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text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1320863459953750016/NlmH... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/the_ironsheik/1656670410014/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/the_ironsheik | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T09:11:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
The Iron Sheik
@the\_ironsheik
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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... | igpaub/LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T09:42:13+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 |
# (NER) roberta-base : conll2012_ontonotesv5-english-v4
This `roberta-base` NER model was finetuned on `conll2012_ontonotesv5` version `english-v4` dataset. <br>
Check out [NER-System Repository](https://github.com/djagatiya/NER-System) for more information.
## Dataset
- conll2012_ontonotesv5
- Language : Englis... | {"tags": ["token-classification"], "datasets": ["djagatiya/ner-ontonotes-v5-eng-v4"], "widget": [{"text": "On September 1st George won 1 dollar while watching Game of Thrones."}]} | djagatiya/ner-roberta-base-ontonotesv5-englishv4 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:djagatiya/ner-ontonotes-v5-eng-v4",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T09:49:16+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us
| (NER) roberta-base : conll2012\_ontonotesv5-english-v4
======================================================
This 'roberta-base' NER model was finetuned on 'conll2012\_ontonotesv5' version 'english-v4' dataset.
Check out NER-System Repository for more information.
Dataset
-------
* conll2012\_ontonotesv5
... | [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | abhinav-kumar-thakur/distilbert-base-uncased-finetuned-mrpc | 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 | 2022-07-01T09:50:13+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
| distilbert-base-uncased-finetuned-mrpc
======================================
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: 0.5556
* Accuracy: 0.8578
* F1: 0.9007
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: 5",
"### 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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-test2sql
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-test2sql", "results": []}]} | mousaazari/t5-test2sql | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T10:12:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-test2sql
===========
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1207
* Rouge2 Precision: 0.9214
* Rouge2 Recall: 0.4259
* Rouge2 Fmeasure: 0.5578
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: 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: 50",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-classification | transformers |
## Model information:
This model is the [roberta-base](https://huggingface.co/roberta-base) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcatio... | {"language": ["en"], "license": "cc", "datasets": ["MIMIC-III\u00a0"], "thumbnail": "url to a thumbnail used in social sharing", "widget": [{"text": "This report discusses the diagnosis of lung cancer in a female patient who has never smoked."}]} | sarahmiller137/roberta-base-ft-m3-lc | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T10:35:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
This model is the roberta-base model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report texts i... | [
"## Model information:\nThis model is the roberta-base model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report te... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\nThis model is the roberta-base model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classif... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1498996796093509632/Z7Vw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tacticalmaid/1656676226544/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tacticalmaid | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T10:48:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Maid POLadin
@tacticalmaid
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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-base-libriSpeech-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-libriSpeech-demo-colab", "results": []}]} | vishwasgautam/wav2vec2-base-libriSpeech-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T10:58:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-libriSpeech-demo-colab
====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4627
* Wer: 0.3174
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: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft500_4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft500_4", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft500_4 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T11:08:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft500\_4
==========================================
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: 1.1118
* Accuracy: 0.4807
* F1: 0.4638
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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\\_b... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | osanseviero/Reinforce-CartPole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-01T11:10:18+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft500_4class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft500_4class", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft500_4class | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T11:21:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft500\_4class
===============================================
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: 1.1343
* Accuracy: 0.4853
* F1: 0.4777
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# finetuning-sentiment-model-sst
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuning-sentiment-model-sst", "results": []}]} | semy/finetuning-sentiment-model-sst | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T11:44:13+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-sst
This model is a fine-tuned version of distilbert-base-uncased 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 h... | [
"# finetuning-sentiment-model-sst\n\nThis model is a fine-tuned version of distilbert-base-uncased 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... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-sst\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\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... | bothrajat/ppo-LunarLander | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T11:48:24+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-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1510917391533830145/XW-z... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dril-tacticalmaid/1656679850409/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dril-tacticalmaid | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T11:49:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wint & Maid POLadin
@dril-tacticalmaid
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Train... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# min(DALL·E)
[](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
[](https://discord.com/channels/823813159592001... | {"license": "mit", "tags": ["pytorch"]} | kuprel/min-dalle | null | [
"transformers",
"pytorch",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-01T11:50:34+00:00 | [] | [] | TAGS
#transformers #pytorch #license-mit #endpoints_compatible #has_space #region-us
|
# min(DALL·E)
. It has been stripped down for inference and converted to PyTorch. The only third party dependencies are numpy, requests, pillow and torch.
To generate a 4x4 grid of DALL·E Mega images i... | [
"# min(DALL·E)\n\n. It has been stripped down for inference and converted to PyTorch. The only third party dependencies are numpy, requests, pillow and torch.\n\nTo generate a 4x4 grid of DALL·E ... | [
"TAGS\n#transformers #pytorch #license-mit #endpoints_compatible #has_space #region-us \n",
"# min(DALL·E)\n\n. It has been stripped down for inference and converted to PyTorch. The only third ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-text2sql
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-text2sql", "results": []}]} | mousaazari/t5-text2sql | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T12:00:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-text2sql
===========
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1611
* Rouge2 Precision: 0.8631
* Rouge2 Recall: 0.2595
* Rouge2 Fmeasure: 0.3674
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: 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: 30",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1026034854
- CO2 Emissions (in grams): 0.04087910671538076
## Validation Metrics
- Loss: 1.0871405601501465
- Rouge1: 55.8225
- Rouge2: 34.1547
- RougeL: 54.4274
- RougeLsum: 54.408
- Gen Len: 23.178
## Usage
You can use cURL to access this ... | {"language": "unk", "tags": "autotrain", "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.04087910671538076} | hellennamulinda/eng-lug | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain",
"unk",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T12:10:28+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain #unk #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1026034854
- CO2 Emissions (in grams): 0.04087910671538076
## Validation Metrics
- Loss: 1.0871405601501465
- Rouge1: 55.8225
- Rouge2: 34.1547
- RougeL: 54.4274
- RougeLsum: 54.408
- Gen Len: 23.178
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1026034854\n- CO2 Emissions (in grams): 0.04087910671538076",
"## Validation Metrics\n\n- Loss: 1.0871405601501465\n- Rouge1: 55.8225\n- Rouge2: 34.1547\n- RougeL: 54.4274\n- RougeLsum: 54.408\n- Gen Len: 23.178",
"## Usage\n\nYou can ... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain #unk #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1026034854\n- CO2 Emissions (in grams): 0.04087910671538076",
"## Validation Metr... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | osanseviero/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-01T12:32:34+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
text-classification | transformers |
## Model information:
This model is the [scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other varia... | {"language": "en", "license": "cc", "tags": ["text-classifcation"], "datasets": "MIMIC-III\u00a0", "widget": [{"text": "This report discusses the diagnosis of lung cancer in a female patient who has never smoked."}]} | sarahmiller137/scibert-scivocab-uncased-ft-m3-lc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text-classifcation",
"en",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T12:38:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #text-classifcation #en #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
This model is the scibert_scivocab_uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology re... | [
"## Model information:\nThis model is the scibert_scivocab_uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiolo... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text-classifcation #en #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\nThis model is the scibert_scivocab_uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The ta... |
null | null | aaa | {} | gegham/active_test_model | null | [
"region:us"
] | null | 2022-07-01T12:48:41+00:00 | [] | [] | TAGS
#region-us
| aaa | [] | [
"TAGS\n#region-us \n"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | WalidLak/Testmodel | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T12:51:42+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | Guillaume63/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-01T13:12:10+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | vbertret/Reinforce-CartPole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-01T13:25:04+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1071537591
- CO2 Emissions (in grams): 0.03985401798934018
## Validation Metrics
- Loss: 0.5283975601196289
- Accuracy: 0.7389705882352942
- Precision: 0.5032894736842105
- Recall: 0.3574766355140187
- AUC: 0.7135599403856304
- F1: 0.... | {"language": "unk", "tags": "autotrain", "datasets": ["Luojike/autotrain-data-test_3"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.03985401798934018} | Luojike/autotrain-test_3-1071537591 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:Luojike/autotrain-data-test_3",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T13:59:39+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Luojike/autotrain-data-test_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1071537591
- CO2 Emissions (in grams): 0.03985401798934018
## Validation Metrics
- Loss: 0.5283975601196289
- Accuracy: 0.7389705882352942
- Precision: 0.5032894736842105
- Recall: 0.3574766355140187
- AUC: 0.7135599403856304
- F1: 0.... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1071537591\n- CO2 Emissions (in grams): 0.03985401798934018",
"## Validation Metrics\n\n- Loss: 0.5283975601196289\n- Accuracy: 0.7389705882352942\n- Precision: 0.5032894736842105\n- Recall: 0.3574766355140187\n- AUC: 0.7135599... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Luojike/autotrain-data-test_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1071537591\n- CO2 Emissions (in grams... |
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="angelinux/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | angelinux/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-01T14:27:17+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 |
<!-- 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. -->
# opt-125m-economy-data
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "opt-125m-economy-data", "results": []}]} | Abdelmageed95/opt-125m-economy-data | null | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T14:30:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# opt-125m-economy-data
This model is a fine-tuned version of facebook/opt-125m on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9036
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More ... | [
"# opt-125m-economy-data\n\nThis model is a fine-tuned version of facebook/opt-125m on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.9036",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-125m-economy-data\n\nThis model is a fine-tuned version of facebook/opt-125m on the None dataset.\nIt achieves the f... |
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="angelinux/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | angelinux/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-01T14:36:22+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #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 #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 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | trtd56/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T14:36:55+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | null |
## rasm
Arabic art using GANs. We currently have two models for generating calligraphy and mosaics.
## Notebooks
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Name</b></th>
<th class="tg-yw4l"><b>Notebook</b></th>
</tr>
<tr>
<td class="tg-yw4l">Visualization</td>
<td class="tg-yw4l"><a href... | {"tags": ["Keras", "gan", "tensorflow", "conditional-image-generation"]} | arbml/Rasm | null | [
"Keras",
"gan",
"tensorflow",
"conditional-image-generation",
"region:us"
] | null | 2022-07-01T14:38:20+00:00 | [] | [] | TAGS
#Keras #gan #tensorflow #conditional-image-generation #region-us
|
## rasm
Arabic art using GANs. We currently have two models for generating calligraphy and mosaics.
## Notebooks
<table class="tg">
<tr>
<th class="tg-yw4l"><b>Name</b></th>
<th class="tg-yw4l"><b>Notebook</b></th>
</tr>
<tr>
<td class="tg-yw4l">Visualization</td>
<td class="tg-yw4l"><a href... | [
"## rasm\nArabic art using GANs. We currently have two models for generating calligraphy and mosaics.",
"## Notebooks \n\n<table class=\"tg\">\n <tr>\n <th class=\"tg-yw4l\"><b>Name</b></th>\n <th class=\"tg-yw4l\"><b>Notebook</b></th>\n </tr>\n <tr>\n <td class=\"tg-yw4l\">Visualization</td>\n <td... | [
"TAGS\n#Keras #gan #tensorflow #conditional-image-generation #region-us \n",
"## rasm\nArabic art using GANs. We currently have two models for generating calligraphy and mosaics.",
"## Notebooks \n\n<table class=\"tg\">\n <tr>\n <th class=\"tg-yw4l\"><b>Name</b></th>\n <th class=\"tg-yw4l\"><b>Notebook</... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1071837613
- CO2 Emissions (in grams): 0.012225117907336358
## Validation Metrics
- Loss: 0.533202052116394
- Accuracy: 0.7408088235294118
- Precision: 0.5072463768115942
- Recall: 0.4088785046728972
- AUC: 0.710585043624057
- F1: 0.4... | {"language": "unk", "tags": "autotrain", "datasets": ["Luojike/autotrain-data-test-4-macbert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.012225117907336358} | Luojike/autotrain-test-4-macbert-1071837613 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:Luojike/autotrain-data-test-4-macbert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T14:43:59+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Luojike/autotrain-data-test-4-macbert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1071837613
- CO2 Emissions (in grams): 0.012225117907336358
## Validation Metrics
- Loss: 0.533202052116394
- Accuracy: 0.7408088235294118
- Precision: 0.5072463768115942
- Recall: 0.4088785046728972
- AUC: 0.710585043624057
- F1: 0.4... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1071837613\n- CO2 Emissions (in grams): 0.012225117907336358",
"## Validation Metrics\n\n- Loss: 0.533202052116394\n- Accuracy: 0.7408088235294118\n- Precision: 0.5072463768115942\n- Recall: 0.4088785046728972\n- AUC: 0.7105850... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Luojike/autotrain-data-test-4-macbert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1071837613\n- CO2 Emissions (... |
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="angelinux/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": "7.50 +/... | angelinux/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-01T14:52:57+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"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BreakoutNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stab... | {"library_name": "stable-baselines3", "tags": ["BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BreakoutNoFrameskip-v4",... | danieladejumo/dqn-BreakoutNoFrameskip-v4 | null | [
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T16:43:34+00:00 | [] | [] | TAGS
#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BreakoutNoFrameskip-v4
This is a trained model of a DQN agent playing BreakoutNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.... | [
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agent... | [
"TAGS\n#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
summarization | transformers |
# Bart-Large CiteSum (Sentences)
This is facebook/bart-large fine-tuned on CiteSum.
The "src" column is the input and the "tgt" column is the target summarization.
## Authors
### Yuning Mao, Ming Zhong, Jiawei Han
#### University of Illinois Urbana-Champaign
{yuningm2, mingz5, hanj}@illinois.edu
## Results
``... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["summarization"], "datasets": ["yuningm/citesum"], "widget": [{"text": "Abstract-This paper presents a control strategy that allows a group of mobile robots to position themselves to optimize the measurement of sensory information in the environment. The robots use... | yuningm/bart-large-citesum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:yuningm/citesum",
"arxiv:2205.06207",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T16:53:34+00:00 | [
"2205.06207"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-yuningm/citesum #arxiv-2205.06207 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Bart-Large CiteSum (Sentences)
This is facebook/bart-large fine-tuned on CiteSum.
The "src" column is the input and the "tgt" column is the target summarization.
## Authors
### Yuning Mao, Ming Zhong, Jiawei Han
#### University of Illinois Urbana-Champaign
{yuningm2, mingz5, hanj}@URL
## Results
## Datase... | [
"# Bart-Large CiteSum (Sentences)\n\nThis is facebook/bart-large fine-tuned on CiteSum. \nThe \"src\" column is the input and the \"tgt\" column is the target summarization.",
"## Authors",
"### Yuning Mao, Ming Zhong, Jiawei Han",
"#### University of Illinois Urbana-Champaign \n{yuningm2, mingz5, hanj}@URL... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-yuningm/citesum #arxiv-2205.06207 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bart-Large CiteSum (Sentences)\n\nThis is facebook/bart-large fine-tuned on CiteSum. \nThe \"src\" column is... |
summarization | transformers | # Bart-Large CiteSum (Titles)
This is facebook/bart-large fine-tuned on CiteSum. The "src" column is the input and the "title" column is the target summarization.
## Authors
### Yuning Mao, Ming Zhong, Jiawei Han
#### University of Illinois Urbana-Champaign
{yuningm2, mingz5, hanj}@illinois.edu
## Results
```
{
... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["summarization"], "datasets": ["yuningm/citesum"], "widget": [{"text": "Abstract-This paper presents a control strategy that allows a group of mobile robots to position themselves to optimize the measurement of sensory information in the environment. The robots use... | yuningm/bart-large-citesum-title | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:yuningm/citesum",
"arxiv:2205.06207",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T16:54:02+00:00 | [
"2205.06207"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-yuningm/citesum #arxiv-2205.06207 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # Bart-Large CiteSum (Titles)
This is facebook/bart-large fine-tuned on CiteSum. The "src" column is the input and the "title" column is the target summarization.
## Authors
### Yuning Mao, Ming Zhong, Jiawei Han
#### University of Illinois Urbana-Champaign
{yuningm2, mingz5, hanj}@URL
## Results
## Dataset Des... | [
"# Bart-Large CiteSum (Titles)\n\nThis is facebook/bart-large fine-tuned on CiteSum. The \"src\" column is the input and the \"title\" column is the target summarization.",
"## Authors",
"### Yuning Mao, Ming Zhong, Jiawei Han",
"#### University of Illinois Urbana-Champaign \n{yuningm2, mingz5, hanj}@URL",
... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-yuningm/citesum #arxiv-2205.06207 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bart-Large CiteSum (Titles)\n\nThis is facebook/bart-large fine-tuned on CiteSum. The \"src\" column is the i... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1226468832933564418/oZJz... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lexisother/1656698565003/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lexisother | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T17:02:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Alyxia Sother
@lexisother
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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... | ben765/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T17:15:39+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. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an u... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-prop-16-train-set", "results": []}]} | annS/roberta-base-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T17:20:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-prop-16-train-set
This model is a fine-tuned version of roberta-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore... |
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. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an u... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-prop-16-train-set", "results": []}]} | scottstots/roberta-base-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T17:28:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-prop-16-train-set
This model is a fine-tuned version of roberta-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-0", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-0 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T17:29:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-0
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2904
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet-base-cased-finetuned-hotpot_qa
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlnet-base-cased-finetuned-hotpot_qa", "results": []}]} | vish88/xlnet-base-cased-finetuned-hotpot_qa | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T17:38:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlnet-base-cased-finetuned-hotpot\_qa
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9574
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_s... |
text2text-generation | transformers |
# bart-base-styletransfer-subjective-to-neutral
## Model description
This [facebook/bart-base](https://huggingface.co/facebook/bart-base) model has been fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://arxiv.org/pdf/1911.09709.pdf) - a parallel corpus of 180,000 biased and neutralized sentence pairs along wi... | {"license": "apache-2.0"} | cffl/bart-base-styletransfer-subjective-to-neutral | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1911.09709",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-01T17:41:46+00:00 | [
"1911.09709"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-1911.09709 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bart-base-styletransfer-subjective-to-neutral
## Model description
This facebook/bart-base model has been fine-tuned on the Wiki Neutrality Corpus (WNC) - a parallel corpus of 180,000 biased and neutralized sentence pairs along with contextual sentences and metadata. The model can be used to transfer style in text... | [
"# bart-base-styletransfer-subjective-to-neutral",
"## Model description\nThis facebook/bart-base model has been fine-tuned on the Wiki Neutrality Corpus (WNC) - a parallel corpus of 180,000 biased and neutralized sentence pairs along with contextual sentences and metadata. The model can be used to transfer styl... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-1911.09709 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bart-base-styletransfer-subjective-to-neutral",
"## Model description\nThis facebook/bart-base model has been fine-tuned on the Wiki Neutralit... |
fill-mask | transformers |
## RoBERTa Latin base model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with a vocabulary size 50,000.
### Training Data
* Subset of [CC-100/la](https://data.statmt.org/cc-100... | {"language": "la", "license": "cc-by-sa-4.0", "datasets": ["cc100"], "widget": [{"text": "quod est tibi <mask> ?\""}, {"text": "vita brevis, ars <mask>."}, {"text": "errare <mask> est."}, {"text": "usus est magister <mask>."}]} | ClassCat/roberta-base-latin-v2 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"la",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T17:45:18+00:00 | [] | [
"la"
] | TAGS
#transformers #pytorch #roberta #fill-mask #la #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## RoBERTa Latin base model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with a vocabulary size 50,000.
### Training Data
* Subset of CC-100/la : Monolingual Datasets from Web ... | [
"## RoBERTa Latin base model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses RoBERTa base setttings except vocabulary size.",
"### Tokenizer\n\nUsing BPE tokenizer with a vocabulary size 50,000.",
"### Training Data \n\n* Subset of CC-100/la : Mo... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #la #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## RoBERTa Latin base model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses RoBERTa base setttin... |
feature-extraction | transformers |
# ALBERT for ARQMath 3
This repository contains our best model for ARQMath 3, the math_10 model. It was initialised from ALBERT-base-v2 and further pre-trained on Math StackExchange in three different stages. We also added more LaTeX tokens to the tokenizer to enable a better tokenization of mathematical formulas. ma... | {"language": ["en"], "tags": ["retrieval", "math-retrieval"], "datasets": ["MathematicalStackExchange", "ARQMath"]} | AnReu/albert-for-arqmath-3 | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"feature-extraction",
"retrieval",
"math-retrieval",
"en",
"dataset:MathematicalStackExchange",
"dataset:ARQMath",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-01T18:31:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #albert #feature-extraction #retrieval #math-retrieval #en #dataset-MathematicalStackExchange #dataset-ARQMath #endpoints_compatible #has_space #region-us
| ALBERT for ARQMath 3
====================
This repository contains our best model for ARQMath 3, the math\_10 model. It was initialised from ALBERT-base-v2 and further pre-trained on Math StackExchange in three different stages. We also added more LaTeX tokens to the tokenizer to enable a better tokenization of mathe... | [
"### Update\n\n\nWe have also further pre-trained a BERT-base-cased model in the same way as our ALBERT model. You can find it here: AnReu/math\\_pretrained\\_bert.\n\n\nUsage\n=====\n\n\nIf you find this model useful, consider citing our paper:"
] | [
"TAGS\n#transformers #pytorch #safetensors #albert #feature-extraction #retrieval #math-retrieval #en #dataset-MathematicalStackExchange #dataset-ARQMath #endpoints_compatible #has_space #region-us \n",
"### Update\n\n\nWe have also further pre-trained a BERT-base-cased model in the same way as our ALBERT model. ... |
text-classification | transformers |
# bert-base-styleclassification-subjective-neutral
## Model description
This [bert-base-uncased](https://huggingface.co/bert-base-uncased) model has been fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://arxiv.org/pdf/1911.09709.pdf) - a parallel corpus of 180,000 biased and neutralized sentence pairs along w... | {"license": "apache-2.0"} | cffl/bert-base-styleclassification-subjective-neutral | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:1911.09709",
"arxiv:1703.01365",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-01T18:35:53+00:00 | [
"1911.09709",
"1703.01365"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-1911.09709 #arxiv-1703.01365 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert-base-styleclassification-subjective-neutral
## Model description
This bert-base-uncased model has been fine-tuned on the Wiki Neutrality Corpus (WNC) - a parallel corpus of 180,000 biased and neutralized sentence pairs along with contextual sentences and metadata. The model can be used to classify text as sub... | [
"# bert-base-styleclassification-subjective-neutral",
"## Model description\nThis bert-base-uncased model has been fine-tuned on the Wiki Neutrality Corpus (WNC) - a parallel corpus of 180,000 biased and neutralized sentence pairs along with contextual sentences and metadata. The model can be used to classify te... | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-1911.09709 #arxiv-1703.01365 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bert-base-styleclassification-subjective-neutral",
"## Model description\nThis bert-base-uncased model has been fine-tuned on... |
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... | a-doering/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T18:56:21+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
question-answering | transformers |
<!-- 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-becas-7
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-7", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-7 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T19:00:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-7
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 4.3059
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1013878708539666439/FqgS... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/o_strunz/1656712663617/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/o_strunz | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T20:57:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
'O Strunz
@o\_strunz
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | jdeboever/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T21:50:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1363
* F1: 0.8627
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deit-base-mri
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/d... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "deit-base-mri", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "mriDataSet", "type": "imagefol... | raedinkhaled/deit-base-mri | null | [
"transformers",
"pytorch",
"tensorboard",
"deit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T22:10:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| deit-base-mri
=============
This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the mriDataSet dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0657
* Accuracy: 0.9901
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #deit #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
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. -->
# twitter-roberta-base-WNUT
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiff... | {"tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter-roberta-base-WNUT", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "args": "wnut... | emilys/twitter-roberta-base-WNUT | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T00:07:02+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-wnut_17 #model-index #autotrain_compatible #endpoints_compatible #region-us
| twitter-roberta-base-WNUT
=========================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1938
* Precision: 0.7045
* Recall: 0.6304
* F1: 0.6654
* Accuracy: 0.9640
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 1024\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Tra... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-wnut_17 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
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. -->
# bertlawbr
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following resul... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertlawbr", "results": []}]} | alfaneo/jurisbert-base-portuguese-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T01:53:52+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertlawbr
=========
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0495
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* se... |
fill-mask | transformers |
# BERT Tiny (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo: [... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-tiny-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T02:00:53+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT Tiny (uncased)
===================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Usage
--... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT Mini (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo: [... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-mini-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T02:05:50+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT Mini (uncased)
===================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Usage
--... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT Small (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo: ... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-small-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T02:10:08+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT Small (uncased)
====================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Usage
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT Medium (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo:... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-medium-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T02:10:32+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT Medium (uncased)
=====================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Usag... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# xenergy/gpt2-indo
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the fol... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "xenergy/gpt2-indo", "results": []}]} | xenergy/gpt2-indo | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-02T03:15:36+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| xenergy/gpt2-indo
=================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.3370
* Validation Loss: 1.8387
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_schedule... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'c... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | jdang/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T03:22:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2166
* Accuracy: 0.924
* F1: 0.9239
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# HuBERT-base-libriSpeech-demo-colab
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "HuBERT-base-libriSpeech-demo-colab", "results": []}]} | vishwasgautam/HuBERT-base-libriSpeech-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T03:36:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| HuBERT-base-libriSpeech-demo-colab
==================================
This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1456
* Wer: 0.2443
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #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: 2\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vinitharaj/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vinitharaj/distilbert-base-uncased-finetuned-squad", "results": []}]} | vinitharaj/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T04:42:36+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| vinitharaj/distilbert-base-uncased-finetuned-squad
==================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.5718
* Validation Loss: 4.2502
* Epoch: 1
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 46, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name'... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
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. -->
# AIDman-wav2vec2-large-xls-r-300m-Irish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "AIDman-wav2vec2-large-xls-r-300m-Irish-colab", "results": []}]} | AIDman/AIDman-wav2vec2-large-xls-r-300m-Irish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T04:56:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| AIDman-wav2vec2-large-xls-r-300m-Irish-colab
============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2158
* Wer: 0.5657
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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* t... |
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 huggingface_sb3 import load_from_hub
checkpoint = ... | {"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... | rainbow/rainbow-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-02T05:03:20+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-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1458465489425158144/WQBM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pldroneoperator-superpiss/1656742858038/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pldroneoperator-superpiss | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-02T05:19:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Peter & xbox 720
@pldroneoperator-superpiss
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-skin-cancer
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-skin-cancer", "results": [{"task": {"type": "image-classification", "name": "Image ... | gianlab/swin-tiny-patch4-window7-224-finetuned-skin-cancer | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-02T05:35:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| swin-tiny-patch4-window7-224-finetuned-skin-cancer
==================================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7695
* Accuracy: 0.7275
Model descriptio... | [
"### 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: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe foll... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | kidzy/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:00:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7711
* Accuracy: 0.9174
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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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* lea... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | andreaschandra/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:02:31+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2186
* Accuracy: 0.924
* F1: 0.9241
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad-finetuned-triviaqa
This model is a fine-tuned version of [FabianWillner/bert-base-uncased-fine... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-squad-finetuned-triviaqa", "results": []}]} | FabianWillner/bert-base-uncased-finetuned-squad-finetuned-triviaqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:14:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad-finetuned-triviaqa
====================================================
This model is a fine-tuned version of FabianWillner/bert-base-uncased-finetuned-squad on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9132
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
fill-mask | transformers |
# BERT L2-H256 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L2-H256-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:25:41+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L2-H256 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L2-H512 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L2-H512-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:25:54+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L2-H512 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L2-H768 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L2-H768-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:26:04+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L2-H768 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L4-H128 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L4-H128-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:26:56+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L4-H128 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# mT5-base finetuned on the GermanQuAD dataset for answer-agnostic question generation
This model is a finetuned [mT5-base](https://arxiv.org/abs/2010.11934) model for the task of answer-agnostic (or end-to-end) question generation. The approach from [Lopez et al.](https://arxiv.org/abs/2005.01107) was used called *A... | {"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "metrics": ["sacrebleu", "bleu", "rouge-l", "meteor", "bertscore"], "widget": [{"text": "generate question: KMI ist eine Variante des allgemeinen Bachelors Informatik und damit zu ca. 80% identisch mit dem allgem... | tilomichel/mT5-base-GermanQuAD-e2e-qg | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"question generation",
"de",
"dataset:deepset/germanquad",
"arxiv:2010.11934",
"arxiv:2005.01107",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region... | null | 2022-07-02T06:27:06+00:00 | [
"2010.11934",
"2005.01107"
] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #question generation #de #dataset-deepset/germanquad #arxiv-2010.11934 #arxiv-2005.01107 #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5-base finetuned on the GermanQuAD dataset for answer-agnostic question generation
This model is a finetuned mT5-base model for the task of answer-agnostic (or end-to-end) question generation. The approach from Lopez et al. was used called *All questions per line (AQPL)*. This means a paragraph is provided as inp... | [
"# mT5-base finetuned on the GermanQuAD dataset for answer-agnostic question generation\n\nThis model is a finetuned mT5-base model for the task of answer-agnostic (or end-to-end) question generation. The approach from Lopez et al. was used called *All questions per line (AQPL)*. This means a paragraph is provided ... | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #question generation #de #dataset-deepset/germanquad #arxiv-2010.11934 #arxiv-2005.01107 #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5-base finetuned on the GermanQuAD da... |
fill-mask | transformers |
# BERT L4-H768 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L4-H768-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:27:17+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L4-H768 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L6-H128 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L6-H128-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:27:40+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L6-H128 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L6-H256 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L6-H256-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:27:52+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L6-H256 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L6-H512 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L6-H512-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:28:07+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L6-H512 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L6-H768 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L6-H768-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:28:24+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L6-H768 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L8-H128 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L8-H128-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:28:58+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L8-H128 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L8-H256 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L8-H256-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:29:10+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L8-H256 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L8-H768 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google repo... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L8-H768-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:29:34+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L8-H768 (uncased)
======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
Us... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L10-H128 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google rep... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L10-H128-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:29:48+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L10-H128 (uncased)
=======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L10-H256 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google rep... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L10-H256-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:30:05+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L10-H256 (uncased)
=======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L10-H512 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google rep... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L10-H512-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:30:14+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L10-H512 (uncased)
=======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L10-H768 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google rep... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L10-H768-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:30:24+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L10-H768 (uncased)
=======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L12-H128 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google rep... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L12-H128-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:30:34+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L12-H128 (uncased)
=======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L12-H256 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google rep... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L12-H256-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:30:42+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L12-H256 (uncased)
=======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# BERT L12-H512 (uncased)
Mini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) is used.
See the original Google rep... | {"language": ["en"], "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | gaunernst/bert-L12-H512-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1908.08962",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:31:41+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT L12-H512 (uncased)
=======================
Mini BERT models from URL that the HF team didn't convert. The original conversion script is used.
See the original Google repo: google-research/bert
Note: it's not clear if these checkpoints have undergone knowledge distillation.
Model variants
--------------
... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1908.08962 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dat... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "test", "results": []}]} | Mimita6654/test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T06:44:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# test
This model is a fine-tuned version of prajjwal1/bert-small on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followi... | [
"# test\n\nThis model is a fine-tuned version of prajjwal1/bert-small on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# test\n\nThis model is a fine-tuned version of prajjwal1/bert-small on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Int... |
token-classification | transformers |
# akhisreelibra/bert-malayalam-pos-tagger
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on a set of tagged Malayalam sentence dataset
It achieves the following results on the evaluation set:
- Loss: 0.4383
- Precision: 0.7380
- Recall: 0... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-malayalam-pos-tagger", "results": []}]} | akhisreelibra/bert-malayalam-pos-tagger | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T07:00:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| akhisreelibra/bert-malayalam-pos-tagger
=======================================
This model is a fine-tuned version of bert-base-multilingual-uncased on a set of tagged Malayalam sentence dataset
It achieves the following results on the evaluation set:
* Loss: 0.4383
* Precision: 0.7380
* Recall: 0.7767
* F1: 0.7569... | [
"### 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 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
reinforcement-learning | 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... | infinitejoy/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-02T07:18:06+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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | duchung17/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T08:42:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4049
* Wer: 0.3556
Model description
-----------------
More information needed
Intend... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #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... |
audio-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. -->
# results
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-x... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "results", "results": []}]} | Talha/urdu-audio-emotions | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T08:46:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| results
=======
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: 0.1638
* Accuracy: 0.975
Model description
-----------------
The model Urdu audio and classify in following categories
* Angry
* Happy
* N... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 5e-05\n* train\\_batch\\_size: 32\n* eval... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1388858833582297095/5_Fg... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/crimseyvt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-02T09:12:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
CrimseyVT~
@crimseyvt
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-qa-en
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-qa-en", "results": []}]} | srcocotero/bert-qa-en | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T09:16:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-qa-en
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# bert-qa-en\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-qa-en\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed"... |
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="a-doering/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | a-doering/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-02T09:18:49+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="a-doering/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 +/... | a-doering/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-02T09:28:13+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gtsrb-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pat... | {"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["gtsrb"], "metrics": ["accuracy"], "model-index": [{"name": "gtsrb-model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "bazyl/GTSRB", "type": "gtsrb... | bazyl/gtsrb-model | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:gtsrb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-02T09:39:06+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-gtsrb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| gtsrb-model
===========
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the bazyl/GTSRB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0034
* Accuracy: 0.9993
Model description
-----------------
The German Traffic Sign Benchmark is a multi-class, single-im... | [
"### 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: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Tra... | [
"TAGS\n#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-gtsrb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
text-generation | transformers |
# Sheldon Model | {"tags": ["conversational"]} | infinix/Sheldon-bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-02T09:40:17+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sheldon Model | [
"# Sheldon Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sheldon Model"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | sswt/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-02T09:42:04+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Neha2608/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T09:48:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1363
* F1: 0.8627
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | Neha2608/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T10:15:56+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1644
* F1: 0.8617
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
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