-finetuned-finer-full
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/go... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["finer-139"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "google/bert_uncased_L-2_H-128_A-2", "model-index": [{"name": "bertiny-finetuned-finer-full", "results": [{"task": {"type": "token-classification", "name": "Token ... | muhtasham/bert-tiny-finetuned-finer-longer | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:finer-139",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T00:58:25+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #dataset-finer-139 #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bertiny-finetuned-finer-full
============================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the 10% of finer-139 dataset for 40 epochs according to paper.
It achieves the following results on the evaluation set:
* Loss: 0.0788
* Precision: 0.5554
* Recall: 0.5164
* F1: 0... | [
"### 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: 40",
"### Trainin... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #dataset-finer-139 #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparamet... |
question-answering | transformers |
# INT8 BERT base uncased finetuned on Squad
### Post-training static quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [jimypbr/bert-base-uncased-squad](https://huggingface.co/ji... | {"license": "apache-2.0", "tags": ["int8", "Intel\u00ae Neural Compressor", "neural-compressor", "PostTrainingStatic"], "datasets": ["squad"], "metrics": ["f1"]} | Intel/bert-base-uncased-squad-int8-static | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"int8",
"Intel® Neural Compressor",
"neural-compressor",
"PostTrainingStatic",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T01:15:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| INT8 BERT base uncased finetuned on Squad
=========================================
### Post-training static quantization
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model jimypbr/bert-base-uncased-squad.
The calibration dataloader is ... | [
"### Post-training static quantization\n\n\nThis is an INT8 PyTorch model quantized with Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model jimypbr/bert-base-uncased-squad.\n\n\nThe calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't di... | [
"TAGS\n#transformers #pytorch #bert #question-answering #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Post-training static quantization\n\n\nThis is an INT8 PyTorch model quantized with Intel® Neural Compressor.... |
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. -->
# nlp-esg-scoring/bert-base-finetuned-esg-gri-clean
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/ber... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlp-esg-scoring/bert-base-finetuned-esg-gri-clean", "results": []}]} | nlp-esg-scoring/bert-base-finetuned-esg-gri-clean | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T01:32:56+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nlp-esg-scoring/bert-base-finetuned-esg-gri-clean
=================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.9511
* Validation Loss: 1.5293
* Epoch: 9
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'cl... |
automatic-speech-recognition | transformers | # exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s377
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When u... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s377 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T01:50:49+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s377
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s377\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound too... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s377\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition usin... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2-agu
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2-agu", "results": []}]} | arminmehrabian/distilgpt2-finetuned-wikitext2-agu | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-25T01:53:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2-agu
==================================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1869
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
automatic-speech-recognition | transformers | # exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When u... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T01:56:00+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound too... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition usin... |
automatic-speech-recognition | transformers | # exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s527
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When u... | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s527 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T02:01:13+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s527
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s527\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition using the train split of Common Voice 7.0 (de).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound too... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s527\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition usin... |
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