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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | JS2498/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-24T18:16:58+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-language-detection-new
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-base-finetuned-language-identification", "results": []}]} | dinalzein/xlm-roberta-base-finetuned-language-identification | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-24T18:22:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-language-detection-new
=================================================
This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0436
* Accuracy: 0.9959
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #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* trai... |
null | null | Creado para tener datos creados de un texto | {"license": "apache-2.0"} | Joleo/nlp-basado-en-otro-no-original | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-05-24T18:22:36+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Creado para tener datos creados de un texto | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | DaveMSE/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T18:53:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0669
* Precision: 0.9333
* Recall: 0.9495
* F1: 0.9414
* Accuracy: 0.9857
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
image-classification | transformers |
# PANDA_ConvNeXT
An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
Example Images (1152,1152,3) 36 WSI patches:
ISUP 0:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolv... | {"tags": ["image-classification", "pytorch"], "metrics": ["accuracy", "Cohen's Kappa"]} | smc/PANDA_ConvNeXT | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T19:30:32+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# PANDA_ConvNeXT
An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
Example Images (1152,1152,3) 36 WSI patches:
ISUP 0:
<img width="256" height="256" src="URL
ISUP 1:
<img width="256" height="256" ... | [
"# PANDA_ConvNeXT\n\nAn attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input\n\nExample Images (1152,1152,3) 36 WSI patches: \n\n\n\nISUP 0:\n<img width=\"256\" height=\"256\" src=\"URL\n\nISUP 1:\n<img widt... | [
"TAGS\n#transformers #pytorch #convnext #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# PANDA_ConvNeXT\n\nAn attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as... |
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="jabot/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | jabot/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-24T19:40:14+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="jabot/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | jabot/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-24T19:44:57+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null |
# NoiceGAN
This project is aimed to understand the scope of noise generator models trained with GAN architecture. The *noise* in this project refers to the output of the generator, which is then added to the samples before given to the discriminator to classify. One of the target goals to hit in this project is ident... | {"license": "mit"} | egesko/NoiceGAN | null | [
"license:mit",
"region:us"
] | null | 2022-05-24T20:00:30+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# NoiceGAN
This project is aimed to understand the scope of noise generator models trained with GAN architecture. The *noise* in this project refers to the output of the generator, which is then added to the samples before given to the discriminator to classify. One of the target goals to hit in this project is ident... | [
"# NoiceGAN\n\nThis project is aimed to understand the scope of noise generator models trained with GAN architecture. The *noise* in this project refers to the output of the generator, which is then added to the samples before given to the discriminator to classify. One of the target goals to hit in this project is... | [
"TAGS\n#license-mit #region-us \n",
"# NoiceGAN\n\nThis project is aimed to understand the scope of noise generator models trained with GAN architecture. The *noise* in this project refers to the output of the generator, which is then added to the samples before given to the discriminator to classify. One of the ... |
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... | pva/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-24T20:40:07+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... |
token-classification | transformers |
`clinitokenizer` is a sentence tokenizer for clinical text to split unstructured text from clinical text (such as Electronic Medical Records) into individual sentences.
To use this model, see the [clinitokenizer repository](https://github.com/clinisift/clinitokenizer).
General English sentence tokenizers are often... | {"license": "apache-2.0"} | samrawal/medical-sentence-tokenizer | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-24T21:05:09+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
'clinitokenizer' is a sentence tokenizer for clinical text to split unstructured text from clinical text (such as Electronic Medical Records) into individual sentences.
To use this model, see the clinitokenizer repository.
General English sentence tokenizers are often unable to correctly parse medical abbreviation... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Rick DialogGPT Model | {"tags": ["conversational"]} | ulises801/DialoGPT-medium-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-24T21:21:16+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialogGPT Model | [
"# Rick DialogGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialogGPT Model"
] |
text2text-generation | transformers |
```
```
[](https://paperswithcode.com/sota/code-generation-on-conala?p=mariancg-a-code-generation-transformer-model)
```
```
# MarianCG: a code generation transformer... | {"widget": [{"text": "create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]"}, {"text": "check if all elements in list `mylist` are identical"}, {"text": "enable debug mode on flask application `app`"}, {"text": "getting the length of `my_tuple`"}, {"text": "find all... | AhmedSSoliman/MarianCG-CoNaLa-Large | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-24T21:50:16+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
![PWC](URL
# MarianCG: a code generation transformer model inspired by machine translation
This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that c... | [
"# MarianCG: a code generation transformer model inspired by machine translation\nThis model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be abl... | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MarianCG: a code generation transformer model inspired by machine translation\nThis model is to improve the solving of the code generation problem and implement a tran... |
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. -->
# xlsr-wav2vec2-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlsr-wav2vec2-2", "results": []}]} | chrisvinsen/xlsr-wav2vec2-2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T23:04:01+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xlsr-wav2vec2-2
===============
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5884
* Wer: 0.4301
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_ba... |
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="micheljperez/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | micheljperez/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-24T23:16:33+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="micheljperez/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False et... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | micheljperez/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-24T23:24:34+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | nobuotto/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T23:43:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4734
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
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... | nateraw/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"tensorboard",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T00:27:16+00:00 | [] | [] | TAGS
#stable-baselines3 #tensorboard #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 #tensorboard #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)\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... | egypationbill/RL_W1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T00:57:15+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... |
null | transformers | Pretrained ELECTRA Language Model for Korean by bigwaveAI (bw-electra-base-discriminator)
### Usage
## Load Model and Tokenizer
```python
from transformers import ElectraModel,TFElectraModel,ElectraTokenizer
# tensorflow
model = TFElectraModel.from_pretrained("ifuseok/bw-electra-base-discriminator")
# torch
#model ... | {} | ifuseok/bw-electra-base-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T01:15:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us
| Pretrained ELECTRA Language Model for Korean by bigwaveAI (bw-electra-base-discriminator)
### Usage
## Load Model and Tokenizer
## Tokenizer example
## Example using ElectraForPreTraining(Torch)
## Example using ElectraForPreTraining(Tensorflow)
| [
"### Usage",
"## Load Model and Tokenizer",
"## Tokenizer example",
"## Example using ElectraForPreTraining(Torch)",
"## Example using ElectraForPreTraining(Tensorflow)"
] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us \n",
"### Usage",
"## Load Model and Tokenizer",
"## Tokenizer example",
"## Example using ElectraForPreTraining(Torch)",
"## Example using ElectraForPreTraining(Tensorflow)"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Lordli/bert-finetuned-ner
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Lordli/bert-finetuned-ner", "results": []}]} | Lordli/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T01:51:11+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Lordli/bert-finetuned-ner
=========================
This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0613
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7039, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["precision", "recall"], "model-index": [{"name": "bert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emo... | mehnaazasad/bert-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T01:52:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-emotion
============
This model is a fine-tuned version of distilbert-base-cased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2037
* Precision: 0.9391
* Recall: 0.9190
* Fscore: 0.9278
Model description
-----------------
More information needed
Intended u... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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* le... |
text-classification | transformers |
## Training Parameters
```
learning rate: 2e-5
epochs: 40
weight decay: 0.01
batch size: 16
```
## Metrics
```
acuraccy: 0.93
macro-F1 (macro avg): 0.88
best epoch: 15
```
## Dataset:
[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
| {"language": "en", "license": "apache-2.0", "tags": ["text-classification", "pytorch", "emotion"], "metrics": ["accuracy, F1 score"], "dataset": ["emotion"]} | sbenel/emotion-distilbert | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"emotion",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-25T02:00:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #emotion #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Training Parameters
## Metrics
## Dataset:
Twitter-Sentiment-Analysis.
| [
"## Training Parameters",
"## Metrics",
"## Dataset:\nTwitter-Sentiment-Analysis."
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #emotion #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Training Parameters",
"## Metrics",
"## Dataset:\nTwitter-Sentiment-Analysis."
] |
text-to-speech | fairseq | ## fastspeech2-freeman | {"language": "en", "license": "gpl-3.0", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech", "multi-speaker"], "task": "text-to-speech", "widget": [{"text": "Hello stranger! I am happy to meet you", "example_title": "Hi"}]} | Voicemod/fastspeech2-mf | null | [
"fairseq",
"audio",
"text-to-speech",
"multi-speaker",
"en",
"license:gpl-3.0",
"region:us"
] | null | 2022-05-25T02:17:21+00:00 | [] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #multi-speaker #en #license-gpl-3.0 #region-us
| ## fastspeech2-freeman | [
"## fastspeech2-freeman"
] | [
"TAGS\n#fairseq #audio #text-to-speech #multi-speaker #en #license-gpl-3.0 #region-us \n",
"## fastspeech2-freeman"
] |
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="bguan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | bguan/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T02:44:32+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 | 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... | bhaswara/Test1ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T02:59:03+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-hindi-new
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-hindi-new", "results": []}]} | morahil/wav2vec2-hindi-new | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T04:13:01+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-hindi-new
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# wav2vec2-hindi-new\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-hindi-new\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nMore information ... |
fill-mask | transformers |
We are releasing the first BERT model trained on monolingual text for Nepali. Please refer our paper [NPVec1: Word Embeddings for Nepali - Construction and Evaluation](https://aclanthology.org/2021.repl4nlp-1.18.pdf) to get details on its construction and evaluation. | {"license": "apache-2.0"} | nowalab/nepali-bert-npvec1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T04:40:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
We are releasing the first BERT model trained on monolingual text for Nepali. Please refer our paper NPVec1: Word Embeddings for Nepali - Construction and Evaluation to get details on its construction and evaluation. | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikian... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikia... | Ravindra001/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T05:09:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3217
* Precision: 0.8196
* Recall: 0.8445
* F1: 0.8319
* Accuracy: 0.9269
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #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\\... |
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="shivigupta/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"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": ... | shivigupta/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T05:13:58+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="shivigupta/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | shivigupta/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T05:28:20+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"
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"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"
] |
text2text-generation | transformers | # rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: `text2text generation`
* Type: `encoder-decoder`
* Tokenizer: `bpe`
* Dict size: `32 101`
* Num Parameters: `222 M`
| {"language": ["ru"], "tags": ["PyTorch", "Transformers"]} | orzhan/rut5-base-detox-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T05:51:41+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101'
* Num Parameters: '222 M'
| [
"# rut5-base-detox-v2\nModel was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.\n* Task: 'text2text generation'\n* Type: 'encoder-decoder'\n* Tokenizer: 'bpe'\n* Dict size: '32 101'\n* Num Parameters: '222 M'"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# rut5-base-detox-v2\nModel was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.\n* Task: 'text2text generat... |
null | null | Best thirumana porutham online |Astrothoughts
Astro Thoughts is one of the Best online Astrologer in Chennai. Shri.Nallakutalam is an expert in the field of astrology. For Online Consult Call us:8879798701
https://astrothoughts.in/ | {} | astrothoughts/Bestthirumanaporuthamonline | null | [
"region:us"
] | null | 2022-05-25T05:51:42+00:00 | [] | [] | TAGS
#region-us
| Best thirumana porutham online |Astrothoughts
Astro Thoughts is one of the Best online Astrologer in Chennai. Shri.Nallakutalam is an expert in the field of astrology. For Online Consult Call us:8879798701
URL | [] | [
"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="SimingSiming/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "met... | SimingSiming/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T06:17:02+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #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-v1-8x8 #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"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | Mesablip/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-05-25T06:20:09+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (2 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (2 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2 labels for 1 components)",
"### Accuracy"
] |
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="SimingSiming/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False et... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | SimingSiming/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T07:05:50+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"
] |
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="Rai220/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "F... | Rai220/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T07:12:22+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"
] |
fill-mask | transformers |
# CoReNer
## Demo
We released an online demo so you can easily play with the model. Check it out: [http://corener-demo.aiola-lab.com](http://corener-demo.aiola-lab.com).
The demo uses the [aiola/roberta-base-corener](https://huggingface.co/aiola/roberta-base-corener) model.
## Model description
A multi-task model... | {"language": ["en"], "license": "afl-3.0", "tags": ["NER", "named entity recognition", "RE", "relation extraction", "entity mention detection", "EMD", "coreference resolution"], "datasets": ["Ontonotes", "CoNLL04"]} | aiola/roberta-large-corener | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"NER",
"named entity recognition",
"RE",
"relation extraction",
"entity mention detection",
"EMD",
"coreference resolution",
"en",
"dataset:Ontonotes",
"dataset:CoNLL04",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatib... | null | 2022-05-25T07:13:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #NER #named entity recognition #RE #relation extraction #entity mention detection #EMD #coreference resolution #en #dataset-Ontonotes #dataset-CoNLL04 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CoReNer
## Demo
We released an online demo so you can easily play with the model. Check it out: URL.
The demo uses the aiola/roberta-base-corener model.
## Model description
A multi-task model for named-entity recognition, relation extraction, entity mention detection, and coreference resolution.
We model NER ... | [
"# CoReNer",
"## Demo\n\nWe released an online demo so you can easily play with the model. Check it out: URL. \nThe demo uses the aiola/roberta-base-corener model.",
"## Model description\n\nA multi-task model for named-entity recognition, relation extraction, entity mention detection, and coreference resolutio... | [
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"# CoReNer",
"## Demo\n... |
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="Rai220/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Rai220/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T07:18:53+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# opus-mt-ar-en-finetuned-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_infopankki"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ar-en-finetuned-ar-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "opus_infopankki",... | PontifexMaximus/ArabicTranslator | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T07:25:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ar-en-finetuned-ar-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on the opus\_infopankki dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7269
* Bleu: 51.6508
* Gen Len: 15.0812
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #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* ... |
text-to-speech | fairseq | # fastspeech2-en-200_speaker-cv4
[FastSpeech 2](https://arxiv.org/abs/2006.04558) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- English
- 200 male/female voices (random speaker when using the widget)
-... | {"language": "en", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech", "multi-speaker"], "datasets": ["common_voice"], "task": "text-to-speech", "widget": [{"text": "Hello, this is a test run.", "example_title": "Hello, this is a test run."}]} | Voicemod/fastspeech2-en-male1 | null | [
"fairseq",
"audio",
"text-to-speech",
"multi-speaker",
"en",
"dataset:common_voice",
"arxiv:2006.04558",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-05-25T07:28:31+00:00 | [
"2006.04558",
"2109.06912"
] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #multi-speaker #en #dataset-common_voice #arxiv-2006.04558 #arxiv-2109.06912 #has_space #region-us
| # fastspeech2-en-200_speaker-cv4
FastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):
- English
- 200 male/female voices (random speaker when using the widget)
- Trained on Common Voice v4
## Usage
See also fairseq S^2 example.
| [
"# fastspeech2-en-200_speaker-cv4\n\nFastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):\n- English\n- 200 male/female voices (random speaker when using the widget)\n- Trained on Common Voice v4",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
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"# fastspeech2-en-200_speaker-cv4\n\nFastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):\n- English\n- 200 male/female voices (random speaker when using the w... |
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-hindi-new-3
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-hindi-new-3", "results": []}]} | morahil/wav2vec2-hindi-new-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T07:37:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-hindi-new-3
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.1206
- eval_wer: 0.8949
- eval_runtime: 20.2358
- eval_samples_per_second: 19.767
- eval_steps_per_second: 2.471
- epoch: 25.8
- s... | [
"# wav2vec2-hindi-new-3\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 2.1206\n- eval_wer: 0.8949\n- eval_runtime: 20.2358\n- eval_samples_per_second: 19.767\n- eval_steps_per_second: 2.471\n- epoc... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-hindi-new-3\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.\nIt achieves the following resu... |
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="XGBooster/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": ... | XGBooster/q-FrozenLake-v1-8x8-noSlippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T07:43:38+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-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-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- 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-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e16
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e16", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e16 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T07:50:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e16
===============================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8960
* Rouge1: 57.7198
* Rouge2: 44.57... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | 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="XGBooster/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | XGBooster/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T08:14:03+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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | leander/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T08:36:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0589
* Precision: 0.9329
* Recall: 0.9507
* F1: 0.9417
* Accuracy: 0.9870
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
# GPTPoet: Pre-training GPT2 for Arabic Poetry Language Understanding
<img src="https://huggingface.co/usama98/arabic_poem_gen/resolve/main/6C76C5D6-A4F2-4443-AB2A-278E87B8E33C.png" width="100" align="left"/>
**GPTPoet** is an Arabic pretrained language model based on [OpenAi GPT2 architechture](https://github.com/o... | {"language": ["ar"], "license": "apache-2.0", "tags": ["text-generation"], "datasets": ["Arabic Poem Comprehensive Dataset (APCD)"], "widget": [{"text": "\u0639\u0645\u0631\u0648 \u0628\u0646\u0650 \u0642\u064f\u0645\u064e\u064a\u0626\u064e\u0629: \u062e\u064e\u0644\u064a\u0644\u064e\u064a\u0651\u064e \u0644\u0627 \u06... | usama98/arabic_poem_gen | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T08:40:56+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPTPoet: Pre-training GPT2 for Arabic Poetry Language Understanding
<img src="URL width="100" align="left"/>
GPTPoet is an Arabic pretrained language model based on OpenAi GPT2 architechture. We use the same GPT2-Base config. More details are available in the Google Colab [URL
To save computation time the model u... | [
"# GPTPoet: Pre-training GPT2 for Arabic Poetry Language Understanding\n\n<img src=\"URL width=\"100\" align=\"left\"/>\n\nGPTPoet is an Arabic pretrained language model based on OpenAi GPT2 architechture. We use the same GPT2-Base config. More details are available in the Google Colab [URL\n\nTo save computation t... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPTPoet: Pre-training GPT2 for Arabic Poetry Language Understanding\n\n<img src=\"URL width=\"100\" align=\"left\"/>\n\nGPTPoet is an Arabic pretra... |
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. -->
# binary-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "autoevaluate-binary-classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "m... | autoevaluate/binary-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T08:46:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| binary-classification
=====================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3009
* Accuracy: 0.8968
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
document-question-answering | null |
# LayoutLM for Visual Question Answering
This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on documents. It has been fine-tuned using both the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) datasets.... | {"language": "en", "license": "mit", "tags": ["layoutlm", "pdf"], "pipeline_tag": "document-question-answering"} | mishig/temp-model | null | [
"layoutlm",
"pdf",
"document-question-answering",
"en",
"license:mit",
"region:us"
] | null | 2022-05-25T08:59:02+00:00 | [] | [
"en"
] | TAGS
#layoutlm #pdf #document-question-answering #en #license-mit #region-us
|
# LayoutLM for Visual Question Answering
This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on documents. It has been fine-tuned using both the SQuAD2.0 and DocVQA datasets.
## Getting started with the model
To run these examples, you must have PIL, pytesseract, and Py... | [
"# LayoutLM for Visual Question Answering\n\nThis is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on documents. It has been fine-tuned using both the SQuAD2.0 and DocVQA datasets.",
"## Getting started with the model\n\nTo run these examples, you must have PIL, pytesse... | [
"TAGS\n#layoutlm #pdf #document-question-answering #en #license-mit #region-us \n",
"# LayoutLM for Visual Question Answering\n\nThis is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on documents. It has been fine-tuned using both the SQuAD2.0 and DocVQA datasets.",
"... |
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. -->
# outputs
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the cnn_daily... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "outputs", "results": []}]} | jimypbr/bart-large-test | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T09:04:33+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #bart #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# outputs
This model is a fine-tuned version of facebook/bart-large on the cnn_dailymail 3.0.0 dataset.
## Model description
More information needed
## Intended uses & limitations
This is a work in progress. Please don't use these weights.
## Training and evaluation data
More information needed
## Training p... | [
"# outputs\n\nThis model is a fine-tuned version of facebook/bart-large on the cnn_dailymail 3.0.0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nThis is a work in progress. Please don't use these weights.",
"## Training and evaluation data\n\nMore information... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #bart #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# outputs\n\nThis model is a fine-tuned version of facebook/bart-large on the cnn_dailymail 3.0.0 dataset.",
... |
text-classification | transformers | # Dataset: https://huggingface.co/datasets/xnli/viewer/vi/train
# Github: https://github.com/namlv97/vi-nli-xlm-roberta-base
```python
>>> import torch
>>> from transformers import AutoTokenizer,AutoModelForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
>>> model=AutoModelFor... | {} | nam7197/vi-nli-xlm-roberta-base | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T09:19:31+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Dataset: URL
============
Github: URL
===========
Performance
===========
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #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="ksmcg/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | ksmcg/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T09:39:45+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"
] |
null | transformers |
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | {"license": "mit", "tags": ["nowcasting", "forecasting", "timeseries", "remote-sensing"]} | openclimatefix/nowcasting_cnn_v2 | null | [
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T09:40:57+00:00 | [] | [] | TAGS
#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us
|
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | [
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes into many 3d convolution layers.\n 2. nwp time series goes into many 3d convolution layers.\n 3. Final convolutional layer ... | [
"TAGS\n#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us \n",
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes i... |
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="ksmcg/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | ksmcg/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T09:43:30+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"
] |
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... | dsavich/LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T09:44:10+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... |
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. -->
# tape-fluorescence-prediction-RITA_s
This model is a fine-tuned version of [lightonai/RITA_s](https://huggingface.co/lightonai/RI... | {"license": "apache-2.0", "tags": ["protein language model", "generated_from_trainer"], "datasets": ["train"], "metrics": ["spearmanr"], "model-index": [{"name": "tape-fluorescence-prediction-RITA_s", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "cradle-bio/ta... | thundaa/tape-fluorescence-prediction-RITA_s | null | [
"transformers",
"pytorch",
"rita",
"text-classification",
"protein language model",
"generated_from_trainer",
"custom_code",
"dataset:train",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"region:us"
] | null | 2022-05-25T09:59:12+00:00 | [] | [] | TAGS
#transformers #pytorch #rita #text-classification #protein language model #generated_from_trainer #custom_code #dataset-train #license-apache-2.0 #model-index #autotrain_compatible #region-us
| tape-fluorescence-prediction-RITA\_s
====================================
This model is a fine-tuned version of lightonai/RITA\_s on the cradle-bio/tape-fluorescence dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5855
* Spearmanr: 0.2955
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 128\n* total\\_train\\_batch\\_size: 4096\n* optimizer: Adam with betas=(0.9,0.999) and eps... | [
"TAGS\n#transformers #pytorch #rita #text-classification #protein language model #generated_from_trainer #custom_code #dataset-train #license-apache-2.0 #model-index #autotrain_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# entity-extraction
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003", "autoevaluate/conll2003-sample"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "entity-extraction", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": ... | autoevaluate/entity-extraction | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"dataset:autoevaluate/conll2003-sample",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-25T10:08:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #dataset-autoevaluate/conll2003-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| entity-extraction
=================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0808
* Precision: 0.8863
* Recall: 0.9085
* F1: 0.8972
* Accuracy: 0.9775
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #dataset-autoevaluate/conll2003-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hy... |
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. -->
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467... | {"language": ["en"], "tags": ["summarization"], "datasets": ["ccdv/WCEP-10"], "metrics": ["rouge"], "model-index": [{"name": "ccdv/lsg-bart-base-4096-wcep", "results": []}]} | ccdv/lsg-bart-base-4096-wcep | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"custom_code",
"en",
"dataset:ccdv/WCEP-10",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-05-25T10:09:11+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #custom_code #en #dataset-ccdv/WCEP-10 #arxiv-2210.15497 #autotrain_compatible #region-us
| Transformers >= 4.36.1
This model relies on a custom modeling file, you need to add trust\_remote\_code=True
See #13467
LSG ArXiv paper.
Github/conversion script is available at this link.
ccdv/lsg-bart-base-4096-wcep
============================
This model is a fine-tuned version of ccdv/lsg-bart-base-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_t... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #custom_code #en #dataset-ccdv/WCEP-10 #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size:... |
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. -->
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467... | {"language": ["en"], "tags": ["summarization"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "ccdv/lsg-bart-base-4096-multinews", "results": []}]} | ccdv/lsg-bart-base-4096-multinews | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"custom_code",
"en",
"dataset:multi_news",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-05-25T10:09:23+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #custom_code #en #dataset-multi_news #arxiv-2210.15497 #autotrain_compatible #region-us
| Transformers >= 4.36.1
This model relies on a custom modeling file, you need to add trust\_remote\_code=True
See #13467
LSG ArXiv paper.
Github/conversion script is available at this link.
ccdv/lsg-bart-base-4096-multinews
=================================
This model is a fine-tuned version of ccdv/lsg-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_t... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #custom_code #en #dataset-multi_news #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size: 8... |
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="arimboux/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": ... | arimboux/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T10:37:52+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="arimboux/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | arimboux/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T10:40:58+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"
] |
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="arimboux/q-Taxi-v4", 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-v4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | arimboux/q-Taxi-v4 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T10:50:42+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null |
erlich is the text2image latent diffusion model from CompVis (with additions from `glid-3-xl`) finetuned on a dataset collected from LAION-5B named Large Logo Dataset. It consists of roughly 100K images of logos with captions generated via BLIP using aggressive re-ranking.
Replicate versions:
[original](https://repl... | {"language": "en", "license": "mit", "tags": ["glid-3-xl", "latent-diffusion"]} | laion/erlich | null | [
"glid-3-xl",
"latent-diffusion",
"en",
"license:mit",
"region:us"
] | null | 2022-05-25T10:55:36+00:00 | [] | [
"en"
] | TAGS
#glid-3-xl #latent-diffusion #en #license-mit #region-us
|
erlich is the text2image latent diffusion model from CompVis (with additions from 'glid-3-xl') finetuned on a dataset collected from LAION-5B named Large Logo Dataset. It consists of roughly 100K images of logos with captions generated via BLIP using aggressive re-ranking.
Replicate versions:
original
latest | [] | [
"TAGS\n#glid-3-xl #latent-diffusion #en #license-mit #region-us \n"
] |
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. -->
# swadeshi_bhojpuriwav2vec2asr
This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "swadeshi_bhojpuriwav2vec2asr", "results": []}]} | pritam18/swadeshi_bhojpuriwav2vec2asr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T10:59:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| swadeshi\_bhojpuriwav2vec2asr
=============================
This model is a fine-tuned version of theainerd/Wav2Vec2-large-xlsr-hindi on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2155
* Wer: 0.2931
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
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 import... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"... | comodoro/ppo-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-25T11:10:20+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
sentence-similarity | sentence-transformers |
# ronanki/ml_use_512_MNR_15
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becom... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ronanki/ml_use_512_MNR_15 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T11:11:46+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# ronanki/ml_use_512_MNR_15
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
T... | [
"# ronanki/ml_use_512_MNR_15\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers insta... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# ronanki/ml_use_512_MNR_15\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering ... |
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="ThoDum/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | ThoDum/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-25T11:56:16+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"
] |
text-generation | transformers |
# Aeona | Chatbot
An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small).
Recommended to use along with an [AIML Chatbot](https://github.com/deepsarda/Aeona-Aiml) t... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://images-ext-2.discordapp.net/external/Wvtx1L98EbA7DR2lpZPbDxDuO4qmKt03nZygATZtXgk/%3Fsize%3D4096/https/cdn.discordapp.com/avatars/931226824753700934/338a9e413bbceaeb9095a29e97d4fac0.png"} | deepparag/Aeona-Beta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-25T12:43:39+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Aeona | Chatbot
===============
!Aeona Banner
An generative AI made using microsoft/DialoGPT-small.
Recommended to use along with an AIML Chatbot to reduce load, get better replies, add name and personality to your bot.
Using an AIML Chatbot will allow you to hardcode some replies also.
AEONA
=====
Aeona is a... | [
"#### Why not an AI on its own?\n\n\nFor AI it is not possible (realistically) to learn about the user and store data on them, when compared to an AIML which can even execute code!\nThe goal of the AI is to generate responses where the AIML fails.\n\n\nHence the goals becomes to make an AI which has a wide variety ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### Why not an AI on its own?\n\n\nFor AI it is not possible (realistically) to learn about the user and store data on them, when co... |
null | transformers | # SQuADv1 teacher
This model is used as a teacher for all runs on the SQuADv1 downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
SQuADv1 dev-set:
```
EM = 81.41
F1 = 88.54
```
Code: [https://github.com/neura... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-teacher-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:47:26+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # SQuADv1 teacher
This model is used as a teacher for all runs on the SQuADv1 downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
SQuADv1 dev-set:
Code: URL
If you find the model useful, please consider citing our work.
info
| [
"# SQuADv1 teacher\n\nThis model is used as a teacher for all runs on the SQuADv1 downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nSQuADv1 dev-set:\n\n\nCode: URL\n\nIf you find the model useful, please consider citing our work.\n\ninfo"... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# SQuADv1 teacher\n\nThis model is used as a teacher for all runs on the SQuADv1 downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Secon... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - SQuADv1 80%`.
```
Pruning m... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-80-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:53:16+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-80-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - SQuADv1 80%'.
The dev-set performance reported in the paper i... | [
"# oBERT-12-downstream-pruned-unstructured-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - SQuADv1 80%'.\n\n\n\nThe dev-set performance reported ... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large ... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-90-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - SQuADv1 90%`.
```
Pruning m... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-90-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:53:32+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-90-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - SQuADv1 90%'.
The dev-set performance reported in the paper i... | [
"# oBERT-12-downstream-pruned-unstructured-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - SQuADv1 90%'.\n\n\n\nThe dev-set performance reported ... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large ... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-97-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - SQuADv1 97%`.
```
Pruning m... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-97-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:53:49+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-97-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - SQuADv1 97%'.
The dev-set performance reported in the paper i... | [
"# oBERT-12-downstream-pruned-unstructured-97-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - SQuADv1 97%'.\n\n\n\nThe dev-set performance reported ... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-97-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large ... |
null | transformers | # MNLI teacher
This model is used as a teacher for all runs on the MNLI downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
MNLI dev-set:
```
matched accuracy = 84.54
mismatched accuracy = 85.06
```
Code: [ht... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-teacher-mnli | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:mnli",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:54:14+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # MNLI teacher
This model is used as a teacher for all runs on the MNLI downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
MNLI dev-set:
Code: URL
If you find the model useful, please consider citing our work.
info
| [
"# MNLI teacher\n\nThis model is used as a teacher for all runs on the MNLI downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nMNLI dev-set:\n\n\nCode: URL\n\nIf you find the model useful, please consider citing our work.\n\ninfo"
] | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# MNLI teacher\n\nThis model is used as a teacher for all runs on the MNLI downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-80-mnli
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - MNLI 80%`.
```
Pruning method:... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-80-mnli | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:mnli",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:54:40+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-80-mnli
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - MNLI 80%'.
The dev-set performance reported in the paper is aver... | [
"# oBERT-12-downstream-pruned-unstructured-80-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - MNLI 80%'.\n\n\n\nThe dev-set performance reported in the... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-80-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Lang... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-90-mnli
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - MNLI 90%`.
```
Pruning method:... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-90-mnli | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:mnli",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:54:55+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-90-mnli
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - MNLI 90%'.
The dev-set performance reported in the paper is aver... | [
"# oBERT-12-downstream-pruned-unstructured-90-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - MNLI 90%'.\n\n\n\nThe dev-set performance reported in the... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-90-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Lang... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-97-mnli
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - MNLI 97%`.
```
Pruning method:... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-97-mnli | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:mnli",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:55:09+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-97-mnli
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - MNLI 97%'.
The dev-set performance reported in the paper is aver... | [
"# oBERT-12-downstream-pruned-unstructured-97-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - MNLI 97%'.\n\n\n\nThe dev-set performance reported in the... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-97-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Lang... |
null | transformers | # QQP teacher
This model is used as a teacher for all runs on the QQP downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
QQP dev-set:
```
accuracy = 91.06
F1 = 88.00
```
Code: [https://github.com/neuralmagic... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-teacher-qqp | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:55:22+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # QQP teacher
This model is used as a teacher for all runs on the QQP downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
QQP dev-set:
Code: URL
If you find the model useful, please consider citing our work.
info
| [
"# QQP teacher\n\nThis model is used as a teacher for all runs on the QQP downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nQQP dev-set:\n\n\nCode: URL\n\nIf you find the model useful, please consider citing our work.\n\ninfo"
] | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# QQP teacher\n\nThis model is used as a teacher for all runs on the QQP downstream task in the paper The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pr... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-80-qqp
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - QQP 80%`.
```
Pruning method: o... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-80-qqp | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:55:37+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-80-qqp
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - QQP 80%'.
The dev-set performance reported in the paper is averag... | [
"# oBERT-12-downstream-pruned-unstructured-80-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - QQP 80%'.\n\n\n\nThe dev-set performance reported in the p... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-80-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Langua... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-90-qqp
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - QQP 90%`.
```
Pruning method: o... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-90-qqp | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:55:50+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-90-qqp
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - QQP 90%'.
The dev-set performance reported in the paper is averag... | [
"# oBERT-12-downstream-pruned-unstructured-90-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - QQP 90%'.\n\n\n\nThe dev-set performance reported in the p... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-90-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Langua... |
null | transformers | # oBERT-12-downstream-pruned-unstructured-97-qqp
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - QQP 97%`.
```
Pruning method: o... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-12-downstream-pruned-unstructured-97-qqp | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:56:04+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-unstructured-97-qqp
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - QQP 97%'.
The dev-set performance reported in the paper is averag... | [
"# oBERT-12-downstream-pruned-unstructured-97-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 1 - 30 Epochs - oBERT - QQP 97%'.\n\n\n\nThe dev-set performance reported in the p... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-unstructured-97-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Langua... |
null | transformers | # oBERT-12-upstream-pretrained-dense
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the pretrained dense model used as a teacher for upstream pruning runs, as described in the paper. The... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": ["bookcorpus", "wikipedia"]} | neuralmagic/oBERT-12-upstream-pretrained-dense | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:56:17+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pretrained-dense
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the pretrained dense model used as a teacher for upstream pruning runs, as described in the paper. The model can be finetuned on any downs... | [
"# oBERT-12-upstream-pretrained-dense\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the pretrained dense model used as a teacher for upstream pruning runs, as described in the paper. The model can be finetuned on... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pretrained-dense\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning fo... |
null | transformers | # oBERT-6-upstream-pretrained-dense
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to 6 layers from `neuralmagic/oBERT-12-upstream-pretrained-dense`, pretrained with knowledge distillation. ... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": ["bookcorpus", "wikipedia"]} | neuralmagic/oBERT-6-upstream-pretrained-dense | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:56:31+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-upstream-pretrained-dense
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to 6 layers from 'neuralmagic/oBERT-12-upstream-pretrained-dense', pretrained with knowledge distillation. This model is used as a starting poi... | [
"# oBERT-6-upstream-pretrained-dense\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to 6 layers from 'neuralmagic/oBERT-12-upstream-pretrained-dense', pretrained with knowledge distillation. This model is used as a sta... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-upstream-pretrained-dense\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for... |
null | transformers | # oBERT-3-upstream-pretrained-dense
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to 3 layers from `neuralmagic/oBERT-12-upstream-pretrained-dense`, pretrained with knowledge distillation. ... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": ["bookcorpus", "wikipedia"]} | neuralmagic/oBERT-3-upstream-pretrained-dense | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:56:43+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-upstream-pretrained-dense
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to 3 layers from 'neuralmagic/oBERT-12-upstream-pretrained-dense', pretrained with knowledge distillation. This model is used as a starting poi... | [
"# oBERT-3-upstream-pretrained-dense\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to 3 layers from 'neuralmagic/oBERT-12-upstream-pretrained-dense', pretrained with knowledge distillation. This model is used as a sta... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-upstream-pretrained-dense\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-90
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream ta... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": ["bookcorpus", "wikipedia"]} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:56:55+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'Table 2 - oBER... | [
"# oBERT-12-upstream-pruned-unstructured-90\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'Tabl... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-90\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Prun... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-97
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream ta... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": ["bookcorpus", "wikipedia"]} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97 | null | [
"transformers",
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"pruning",
"compression",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:57:16+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'Table 2 - oBER... | [
"# oBERT-12-upstream-pruned-unstructured-97\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the 'Tabl... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-97\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Prun... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - SQuADv1 90%`.
```
Pruning metho... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:57:34+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 90%'.
The dev-set performance reported in the paper is av... | [
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 90%'.\n\n\n\nThe dev-set performance reported in t... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning fo... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - SQuADv1 97%`.
```
Pruning metho... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:57:49+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 97%'.
The dev-set performance reported in the paper is av... | [
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - SQuADv1 97%'.\n\n\n\nThe dev-set performance reported in t... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning fo... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - MNLI 90%`.
```
Pruning method: oBE... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:mnli",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:58:03+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - MNLI 90%'.
The dev-set performance reported in the paper is averaged... | [
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - MNLI 90%'.\n\n\n\nThe dev-set performance reported in the pap... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for La... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - MNLI 97%`.
```
Pruning method: oBE... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "mnli"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:mnli",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:58:16+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - MNLI 97%'.
The dev-set performance reported in the paper is averaged... | [
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - MNLI 97%'.\n\n\n\nThe dev-set performance reported in the pap... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-mnli #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for La... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - QQP 90%`.
```
Pruning method: oBERT... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:58:30+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - QQP 90%'.
The dev-set performance reported in the paper is averaged o... | [
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - QQP 90%'.\n\n\n\nThe dev-set performance reported in the paper... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Larg... |
null | transformers | # oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - QQP 97%`.
```
Pruning method: oBERT... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "qqp"} | neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:qqp",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:58:41+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 2 - oBERT - QQP 97%'.
The dev-set performance reported in the paper is averaged o... | [
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 2 - oBERT - QQP 97%'.\n\n\n\nThe dev-set performance reported in the paper... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-qqp #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Larg... |
null | transformers | # oBERT-12-downstream-dense-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 12 Layers - 0% Sparsity`, and it represents an upper bound for per... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-dense-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:58:54+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-dense-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 12 Layers - 0% Sparsity', and it represents an upper bound for performance of the corresponding pruned... | [
"# oBERT-12-downstream-dense-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to the model presented in the 'Table 3 - 12 Layers - 0% Sparsity', and it represents an upper bound for performance of the correspondi... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-dense-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\... |
null | transformers | # oBERT-12-downstream-pruned-block4-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 12 Layers - Sparsity 80% - 4-block`.
```
Pruning meth... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-pruned-block4-80-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:59:08+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-block4-80-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 80% - 4-block'.
The dev-set performance of this model:
Code: URL... | [
"# oBERT-12-downstream-pruned-block4-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 80% - 4-block'.\n\n\n\nThe dev-set performance of this mode... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-block4-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Langua... |
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-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-colab", "results": []}]} | vai6hav/wav2vec2-large-xls-r-300m-hindi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:59:18+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-hindi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# wav2vec2-large-xls-r-300m-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"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-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_vo... |
null | transformers | # oBERT-12-downstream-pruned-block4-90-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 12 Layers - Sparsity 90% - 4-block`.
```
Pruning meth... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-12-downstream-pruned-block4-90-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:59:21+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-12-downstream-pruned-block4-90-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 90% - 4-block'.
The dev-set performance of this model:
Code: URL... | [
"# oBERT-12-downstream-pruned-block4-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 12 Layers - Sparsity 90% - 4-block'.\n\n\n\nThe dev-set performance of this mode... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-12-downstream-pruned-block4-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Langua... |
null | transformers | # oBERT-6-downstream-dense-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - 0% Sparsity`, and it represents an upper bound for perfo... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-dense-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:59:35+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-dense-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - 0% Sparsity', and it represents an upper bound for performance of the corresponding pruned m... | [
"# oBERT-6-downstream-dense-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - 0% Sparsity', and it represents an upper bound for performance of the corresponding... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-dense-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n... |
null | transformers | # oBERT-6-downstream-pruned-unstructured-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - unstructured`.
```
Pru... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-pruned-unstructured-80-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T12:59:52+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-pruned-unstructured-80-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 80% - unstructured'.
The dev-set performance of this model:
... | [
"# oBERT-6-downstream-pruned-unstructured-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 80% - unstructured'.\n\n\n\nThe dev-set performance of ... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-pruned-unstructured-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large L... |
null | transformers | # oBERT-6-downstream-pruned-unstructured-90-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 90% - unstructured`.
```
Pru... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-pruned-unstructured-90-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:00:05+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-pruned-unstructured-90-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 90% - unstructured'.
The dev-set performance of this model:
... | [
"# oBERT-6-downstream-pruned-unstructured-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 90% - unstructured'.\n\n\n\nThe dev-set performance of ... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-pruned-unstructured-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large L... |
null | transformers | # oBERT-6-downstream-pruned-block4-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - 4-block`.
```
Pruning method... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:00:18+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-pruned-block4-80-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 80% - 4-block'.
The dev-set performance of this model:
Code: URL
... | [
"# oBERT-6-downstream-pruned-block4-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 80% - 4-block'.\n\n\n\nThe dev-set performance of this model:... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-pruned-block4-80-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Languag... |
null | transformers | # oBERT-6-downstream-pruned-block4-90-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 90% - 4-block`.
```
Pruning method... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:00:31+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-6-downstream-pruned-block4-90-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 90% - 4-block'.
The dev-set performance of this model:
Code: URL
... | [
"# oBERT-6-downstream-pruned-block4-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\n\nIt corresponds to the model presented in the 'Table 3 - 6 Layers - Sparsity 90% - 4-block'.\n\n\n\nThe dev-set performance of this model:... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-6-downstream-pruned-block4-90-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Languag... |
null | transformers | # oBERT-3-downstream-dense-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 3 - 3 Layers - 0% Sparsity`, and it represents an upper bound for perfo... | {"language": "en", "tags": ["bert", "oBERT", "sparsity", "pruning", "compression"], "datasets": "squad"} | neuralmagic/oBERT-3-downstream-dense-squadv1 | null | [
"transformers",
"pytorch",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression",
"en",
"dataset:squad",
"arxiv:2203.07259",
"endpoints_compatible",
"region:us"
] | null | 2022-05-25T13:00:43+00:00 | [
"2203.07259"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us
| # oBERT-3-downstream-dense-squadv1
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to the model presented in the 'Table 3 - 3 Layers - 0% Sparsity', and it represents an upper bound for performance of the corresponding pruned m... | [
"# oBERT-3-downstream-dense-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n\nIt corresponds to the model presented in the 'Table 3 - 3 Layers - 0% Sparsity', and it represents an upper bound for performance of the corresponding... | [
"TAGS\n#transformers #pytorch #bert #oBERT #sparsity #pruning #compression #en #dataset-squad #arxiv-2203.07259 #endpoints_compatible #region-us \n",
"# oBERT-3-downstream-dense-squadv1\n\nThis model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.\n... |
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