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reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-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_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | masterdezign/ppo-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-21T10:56:49+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-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_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | masterdezign/ppo-CarRacing-v0-1 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-21T11:26:29+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
question-answering | transformers |
### Time: 2020/07/10
### ICAN-AI
| {"license": "afl-3.0"} | LDY/Question-Answering-Ican | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"license:afl-3.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-21T11:27:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #license-afl-3.0 #endpoints_compatible #has_space #region-us
|
### Time: 2020/07/10
### ICAN-AI
| [
"### Time: 2020/07/10",
"### ICAN-AI"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #license-afl-3.0 #endpoints_compatible #has_space #region-us \n",
"### Time: 2020/07/10",
"### ICAN-AI"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 11405516
- CO2 Emissions (in grams): 28.375764585180136
## Validation Metrics
- Loss: 1.5257819890975952
- Rouge1: 41.9534
- Rouge2: 18.5044
- RougeL: 34.7507
- RougeLsum: 38.6091
- Gen Len: 15.1037
## Usage
You can use cURL to access this ... | {"language": "unk", "tags": "autotrain", "datasets": ["abhishek/autotrain-data-summtest1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 28.375764585180136} | abhishek/autotrain-summtest1-11405516 | null | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"autotrain",
"unk",
"dataset:abhishek/autotrain-data-summtest1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T11:33:40+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #longt5 #text2text-generation #autotrain #unk #dataset-abhishek/autotrain-data-summtest1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 11405516
- CO2 Emissions (in grams): 28.375764585180136
## Validation Metrics
- Loss: 1.5257819890975952
- Rouge1: 41.9534
- Rouge2: 18.5044
- RougeL: 34.7507
- RougeLsum: 38.6091
- Gen Len: 15.1037
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 11405516\n- CO2 Emissions (in grams): 28.375764585180136",
"## Validation Metrics\n\n- Loss: 1.5257819890975952\n- Rouge1: 41.9534\n- Rouge2: 18.5044\n- RougeL: 34.7507\n- RougeLsum: 38.6091\n- Gen Len: 15.1037",
"## Usage\n\nYou can... | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #autotrain #unk #dataset-abhishek/autotrain-data-summtest1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 11405516\n- CO2 Emissions (in grams): ... |
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. -->
# ddpm-ema-pokemon-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hug... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/pokemon", "metrics": []} | anton-l/ddpm-ema-pokemon-64 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/pokemon",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-21T11:36:43+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-pokemon-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/pokemon' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: desc... | [
"# ddpm-ema-pokemon-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## T... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-pokemon-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### Ho... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **AntBulletEnv-v0**
This is a trained model of a **PPO** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hu... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | Al020198zee/ppo-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-21T14:10:06+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing AntBulletEnv-v0
This is a trained model of a PPO agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
MODEL
model = PPO(policy = "MlpPolicy",
env = env,
batch_size = 256,
clip_range = 0.4,
ent_coef = ... | [
"# PPO Agent playing AntBulletEnv-v0\nThis is a trained model of a PPO agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\n\n\nMODEL\nmodel = PPO(policy = \"MlpPolicy\",\n env = env,\n batch_size = 256,\n clip_range = 0.4,\n ... | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing AntBulletEnv-v0\nThis is a trained model of a PPO agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\n\n\nMODEL\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BART_reddit_gaming
This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "BART_reddit_gaming", "results": []}]} | trevorj/BART_reddit_gaming | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T14:20:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BART\_reddit\_gaming
====================
This model is a fine-tuned version of sshleifer/distilbart-xsum-6-6 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7373
* Rouge1: 18.1202
* Rouge2: 4.6045
* Rougel: 15.1273
* Rougelsum: 15.7601
* Gen Len: 18.208
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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
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-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | masterdezign/reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-21T14:21:50+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 |
# **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... | ThomasSimonini/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"has_space",
"region:us"
] | null | 2022-07-21T14:38:48+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #has_space #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 #has_space #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)\... |
text-generation | transformers | EalAIn is a DialoGPT model loosely based on "Janet" from the TV Series "The Good Place". This particular instance of Janet is responds to the name "Ealain" and has some knowledge about art. It will, at times, promote me as an AI artist. | {"license": "afl-3.0"} | Ian-AI/EalAIn | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-21T14:43:30+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| EalAIn is a DialoGPT model loosely based on "Janet" from the TV Series "The Good Place". This particular instance of Janet is responds to the name "Ealain" and has some knowledge about art. It will, at times, promote me as an AI artist. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | sentence-transformers |
#
This is the ONNX model of sentence-transformers/gtr-t5-xl [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). Currently, Hugging Face does not support downloading ONNX files with external format files. I have created a workaround using sbert and optimum together to generate embedd... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "transformers", "onnx"]} | vamsibanda/sbert-onnx-gtr-t5-xl | null | [
"sentence-transformers",
"onnx",
"t5",
"sentence-similarity",
"feature-extraction",
"transformers",
"en",
"arxiv:2112.07899",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T15:02:04+00:00 | [
"2112.07899"
] | [
"en"
] | TAGS
#sentence-transformers #onnx #t5 #sentence-similarity #feature-extraction #transformers #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #region-us
|
#
This is the ONNX model of sentence-transformers/gtr-t5-xl Large Dual Encoders Are Generalizable Retrievers. Currently, Hugging Face does not support downloading ONNX files with external format files. I have created a workaround using sbert and optimum together to generate embeddings.
Then you can use the model ... | [
"# \n\nThis is the ONNX model of sentence-transformers/gtr-t5-xl Large Dual Encoders Are Generalizable Retrievers. Currently, Hugging Face does not support downloading ONNX files with external format files. I have created a workaround using sbert and optimum together to generate embeddings.\n\n\n\nThen you can use ... | [
"TAGS\n#sentence-transformers #onnx #t5 #sentence-similarity #feature-extraction #transformers #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# \n\nThis is the ONNX model of sentence-transformers/gtr-t5-xl Large Dual Encoders Are Generalizable Retrievers. Currently, Hugging Face ... |
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. -->
# distilgpt_new_0100
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_new_0100", "results": []}]} | bigmorning/distilbert_new_0100 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T15:14:42+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| distilgpt\_new\_0100
====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0286
* Validation Loss: 0.9952
* Epoch: 99
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 #distilbert #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': 2e-05, 'decay': 0.... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BART_reddit_other
This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "BART_reddit_other", "results": []}]} | trevorj/BART_reddit_other | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T15:49:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BART\_reddit\_other
===================
This model is a fine-tuned version of sshleifer/distilbart-xsum-6-6 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5792
* Rouge1: 18.5705
* Rouge2: 5.0107
* Rougel: 15.2581
* Rougelsum: 16.082
* Gen Len: 19.402
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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\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-finetuned-resumes-sections
This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-cased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "distilBERT-finetuned-resumes-sections", "results": []}]} | has-abi/distilBERT-finetuned-resumes-sections | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-21T16:08:29+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilBERT-finetuned-resumes-sections
=====================================
This model is a fine-tuned version of Geotrend/distilbert-base-en-fr-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0369
* F1: 0.9652
* Roc Auc: 0.9808
* Accuracy: 0.9621
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: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_bat... |
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. -->
# ddpm-ema-pokemon-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hug... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/pokemon", "metrics": []} | mrm8488/ddpm-ema-pokemon-64 | null | [
"diffusers",
"en",
"dataset:huggan/pokemon",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-21T16:27:48+00:00 | [] | [
"en"
] | TAGS
#diffusers #en #dataset-huggan/pokemon #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-pokemon-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/pokemon' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: desc... | [
"# ddpm-ema-pokemon-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## T... | [
"TAGS\n#diffusers #en #dataset-huggan/pokemon #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
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"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How ... |
text-generation | transformers |
# Bushcat DialoGPT-Large Model
A personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.
Large "smarter" model based on DialoGPT-Large. iI you use this, this is the recommended version (compared to **TeaTM/DialoGPT-small-bushcat**).
The character plays the persona of a cat in a b... | {"language": ["en"], "tags": ["conversational", "DialoGPT"]} | TeaTM/DialoGPT-large-bushcat | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"DialoGPT",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-21T16:31:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #DialoGPT #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Bushcat DialoGPT-Large Model
A personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.
Large "smarter" model based on DialoGPT-Large. iI you use this, this is the recommended version (compared to TeaTM/DialoGPT-small-bushcat).
The character plays the persona of a cat in a bush ... | [
"# Bushcat DialoGPT-Large Model\n\nA personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.\n\nLarge \"smarter\" model based on DialoGPT-Large. iI you use this, this is the recommended version (compared to TeaTM/DialoGPT-small-bushcat).\n\n\nThe character plays the persona of a c... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #DialoGPT #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bushcat DialoGPT-Large Model\n\nA personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.\n\nLarge \"smart... |
null | null | oghdogspsdfughuisdfhgsudfigdfg
https://www.xing.com/events/new | {} | iuihgisgsd/jhgifgdsg | null | [
"region:us"
] | null | 2022-07-21T17:01:13+00:00 | [] | [] | TAGS
#region-us
| oghdogspsdfughuisdfhgsudfigdfg
URL | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice.
### Pre-training
It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings C... | {"language": "unk", "tags": ["autotrain", "pre-trained", "finbert", "fill-mask"], "widget": [{"text": "Tesla remains one of the highest [MASK] stocks on the market. Meanwhile, Aurora Innovation is a pre-revenue upstart that shows promise."}, {"text": "Asian stocks [MASK] from a one-year low on Wednesday as U.S. share f... | FinanceInc/finbert-pretrain | null | [
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"pytorch",
"bert",
"fill-mask",
"autotrain",
"pre-trained",
"finbert",
"unk",
"arxiv:2006.08097",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T17:11:17+00:00 | [
"2006.08097"
] | [
"unk"
] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain #pre-trained #finbert #unk #arxiv-2006.08097 #autotrain_compatible #endpoints_compatible #region-us
|
'FinBERT' is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice.
### Pre-training
It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings C... | [
"### Pre-training\nIt is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.\n\n- Corporate Reports 10-K & 10-Q: 2.5B tokens\n- Earnings Call Transcripts: 1.3B tokens\n- Analyst Reports: 1.1B tokens\n\nThe entire training is done using an NVIDIA DGX-1 machine. The s... | [
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"### Pre-training\nIt is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.\n\n- Corporate Reports 1... |
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. -->
# lakshaywadhwa1993/mt5-base-finetuned-hindi-mt5-base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/goo... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "lakshaywadhwa1993/mt5-base-finetuned-hindi-mt5-base", "results": []}]} | lakshaywadhwa1993/mt5-base-finetuned-hindi-mt5-base | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-21T17:20:07+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| lakshaywadhwa1993/mt5-base-finetuned-hindi-mt5-base
===================================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1571
* Validation Loss: 1.0867
* Epoch: 4
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 61500, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #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* optimizer: {'name': 'Adam... |
null | null | ujkghjghjfhfghjf
https://www.xing.com/events/new | {} | iuihgisgsd/ogjoidsg | null | [
"region:us"
] | null | 2022-07-21T17:27:15+00:00 | [] | [] | TAGS
#region-us
| ujkghjghjfhfghjf
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | elden ring, elden ring release date, elden ring gameplay, elden ring ps4, elden ring review, elden ring wiki, elden ring classes, elden ring trailer, elden ring platforms, elden ring pc, elden ring map, elden ring xbox one, elden ring dlc
Download Setup & Crack - https://tlniurl.com/2sqLiI
It is the year 1900, and ... | {} | gerollouruc/Elden_Ring | null | [
"region:us"
] | null | 2022-07-21T17:32:05+00:00 | [] | [] | TAGS
#region-us
| elden ring, elden ring release date, elden ring gameplay, elden ring ps4, elden ring review, elden ring wiki, elden ring classes, elden ring trailer, elden ring platforms, elden ring pc, elden ring map, elden ring xbox one, elden ring dlc
Download Setup & Crack - URL
It is the year 1900, and the mysterious continen... | [] | [
"TAGS\n#region-us \n"
] |
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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"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": ... | AdoubleLen/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-21T18:15:07+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"
] |
feature-extraction | transformers |
This is a [MicroBERT](https://github.com/lgessler/microbert) model for Coptic.
* Its suffix is **-mx**, which means that it was pretrained using supervision from masked language modeling and XPOS tagging.
* The unlabeled Coptic data was taken from version 4.2.0 of the [Coptic SCRIPTORIUM corpus](https://github.com/co... | {"language": "cop", "widget": [{"text": "\u2c81\u2c97\u2c97\u2c81 \u2c81\u2c9b\u2c9f\u2c95 \u2c81\u2c93\u2ca5\u2c89\u2ca7\u2ca1\u2ca7\u2c8f\u2ca9\u2ca7\u2c9b \u00b7"}]} | lgessler/microbert-coptic-mx | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"cop",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T18:21:10+00:00 | [] | [
"cop"
] | TAGS
#transformers #pytorch #bert #feature-extraction #cop #endpoints_compatible #region-us
|
This is a MicroBERT model for Coptic.
* Its suffix is -mx, which means that it was pretrained using supervision from masked language modeling and XPOS tagging.
* The unlabeled Coptic data was taken from version 4.2.0 of the Coptic SCRIPTORIUM corpus, totaling 970,642 tokens.
* The UD treebank UD_Coptic_Scriptorium, ... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #cop #endpoints_compatible #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... | TheJarmanitor/rl-class-1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-21T18:57:23+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 |
# **Q-Learning** Agent playing **FrozenLake-v2-4x4-Slippery**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v2-4x4-Slippery** .
## Usage
```python
model = load_from_hub(repo_id="nikitakapitan/FrozenLake-v2-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "FrozenLake-v2-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "metri... | nikitakapitan/FrozenLake-v2-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-21T19:31:46+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v2-4x4-Slippery
This is a trained model of a Q-Learning agent playing FrozenLake-v2-4x4-Slippery .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v2-4x4-Slippery\n This is a trained model of a Q-Learning agent playing FrozenLake-v2-4x4-Slippery .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v2-4x4-Slippery\n This is a trained model of a Q-Learning agent playing FrozenLake-v2-4x4-Slippery .\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="AdoubleLen/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": "7.52 +/... | AdoubleLen/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-21T19:40:13+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"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "s... | csmartins8/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T20:14:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1374
* F1: 0.8632
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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... | dvalbuena1/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-21T20:45:45+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... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-voxrex-npsc-nst-bokmaal-fixed
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://hugging... | {"license": "cc0-1.0", "tags": ["generated_from_trainer"], "base_model": "KBLab/wav2vec2-large-voxrex", "model-index": [{"name": "wav2vec2-large-voxrex-npsc-nst-bokmaal-fixed", "results": []}]} | NbAiLab/wav2vec2-large-voxrex-npsc-nst-bokmaal-fixed | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:KBLab/wav2vec2-large-voxrex",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T21:16:02+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-KBLab/wav2vec2-large-voxrex #license-cc0-1.0 #endpoints_compatible #region-us
| wav2vec2-large-voxrex-npsc-nst-bokmaal-fixed
============================================
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0482
* Wer: 0.0493
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-KBLab/wav2vec2-large-voxrex #license-cc0-1.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n*... |
text-generation | transformers |
This is a GPT-2 model fine-tuned on the [succinctly/midjourney-prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts) dataset, which contains 250k text prompts that users issued to the [Midjourney](https://www.midjourney.com/) text-to-image service over a month period. For more details on how this dat... | {"language": ["en"], "license": "cc-by-2.0", "tags": ["text2image", "prompting"], "datasets": ["succinctly/midjourney-prompts"], "thumbnail": "https://drive.google.com/uc?export=view&id=1JWwrxQbr1s5vYpIhPna_p2IG1pE5rNiV"} | succinctly/text2image-prompt-generator | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"text2image",
"prompting",
"en",
"dataset:succinctly/midjourney-prompts",
"license:cc-by-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-21T21:17:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #text2image #prompting #en #dataset-succinctly/midjourney-prompts #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This is a GPT-2 model fine-tuned on the succinctly/midjourney-prompts dataset, which contains 250k text prompts that users issued to the Midjourney text-to-image service over a month period. For more details on how this dataset was scraped, see Midjourney User Prompts & Generated Images (250k).
This prompt generator ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #text2image #prompting #en #dataset-succinctly/midjourney-prompts #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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="Gianni33/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": ... | Gianni33/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-21T21:30:53+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | Gianni33/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-21T21:38:44+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 | 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. -->
# rule_learning_1mm_many_negatives_spanpred_avf
This model is a fine-tuned version of [enoriega/rule_softmatching](https://hugging... | {"tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_1mm_many_negatives_spanpred_avf", "results": []}]} | enoriega/rule_learning_1mm_many_negatives_spanpred_mse_attention | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T21:44:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us
| rule\_learning\_1mm\_many\_negatives\_spanpred\_avf
===================================================
This model is a fine-tuned version of enoriega/rule\_softmatching on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0731
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_... |
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. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | bert-bits/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-21T21:45:46+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longt5-mediasum
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tgloba... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer", "longt5", "summarization"], "base_model": "google/long-t5-tglobal-base", "model-index": [{"name": "longt5-mediasum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "xsum", "type": "xsum", "config": "default",... | nbroad/longt5-base-global-mediasum | null | [
"transformers",
"pytorch",
"safetensors",
"longt5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"base_model:google/long-t5-tglobal-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-21T21:46:35+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #summarization #base_model-google/long-t5-tglobal-base #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| longt5-mediasum
===============
This model is a fine-tuned version of google/long-t5-tglobal-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0129
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\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* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #summarization #base_model-google/long-t5-tglobal-base #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hype... |
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_new2_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_new2_0020", "results": []}]} | bigmorning/distilgpt_new2_0020 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-21T22:15:20+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new2\_0020
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.6019
* Validation Loss: 2.4890
* 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\... |
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... | Chris1/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-21T22:29:17+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... |
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. -->
# resnet-50-cifar10-quality-drift
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/res... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cifar10_quality_drift"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "resnet-50-cifar10-quality-drift", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cifar10_quality_drif... | arize-ai/resnet-50-cifar10-quality-drift | null | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:cifar10_quality_drift",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T22:44:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-cifar10_quality_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| resnet-50-cifar10-quality-drift
===============================
This model is a fine-tuned version of microsoft/resnet-50 on the cifar10\_quality\_drift dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8235
* Accuracy: 0.724
* F1: 0.7222
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-cifar10_quality_drift #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... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-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_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | TheJarmanitor/mountain-car-v0-bonus | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-21T23:14:47+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing3_ft_pretrain2_wav2vec2-large-xlsr-53
This model is a fine-tuned version of [gary109/ai-light-dance_singin... | {"tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing3_ft_pretrain2_wav2vec2-large-xlsr-53", "results": []}]} | gary109/ai-light-dance_singing3_ft_pretrain2_wav2vec2-large-xlsr-53 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-21T23:21:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us
| ai-light-dance\_singing3\_ft\_pretrain2\_wav2vec2-large-xlsr-53
===============================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing3\_ft\_pretrain2\_wav2vec2-large-xlsr-53 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING3 dataset.
It achieves the follow... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size... |
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-cartpole1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"t... | jsalvatier/Reinforce-cartpole1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-21T23:41:06+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... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m2m100_418M-finetuned-kde4-en-to-pt_BR
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/faceb... | {"license": "mit", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "m2m100_418M-finetuned-kde4-en-to-pt_BR", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type":... | danhsf/m2m100_418M-finetuned-kde4-en-to-pt_BR | null | [
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T00:46:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# m2m100_418M-finetuned-kde4-en-to-pt_BR
This model is a fine-tuned version of facebook/m2m100_418M on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5150
- Bleu: 58.3196
## Model description
More information needed
## Intended uses & limitations
More information needed
##... | [
"# m2m100_418M-finetuned-kde4-en-to-pt_BR\n\nThis model is a fine-tuned version of facebook/m2m100_418M on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5150\n- Bleu: 58.3196",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# m2m100_418M-finetuned-kde4-en-to-pt_BR\n\nThis model is a fine-tuned version of facebook/m2m100_418M... |
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/1280474754214957056/GKqk... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hotwingsuk/1658460403599/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hotwingsuk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T02:25:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
HotWings
@hotwingsuk
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- 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_new2_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_new2_0040", "results": []}]} | bigmorning/distilgpt_new2_0040 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T03:29:53+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new2\_0040
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5812
* Validation Loss: 2.4689
* 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\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "s... | RupE/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T03:35:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1354
* F1: 0.8503
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# distilbert_new2_0020
This model is a fine-tuned version of [/content/drive/MyDrive/Colab Notebooks/oscar/trybackup_distilbert/new_back... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_new2_0020", "results": []}]} | bigmorning/distilbert_new2_0020 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T03:36:21+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_new2\_0020
======================
This model is a fine-tuned version of /content/drive/MyDrive/Colab Notebooks/oscar/trybackup\_distilbert/new\_backup\_0105105 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9920
* Validation Loss: 0.9688
* Epoch: 19
Mod... | [
"### 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 #distilbert #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': 2e-05, 'decay': 0.... |
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... | Krs/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 | 2022-07-22T04:15:00+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: 0.2197
* Accuracy: 0.921
* F1: 0.9214
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: 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 #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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | RupE/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T04:25:23+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1632
* F1: 0.8505
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT-zero-shot-finetuned-50cad
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggi... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "Bio_ClinicalBERT-zero-shot-finetuned-50cad", "results": []}]} | okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50cad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T04:29:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Bio_ClinicalBERT-zero-shot-finetuned-50cad
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1475
- Accuracy: 0.5
- F1: 0.6667
## Model description
More information needed
## Intended uses & limitations
... | [
"# Bio_ClinicalBERT-zero-shot-finetuned-50cad\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1475\n- Accuracy: 0.5\n- F1: 0.6667",
"## Model description\n\nMore information needed",
"## Intended u... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bio_ClinicalBERT-zero-shot-finetuned-50cad\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.\nIt ach... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.fr", "s... | RupE/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T04:38:49+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2880
* F1: 0.8151
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT-zero-shot-finetuned-50noncad
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://hu... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "Bio_ClinicalBERT-zero-shot-finetuned-50noncad", "results": []}]} | okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50noncad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T04:43:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Bio_ClinicalBERT-zero-shot-finetuned-50noncad
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8046
- Accuracy: 0.5
- F1: 0.0
## Model description
More information needed
## Intended uses & limitations
... | [
"# Bio_ClinicalBERT-zero-shot-finetuned-50noncad\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8046\n- Accuracy: 0.5\n- F1: 0.0",
"## Model description\n\nMore information needed",
"## Intended u... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bio_ClinicalBERT-zero-shot-finetuned-50noncad\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.\nIt ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.it", "s... | RupE/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T04:44:03+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3355
* F1: 0.7435
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-generation | transformers |
# America DialoGPT Model | {"tags": ["conversational"]} | throwaway112358112358/DialoGPT-medium-script | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T04:46:26+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# America DialoGPT Model | [
"# America DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# America DialoGPT Model"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.en", "s... | RupE/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T04:47:33+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6380
* F1: 0.5542
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | RupE/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T04:50:43+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1748
* F1: 0.8467
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n*... |
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... | igpaub/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T06:19:05+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 ... |
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. -->
# distilbert_new2_0040
This model is a fine-tuned version of [/content/drive/MyDrive/Colab Notebooks/oscar/trybackup_distilbert/new_back... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_new2_0040", "results": []}]} | bigmorning/distilbert_new2_0040 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T07:50:33+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_new2\_0040
======================
This model is a fine-tuned version of /content/drive/MyDrive/Colab Notebooks/oscar/trybackup\_distilbert/new\_backup\_0105105 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9702
* Validation Loss: 0.9482
* Epoch: 39
Mod... | [
"### 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 #distilbert #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': 2e-05, 'decay': 0.... |
null | null | ## Description
This model is a ViT trained to classify waste images into 6 categories:
- Organic
- Carton
- Glass
- General
- Plastics
- Dangerous.
The repository related to this model is: https://github.com/hectorLop/Waste-Detector
Also, the code related to this model can be found here https://github.com/hectorLop... | {"language": "en"} | hlopez/ViT_waste_classifier | null | [
"en",
"region:us"
] | null | 2022-07-22T07:57:04+00:00 | [] | [
"en"
] | TAGS
#en #region-us
| ## Description
This model is a ViT trained to classify waste images into 6 categories:
- Organic
- Carton
- Glass
- General
- Plastics
- Dangerous.
The repository related to this model is: URL
Also, the code related to this model can be found here URL
### Requirements
- Works with RGB images of size 224x224
| [
"## Description\nThis model is a ViT trained to classify waste images into 6 categories: \n- Organic\n- Carton\n- Glass\n- General\n- Plastics\n- Dangerous.\n\nThe repository related to this model is: URL \nAlso, the code related to this model can be found here URL",
"### Requirements\n- Works with RGB images of... | [
"TAGS\n#en #region-us \n",
"## Description\nThis model is a ViT trained to classify waste images into 6 categories: \n- Organic\n- Carton\n- Glass\n- General\n- Plastics\n- Dangerous.\n\nThe repository related to this model is: URL \nAlso, the code related to this model can be found here URL",
"### Requirement... |
null | null | ## Description
This model is a ViT trained to detect waste from images.
The repository related to this model is: https://github.com/hectorLop/Waste-Detector
This model was created using [Icevision](https://github.com/airctic/icevision), and all the code related to the training can be found here https://github.com/he... | {"language": "en"} | hlopez/EfficientDet_waste_detector | null | [
"en",
"region:us"
] | null | 2022-07-22T08:06:32+00:00 | [] | [
"en"
] | TAGS
#en #region-us
| ## Description
This model is a ViT trained to detect waste from images.
The repository related to this model is: URL
This model was created using Icevision, and all the code related to the training can be found here URL
### Requirements
- It is an EffientDet D1 model
- Works with RBG images of size 512x512 | [
"## Description\nThis model is a ViT trained to detect waste from images.\n\nThe repository related to this model is: URL \nThis model was created using Icevision, and all the code related to the training can be found here URL",
"### Requirements\n- It is an EffientDet D1 model \n- Works with RBG images of size ... | [
"TAGS\n#en #region-us \n",
"## Description\nThis model is a ViT trained to detect waste from images.\n\nThe repository related to this model is: URL \nThis model was created using Icevision, and all the code related to the training can be found here URL",
"### Requirements\n- It is an EffientDet D1 model \n- W... |
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_new2_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_new2_0060", "results": []}]} | bigmorning/distilgpt_new2_0060 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T08:48:42+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new2\_0060
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5626
* Validation Loss: 2.4481
* 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 | IndicBERTv2-alpha
| {} | ai4bharat/IndicBERTv2-alpha | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:00:40+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| IndicBERTv2-alpha
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-d-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/asahi417/... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-d-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-mask-prompt-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:35:02+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-d-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, f... | [
"# relbert/roberta-large-semeval2012-mask-prompt-d-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 Question ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine-tu... |
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/1306571874000830464/AZtk... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/thenextweb | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-22T09:35:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
TNW
@thenextweb
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-a-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/asahi417/... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-a-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-mask-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:37:07+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-a-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, f... | [
"# relbert/roberta-large-semeval2012-mask-prompt-a-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 Question ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine-tu... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-b-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/asahi417/... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-b-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-mask-prompt-b-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:39:36+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-b-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, f... | [
"# relbert/roberta-large-semeval2012-mask-prompt-b-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 Question ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine-tu... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-prompt-c-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/asahi417/... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-prompt-c-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-mask-prompt-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:41:42+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-prompt-c-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, f... | [
"# relbert/roberta-large-semeval2012-mask-prompt-c-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 Question ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine-tu... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-mask-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/asahi417/... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-mask-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-mask-prompt-e-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:43:51+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-mask-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, f... | [
"# relbert/roberta-large-semeval2012-mask-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 Question ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-mask-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine-tu... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-d-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-d-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-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:45:54+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-d-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-d-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-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-a-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-a-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-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:47:57+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-a-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-a-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-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-b-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-b-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-b-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:51:01+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-b-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-b-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-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-prompt-c-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-c-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-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:53:26+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-c-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-c-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-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity.\nFine... |
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. -->
# FAICAM/distilled-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_keras_callback"], "model-index": [{"name": "FAICAM/distilled-finetuned-imdb", "results": []}]} | FAICAM/distilled-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:53:28+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| FAICAM/distilled-finetuned-imdb
===============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8612
* Validation Loss: 2.5836
* Epoch: 0
Model description
-----------------
More informa... | [
"### 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 #distilbert #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'... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-d-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.co... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-d-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:55:31+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-d-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 ... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-d-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- Analog... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarit... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-a-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.co... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-a-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:57:35+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-a-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 ... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-a-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- Analog... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarit... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-b-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.co... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-b-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T09:59:39+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-b-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 ... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-b-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- Analog... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarit... |
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. -->
# exper1_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-p... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper1_mesum5", "results": []}]} | sudo-s/exper1_mesum5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:00:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper1\_mesum5
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem5 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6401
* Accuracy: 0.8278
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 4\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\... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-prompt-c-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.co... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:02:11+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-prompt-c-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 ... | [
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-c-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- Analog... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarit... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-average-no-mask-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.co... | {"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_... | research-backup/roberta-large-semeval2012-average-no-mask-prompt-e-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:04:15+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-average-no-mask-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 ... | [
"# relbert/roberta-large-semeval2012-average-no-mask-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- Analog... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarit... |
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": "Reinforce-Pong-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{... | auriolar/Reinforce-Pong-PLE-v0 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-22T10:07:41+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... |
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"]} | Chris1/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-22T10:08:37+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
sentence-similarity | sentence-transformers |
# ronanki/all-mpnet-base-v2-2022-07-18_15-29-33
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)
Us... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ronanki/all-mpnet-base-v2-2022-07-18_15-29-33 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:11:51+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# ronanki/all-mpnet-base-v2-2022-07-18_15-29-33
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-transfor... | [
"# ronanki/all-mpnet-base-v2-2022-07-18_15-29-33\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 sentenc... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# ronanki/all-mpnet-base-v2-2022-07-18_15-29-33\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks l... |
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. -->
# exper2_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-p... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper2_mesum5", "results": []}]} | sudo-s/exper2_mesum5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:15:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper2\_mesum5
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem5 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4589
* Accuracy: 0.1308
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\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: 4\n* mixed\\_preci... | [
"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: 0.002\n* train\\_batch\\... |
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. -->
# exper3_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-p... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper3_mesum5", "results": []}]} | sudo-s/exper3_mesum5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:30:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper3\_mesum5
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem5 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6366
* Accuracy: 0.8367
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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\... |
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... | igpaub/a2c-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T10:31:42+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... |
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... | sun1638650145/A2C-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-22T10:36:17+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 ... |
null | null |
# 3 Label Ventricular Segmentation
This network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although much of the training data is composed of ... | {"tags": ["MONAI"]} | dnouri/ventricular_short_axis_3label | null | [
"MONAI",
"region:us"
] | null | 2022-07-22T10:38:50+00:00 | [] | [] | TAGS
#MONAI #region-us
|
# 3 Label Ventricular Segmentation
This network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although much of the training data is composed of ... | [
"# 3 Label Ventricular Segmentation\n\nThis network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although much of the training data is compos... | [
"TAGS\n#MONAI #region-us \n",
"# 3 Label Ventricular Segmentation\n\nThis network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although muc... |
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. -->
# exper4_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-p... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper4_mesum5", "results": []}]} | sudo-s/exper4_mesum5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:45:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper4\_mesum5
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem5 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4389
* Accuracy: 0.1331
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_preci... | [
"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: 2e-05\n* train\\_batch\\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1166543171
- CO2 Emissions (in grams): 0.07308302140406821
## Validation Metrics
- Loss: 0.2211569994688034
- Accuracy: 0.9138
- Precision: 0.9020598523124758
- Recall: 0.9284
- AUC: 0.9711116000000001
- F1: 0.9150404100137985
## Usa... | {"language": "en", "tags": "autotrain", "datasets": ["ameerazam08/autotrain-data-imdb"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.07308302140406821} | ameerazam08/autotrain-imdb-1166543171 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:ameerazam08/autotrain-data-imdb",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T10:46:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-ameerazam08/autotrain-data-imdb #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1166543171
- CO2 Emissions (in grams): 0.07308302140406821
## Validation Metrics
- Loss: 0.2211569994688034
- Accuracy: 0.9138
- Precision: 0.9020598523124758
- Recall: 0.9284
- AUC: 0.9711116000000001
- F1: 0.9150404100137985
## Usa... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1166543171\n- CO2 Emissions (in grams): 0.07308302140406821",
"## Validation Metrics\n\n- Loss: 0.2211569994688034\n- Accuracy: 0.9138\n- Precision: 0.9020598523124758\n- Recall: 0.9284\n- AUC: 0.9711116000000001\n- F1: 0.91504... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-ameerazam08/autotrain-data-imdb #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1166543171\n- CO2 Emissions (i... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-dutch-V2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-dutch-V2", "results": []}]} | mhaegeman/wav2vec2-large-xls-r-300m-dutch | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T11:00:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-dutch-V2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4262
- eval_wer: 0.3052
- eval_runtime: 8417.9087
- eval_samples_per_second: 0.678
- eval_steps_per_second: 0.0... | [
"# wav2vec2-large-xls-r-300m-dutch-V2\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4262\n- eval_wer: 0.3052\n- eval_runtime: 8417.9087\n- eval_samples_per_second: 0.678\n- eval_steps_per_s... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-dutch-V2\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1166543179
- CO2 Emissions (in grams): 0.4999789160111311
## Validation Metrics
- Loss: 0.19526566565036774
- Accuracy: 0.9418
- Precision: 0.9441093687173301
- Recall: 0.9392
- AUC: 0.9824502399999999
- F1: 0.9416482855424103
## Usa... | {"language": "en", "tags": "autotrain", "datasets": ["ameerazam08/autotrain-data-imdb"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.4999789160111311} | ameerazam08/autotrain-imdb-1166543179 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:ameerazam08/autotrain-data-imdb",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T11:10:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-ameerazam08/autotrain-data-imdb #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1166543179
- CO2 Emissions (in grams): 0.4999789160111311
## Validation Metrics
- Loss: 0.19526566565036774
- Accuracy: 0.9418
- Precision: 0.9441093687173301
- Recall: 0.9392
- AUC: 0.9824502399999999
- F1: 0.9416482855424103
## Usa... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1166543179\n- CO2 Emissions (in grams): 0.4999789160111311",
"## Validation Metrics\n\n- Loss: 0.19526566565036774\n- Accuracy: 0.9418\n- Precision: 0.9441093687173301\n- Recall: 0.9392\n- AUC: 0.9824502399999999\n- F1: 0.94164... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-ameerazam08/autotrain-data-imdb #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1166543179\n- CO2 Emissions (in g... |
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. -->
# exper6_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-p... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper6_mesum5", "results": []}]} | sudo-s/exper6_mesum5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T11:16:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper6\_mesum5
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem5 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8241
* Accuracy: 0.8036
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 16\n* mixed\\_pre... | [
"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\... |
automatic-speech-recognition | transformers |
# wav2vec2-base-german-cv9
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1742
- Wer: 0.1209
## Model description
More informati... | {"language": ["de"], "license": "mit", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_9_0", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_9_0"], "model-index": [{"name": "wav2vec2-base-german-cv9", "results": [{"task": {"type": "automatic-speech-recognition", "name"... | oliverguhr/wav2vec2-base-german-cv9 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"de",
"dataset:mozilla-foundation/common_voice_9_0",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T11:44:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_9_0 #generated_from_trainer #de #dataset-mozilla-foundation/common_voice_9_0 #license-mit #model-index #endpoints_compatible #region-us
| wav2vec2-base-german-cv9
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the MOZILLA-FOUNDATION/COMMON\_VOICE\_9\_0 - DE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1742
* Wer: 0.1209
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_9_0 #generated_from_trainer #de #dataset-mozilla-foundation/common_voice_9_0 #license-mit #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe follo... |
question-answering | transformers |
# deberta-v3-base for QA
This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Ov... | {"language": "en", "license": "cc-by-4.0", "tags": ["deberta", "deberta-v3"], "datasets": ["squad_v2"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "deepset/deberta-v3-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2",... | deepset/deberta-v3-base-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"question-answering",
"deberta",
"deberta-v3",
"en",
"dataset:squad_v2",
"base_model:microsoft/deberta-v3-base",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-22T11:54:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #base_model-microsoft/deberta-v3-base #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
|
# deberta-v3-base for QA
This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Overview
Language model: deberta-v3-base
Language: English
Downstream-task: Extractive QA
Tr... | [
"# deberta-v3-base for QA \n\nThis is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.",
"## Overview\nLanguage model: deberta-v3-base \nLanguage: English \nDownstream-task: Extract... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #question-answering #deberta #deberta-v3 #en #dataset-squad_v2 #base_model-microsoft/deberta-v3-base #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# deberta-v3-base for QA \n\nThis is the deberta-v3-base model, fine-tuned u... |
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. -->
# distilbert_new2_0060
This model is a fine-tuned version of [/content/drive/MyDrive/Colab Notebooks/oscar/trybackup_distilbert/new_back... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_new2_0060", "results": []}]} | bigmorning/distilbert_new2_0060 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T12:17:27+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_new2\_0060
======================
This model is a fine-tuned version of /content/drive/MyDrive/Colab Notebooks/oscar/trybackup\_distilbert/new\_backup\_0105105 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9522
* Validation Loss: 0.9345
* Epoch: 59
Mod... | [
"### 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 #distilbert #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': 2e-05, 'decay': 0.... |
text-classification | transformers | # IndicXLMv2-alpha-SentimentClassification
| {} | ai4bharat/IndicBERTv2-alpha-SentimentClassification | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T12:29:28+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # IndicXLMv2-alpha-SentimentClassification
| [
"# IndicXLMv2-alpha-SentimentClassification"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# IndicXLMv2-alpha-SentimentClassification"
] |
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"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | d4niel92/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T12:39:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #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: 0.2259
* Accuracy: 0.924
* F1: 0.9238
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: 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 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
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. -->
# exper7_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-p... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper7_mesum5", "results": []}]} | sudo-s/exper7_mesum5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T12:42:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper7\_mesum5
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem5 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5889
* Accuracy: 0.8538
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"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: 0.0001\n* train\\_batch\... |
token-classification | transformers | # IndicXLMv2-alpha-POS-tagging
| {} | ai4bharat/IndicBERTv2-alpha-POS-tagging | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-22T12:46:31+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # IndicXLMv2-alpha-POS-tagging
| [
"# IndicXLMv2-alpha-POS-tagging"
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# IndicXLMv2-alpha-POS-tagging"
] |
null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
| {"license": "mit", "title": "\ud83d\ude4bNLP QA Text Context Gradio\ud83d\udc69\u200d\u2695\ufe0f", "emoji": "\ud83d\udc69\u200d\u2695\ufe0f\ud83d\ude4b\ud83d\udcd1", "colorFrom": "purple", "colorTo": "green", "sdk": "gradio", "sdk_version": "3.0.5", "app_file": "app.py", "pinned": false} | Desh/SOTA | null | [
"license:mit",
"region:us"
] | null | 2022-07-22T12:55:47+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Check out the configuration reference at URL
| [] | [
"TAGS\n#license-mit #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. -->
# ddpm-ema-flower-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hugg... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/flowers-102-categories", "metrics": []} | mrm8488/ddpm-ema-flower-64 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/flowers-102-categories",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-22T13:16:29+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/flowers-102-categories #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-flower-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/flowers-102-categories' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training dat... | [
"# ddpm-ema-flower-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/flowers-102-categories' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediati... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/flowers-102-categories #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-flower-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/flowers-102-categories' dataset.",
"## ... |
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