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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
image-to-image | keras |
Here is a fully trained model of EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution) model. This model surpassed the performance of the current available SOTA models.
Spaces link - https://huggingface.co/spaces/keras-io/EDSR
Paper Link - https://arxiv.org/pdf/1707.02921
Keras Example link - htt... | {"license": "mit", "library_name": "keras", "tags": ["Keras"], "datasets": ["eugenesiow/Div2k"], "metrics": ["PSNR"], "pipeline_tag": "image-to-image"} | keras-io/EDSR | null | [
"keras",
"tensorboard",
"Keras",
"image-to-image",
"dataset:eugenesiow/Div2k",
"arxiv:1707.02921",
"license:mit",
"has_space",
"region:us"
] | null | 2022-05-30T08:38:23+00:00 | [
"1707.02921"
] | [] | TAGS
#keras #tensorboard #Keras #image-to-image #dataset-eugenesiow/Div2k #arxiv-1707.02921 #license-mit #has_space #region-us
|
Here is a fully trained model of EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution) model. This model surpassed the performance of the current available SOTA models.
Spaces link - URL
Paper Link - URL
Keras Example link - URL
It was trained for 500 epochs with 200 steps each.
# Enhanced De... | [
"# Enhanced Deep Residual Networks for Single Image Super-Resolution",
"## Introduction\n\nThis repository contains a trained model based on the Enhanced Deep Residual Networks for Single Image Super-Resolution paper. The model was trained for 500 epochs with 200 steps each, resulting in a high-quality super-reso... | [
"TAGS\n#keras #tensorboard #Keras #image-to-image #dataset-eugenesiow/Div2k #arxiv-1707.02921 #license-mit #has_space #region-us \n",
"# Enhanced Deep Residual Networks for Single Image Super-Resolution",
"## Introduction\n\nThis repository contains a trained model based on the Enhanced Deep Residual Networks f... |
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. -->
# sarcasm-detection-RoBerta-base-CR
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on t... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-RoBerta-base-CR", "results": []}]} | jkhan447/sarcasm-detection-RoBerta-base-CR | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T08:52:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-RoBerta-base-CR
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0240
- Accuracy: 0.726
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training a... | [
"# sarcasm-detection-RoBerta-base-CR\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.0240\n- Accuracy: 0.726",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-RoBerta-base-CR\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following resul... |
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. -->
# sarcasm-detection-Bert-base-uncased-CR
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-Bert-base-uncased-CR", "results": []}]} | jkhan447/sarcasm-detection-Bert-base-uncased-CR | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T08:54:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-Bert-base-uncased-CR
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2057
- Accuracy: 0.7187
## Model description
More information needed
## Intended uses & limitations
More information needed
##... | [
"# sarcasm-detection-Bert-base-uncased-CR\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.2057\n- Accuracy: 0.7187",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-Bert-base-uncased-CR\n\nThis model is a fine-tuned version of bert-base-uncased 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="Jgmorenof/q-FrozenLake-v1-4x4-noSlippery3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type":... | Jgmorenof/q-FrozenLake-v1-4x4-noSlippery3 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T08:58:41+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"
] |
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. -->
# deeppavlov-framebank-50size
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavl... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "deeppavlov-framebank-50size", "results": []}]} | ruselkomp/deeppavlov-framebank-50size | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T09:00:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| deeppavlov-framebank-50size
===========================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1007
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 #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* see... |
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... | Misha24-10/TEST2ppo-LunarLander-v5 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T09:09:57+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **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... | Misha24-10/TEST2ppo-LunarLander-v6 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T09:39:08+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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... | Misha24-10/TEST2ppo-LunarLander-v7 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T09:51:43+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **seals/Walker2d-v0**
This is a trained model of a **PPO** agent playing **seals/Walker2d-v0**
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 Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["seals/Walker2d-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Walker2d-v0", "type": "... | ernestumorga/ppo-seals_Walker2d-v0 | null | [
"stable-baselines3",
"seals/Walker2d-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T09:52:33+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Walker2d-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing seals/Walker2d-v0
This is a trained model of a PPO agent playing seals/Walker2d-v0
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.
## Usage... | [
"# PPO Agent playing seals/Walker2d-v0\nThis is a trained model of a PPO agent playing seals/Walker2d-v0\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 agents included... | [
"TAGS\n#stable-baselines3 #seals/Walker2d-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing seals/Walker2d-v0\nThis is a trained model of a PPO agent playing seals/Walker2d-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **seals/Walker2d-v0**
This is a trained model of a **PPO** agent playing **seals/Walker2d-v0**
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 Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["seals/Walker2d-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Walker2d-v0", "type": "... | ernestumorga/ppo-seals-Walker2d-v0 | null | [
"stable-baselines3",
"seals/Walker2d-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T09:53:25+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Walker2d-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing seals/Walker2d-v0
This is a trained model of a PPO agent playing seals/Walker2d-v0
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.
## Usage... | [
"# PPO Agent playing seals/Walker2d-v0\nThis is a trained model of a PPO agent playing seals/Walker2d-v0\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 agents included... | [
"TAGS\n#stable-baselines3 #seals/Walker2d-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing seals/Walker2d-v0\nThis is a trained model of a PPO agent playing seals/Walker2d-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training ... |
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": []}]} | y05uk/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 | 2022-05-30T09:59:05+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: 0.5353
* Wer: 0.3360
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... |
text-classification | transformers | # Sentiment Analysis
For an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of "nlptown/bert-base-multilingual-uncased-sentiment" was used as a pre-trained transformer. As a starting value, an accuracy of 63% was ... | {} | wuesten/sentiment-analysis-fh-kiel | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T10:07:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Sentiment Analysis
For an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of "nlptown/bert-base-multilingual-uncased-sentiment" was used as a pre-trained transformer. As a starting value, an accuracy of 63% was ... | [
"# Sentiment Analysis\nFor an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of \"nlptown/bert-base-multilingual-uncased-sentiment\" was used as a pre-trained transformer. As a starting value, an accuracy of ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment Analysis\nFor an university project at the University of Applied Sciences Kiel, we conducted a sentiment analysis with the aim of classifying restaurant ratings. The model of \"nlptown... |
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"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": []}]} | godwinh/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T10:18:22+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-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an clinc dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7721
* Accuracy: 0.9184
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #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... |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-4
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-sidewalk-4", "results": []}]} | jakka/segformer-b0-finetuned-segments-sidewalk-4 | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T10:23:46+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-sidewalk-4
==========================================
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6258
* Mean Iou: 0.1481
* Mean Accuracy: 0.1991
* Overall Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
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/1522479920714240001/wi1L... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/ultrafungi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T10:46:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
sydney
@ultrafungi
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"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_300m_mt5-small_minds14.en-US
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingf... | {"language": ["en-US"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_mt5-small_minds14.en-US", "results": []}]} | Splend1dchan/xtreme_s_xlsr_300m_mt5-small_minds14.en-US | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T10:47:22+00:00 | [] | [
"en-US"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_mt5-small\_minds14.en-US
===============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
* Loss: 4.7321
* F1: 0.0154
* Accuracy: 0.0638
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_b... |
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. -->
#
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collect... | {"language": ["lb"], "license": "mit", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-8h-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"lb",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T10:58:01+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us
|
#
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collected from URL. Then the model was fine-tuned on 8h of labelled
Luxembourgish speech from the same domain. Additionally, we rescore the output transcription
with a 5-gram language model tr... | [
"#",
"## Model description\n\nWe fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech\ncollected from URL. Then the model was fine-tuned on 8h of labelled\nLuxembourgish speech from the same domain. Additionally, we rescore the output transcription \nwith a 5-gram languag... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us \n",
"#",
"## Model description\n\nWe fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech\ncollected from URL. T... |
automatic-speech-recognition | transformers | /home/sanchitgandhi/seq2seq-speech/README.md | {} | sanchit-gandhi/flax-wav2vec2-2-bart-large-ls-960h-feature-encoder | null | [
"transformers",
"speech-encoder-decoder",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T10:58:12+00:00 | [] | [] | TAGS
#transformers #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #region-us
| /home/sanchitgandhi/seq2seq-speech/URL | [] | [
"TAGS\n#transformers #speech-encoder-decoder #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **seals/Ant-v0**
This is a trained model of a **PPO** agent playing **seals/Ant-v0**
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 Stable Baselines3
reinfo... | {"library_name": "stable-baselines3", "tags": ["seals/Ant-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Ant-v0", "type": "seals/Ant-... | ernestumorga/ppo-seals-Ant-v0 | null | [
"stable-baselines3",
"seals/Ant-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T10:59:03+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Ant-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing seals/Ant-v0
This is a trained model of a PPO agent playing seals/Ant-v0
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.
## Usage (with SB3... | [
"# PPO Agent playing seals/Ant-v0\nThis is a trained model of a PPO agent playing seals/Ant-v0\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 agents included.",
"## ... | [
"TAGS\n#stable-baselines3 #seals/Ant-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing seals/Ant-v0\nThis is a trained model of a PPO agent playing seals/Ant-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for S... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **seals/Hopper-v0**
This is a trained model of a **PPO** agent playing **seals/Hopper-v0**
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 Stable Baselines3
... | {"library_name": "stable-baselines3", "tags": ["seals/Hopper-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Hopper-v0", "type": "seal... | ernestumorga/ppo-seals-Hopper-v0 | null | [
"stable-baselines3",
"seals/Hopper-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T10:59:04+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Hopper-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing seals/Hopper-v0
This is a trained model of a PPO agent playing seals/Hopper-v0
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.
## Usage (wi... | [
"# PPO Agent playing seals/Hopper-v0\nThis is a trained model of a PPO agent playing seals/Hopper-v0\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 agents included.",
... | [
"TAGS\n#stable-baselines3 #seals/Hopper-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing seals/Hopper-v0\nThis is a trained model of a PPO agent playing seals/Hopper-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framew... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **seals/Humanoid-v0**
This is a trained model of a **PPO** agent playing **seals/Humanoid-v0**
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 Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["seals/Humanoid-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Humanoid-v0", "type": "... | ernestumorga/ppo-seals-Humanoid-v0 | null | [
"stable-baselines3",
"seals/Humanoid-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T11:00:35+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Humanoid-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing seals/Humanoid-v0
This is a trained model of a PPO agent playing seals/Humanoid-v0
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.
## Usage... | [
"# PPO Agent playing seals/Humanoid-v0\nThis is a trained model of a PPO agent playing seals/Humanoid-v0\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 agents included... | [
"TAGS\n#stable-baselines3 #seals/Humanoid-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing seals/Humanoid-v0\nThis is a trained model of a PPO agent playing seals/Humanoid-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training ... |
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-vios
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vivos_dataset"], "model-index": [{"name": "wav2vec2-base-vios", "results": []}]} | tclong/wav2vec2-base-vios | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:vivos_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T11:00:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-vios
==================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the vivos\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3729
* Wer: 0.2427
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 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-vivos_dataset #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* ... |
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. -->
# GWW
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "GWW", "results": []}]} | dunlp/GWW | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T11:06:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| GWW
===
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on Dutch civiel works dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7097
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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: 5.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_si... |
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="dfomin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | dfomin/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T11:38:19+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="dfomin/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/... | dfomin/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T11:42:28+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 |
# MobileViT (small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](ht... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | apple/mobilevit-small | null | [
"transformers",
"pytorch",
"tf",
"coreml",
"mobilevit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2110.02178",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-30T11:43:23+00:00 | [
"2110.02178"
] | [] | TAGS
#transformers #pytorch #tf #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
| MobileViT (small-sized model)
=============================
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and first released in this repository. The license... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe MobileViT model was pretrained on ImageNet-1k, a dataset consistin... | [
"TAGS\n#transformers #pytorch #tf #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-sem_eval-english
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["3rd", "generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "bert-finetuned-sem_eval-english", "results": []}]} | zdreiosis/bert-finetuned-sem_eval-english | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"3rd",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T11:45:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #3rd #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-finetuned-sem_eval-english
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5536
- F1: 0.5455
- Roc Auc: 0.6968
- Accuracy: 0.1839
## Model description
More information needed
## Intended uses & limitations
Mor... | [
"# bert-finetuned-sem_eval-english\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5536\n- F1: 0.5455\n- Roc Auc: 0.6968\n- Accuracy: 0.1839",
"## Model description\n\nMore information needed",
"## Intended uses... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #3rd #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-finetuned-sem_eval-english\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the fol... |
image-classification | transformers |
# MobileViT (extra small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this reposito... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | apple/mobilevit-x-small | null | [
"transformers",
"pytorch",
"tf",
"coreml",
"mobilevit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2110.02178",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T11:45:19+00:00 | [
"2110.02178"
] | [] | TAGS
#transformers #pytorch #tf #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #region-us
| MobileViT (extra small-sized model)
===================================
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and first released in this repository.... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe MobileViT model was pretrained on ImageNet-1k, a dataset consistin... | [
"TAGS\n#transformers #pytorch #tf #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,00... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# en_tn_ukuxhumana_model2
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-tn](https://huggingface.co/Helsinki-NLP/o... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "en_tn_ukuxhumana_model2", "results": []}]} | kabelomalapane/en_tn_ukuxhumana_model2 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T11:46:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# en_tn_ukuxhumana_model2
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-tn on the ukuxhumana dataset.
- Train_data = 12080
- Dev_data = 3000
It achieves the following results on the evaluation set:
After training:
- Loss: 2.6466
- Bleu: 21.8204
## Model description
More information needed
## ... | [
"# en_tn_ukuxhumana_model2\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-tn on the ukuxhumana dataset.\n\n- Train_data = 12080\n- Dev_data = 3000\n\n\nIt achieves the following results on the evaluation set:\n\nAfter training:\n- Loss: 2.6466\n- Bleu: 21.8204",
"## Model description\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# en_tn_ukuxhumana_model2\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-tn on the ukuxhumana dataset.\n\... |
image-classification | transformers |
# MobileViT (extra extra small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this re... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | apple/mobilevit-xx-small | null | [
"transformers",
"pytorch",
"tf",
"coreml",
"mobilevit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2110.02178",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-30T11:46:35+00:00 | [
"2110.02178"
] | [] | TAGS
#transformers #pytorch #tf #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
| MobileViT (extra extra small-sized model)
=========================================
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and first released in this... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe MobileViT model was pretrained on ImageNet-1k, a dataset consistin... | [
"TAGS\n#transformers #pytorch #tf #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one ... |
image-segmentation | transformers |
# MobileViT + DeepLabV3 (small-sized model)
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this rep... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["pascal-voc"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg", "example_title": "Cat"}]} | apple/deeplabv3-mobilevit-small | null | [
"transformers",
"pytorch",
"tf",
"coreml",
"mobilevit",
"vision",
"image-segmentation",
"dataset:pascal-voc",
"arxiv:2110.02178",
"arxiv:1706.05587",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-30T11:49:24+00:00 | [
"2110.02178",
"1706.05587"
] | [] | TAGS
#transformers #pytorch #tf #coreml #mobilevit #vision #image-segmentation #dataset-pascal-voc #arxiv-2110.02178 #arxiv-1706.05587 #license-other #endpoints_compatible #has_space #region-us
| MobileViT + DeepLabV3 (small-sized model)
=========================================
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and first released in this ... | [
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe MobileViT + DeepLabV3 model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the PASCA... | [
"TAGS\n#transformers #pytorch #tf #coreml #mobilevit #vision #image-segmentation #dataset-pascal-voc #arxiv-2110.02178 #arxiv-1706.05587 #license-other #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support ... |
image-segmentation | transformers |
# MobileViT + DeepLabV3 (extra small-sized model)
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [th... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["pascal-voc"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg", "example_title": "Cat"}]} | apple/deeplabv3-mobilevit-x-small | null | [
"transformers",
"pytorch",
"tf",
"coreml",
"mobilevit",
"vision",
"image-segmentation",
"dataset:pascal-voc",
"arxiv:2110.02178",
"arxiv:1706.05587",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T11:50:54+00:00 | [
"2110.02178",
"1706.05587"
] | [] | TAGS
#transformers #pytorch #tf #coreml #mobilevit #vision #image-segmentation #dataset-pascal-voc #arxiv-2110.02178 #arxiv-1706.05587 #license-other #endpoints_compatible #region-us
| MobileViT + DeepLabV3 (extra small-sized model)
===============================================
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and first relea... | [
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe MobileViT + DeepLabV3 model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the PASCA... | [
"TAGS\n#transformers #pytorch #tf #coreml #mobilevit #vision #image-segmentation #dataset-pascal-voc #arxiv-2110.02178 #arxiv-1706.05587 #license-other #endpoints_compatible #region-us \n",
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\... |
image-segmentation | transformers |
# MobileViT + DeepLabV3 (extra extra small-sized model)
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released ... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["pascal-voc"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg", "example_title": "Cat"}]} | apple/deeplabv3-mobilevit-xx-small | null | [
"transformers",
"pytorch",
"tf",
"coreml",
"mobilevit",
"vision",
"image-segmentation",
"dataset:pascal-voc",
"arxiv:2110.02178",
"arxiv:1706.05587",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-30T11:52:28+00:00 | [
"2110.02178",
"1706.05587"
] | [] | TAGS
#transformers #pytorch #tf #coreml #mobilevit #vision #image-segmentation #dataset-pascal-voc #arxiv-2110.02178 #arxiv-1706.05587 #license-other #endpoints_compatible #has_space #region-us
| MobileViT + DeepLabV3 (extra extra small-sized model)
=====================================================
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and... | [
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe MobileViT + DeepLabV3 model was pretrained on ImageNet-1k, a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the PASCA... | [
"TAGS\n#transformers #pytorch #tf #coreml #mobilevit #vision #image-segmentation #dataset-pascal-voc #arxiv-2110.02178 #arxiv-1706.05587 #license-other #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support ... |
sentence-similarity | sentence-transformers |
# PatentSBERTa_V2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy wh... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "A method for wireless charging using magnetic resonance", "sentences": ["Wireless power transfer through inductive coupling", "A new compound for pharma... | AAUBS/PatentSBERTa_V2 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T12:05:57+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# PatentSBERTa_V2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you ca... | [
"# PatentSBERTa_V2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# PatentSBERTa_V2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering o... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-awesome-model-2
This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italia... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-awesome-model-2", "results": []}]} | Fra96/my-awesome-model-2 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T13:04:37+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| my-awesome-model-2
==================
This model is a fine-tuned version of dbmdz/bert-base-italian-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2839
* Validation Loss: 0.2098
* Epoch: 2
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_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... | Battu007/V4_PPO2_LunarLander_v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T13:22:56+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... |
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. -->
# mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books
This model is a fine-tuned version of [Nar... | {"tags": ["generated_from_trainer"], "datasets": ["opus_books"], "model-index": [{"name": "mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books", "results": []}]} | VanessaSchenkel/mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T13:28:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-opus_books #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-50-finetuned-opus-en-pt-translation-finetuned-en-to-pt-dataset-opus-books
=====================================================================================
This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on the opus\_books dataset.
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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-opus_books #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... |
null | transformers |
## Multilingual-clip: XLM-Roberta-Large-Vit-L-14
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-L-14` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](http... | {"language": ["multilingual", "af", "sq", "am", "ar", "az", "bn", "bs", "bg", "ca", "zh", "hr", "cs", "da", "nl", "en", "et", "fr", "de", "el", "hi", "hu", "is", "id", "it", "ja", "mk", "ml", "mr", "pl", "pt", "ro", "ru", "sr", "sl", "es", "sw", "sv", "tl", "te", "tr", "tk", "uk", "ur", "ug", "uz", "vi", "xh"]} | M-CLIP/XLM-Roberta-Large-Vit-L-14 | null | [
"transformers",
"pytorch",
"tf",
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr... | null | 2022-05-30T13:35:41+00:00 | [] | [
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"t... | TAGS
#transformers #pytorch #tf #multilingual #af #sq #am #ar #az #bn #bs #bg #ca #zh #hr #cs #da #nl #en #et #fr #de #el #hi #hu #is #id #it #ja #mk #ml #mr #pl #pt #ro #ru #sr #sl #es #sw #sv #tl #te #tr #tk #uk #ur #ug #uz #vi #xh #endpoints_compatible #has_space #region-us
| Multilingual-clip: XLM-Roberta-Large-Vit-L-14
---------------------------------------------
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model 'ViT-L-14' can be retrieved via instructions found o... | [] | [
"TAGS\n#transformers #pytorch #tf #multilingual #af #sq #am #ar #az #bn #bs #bg #ca #zh #hr #cs #da #nl #en #et #fr #de #el #hi #hu #is #id #it #ja #mk #ml #mr #pl #pt #ro #ru #sr #sl #es #sw #sv #tl #te #tr #tk #uk #ur #ug #uz #vi #xh #endpoints_compatible #has_space #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... | ianspektor/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T13:41:39+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... |
image-segmentation | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras", "tags": ["image-segmentation"]} | merve/deeplab-v3 | null | [
"keras",
"tensorboard",
"image-segmentation",
"region:us"
] | null | 2022-05-30T13:49:02+00:00 | [] | [] | TAGS
#keras #tensorboard #image-segmentation #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #tensorboard #image-segmentation #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cewinharhar/iceCream
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieve... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "cewinharhar/iceCream", "results": []}]} | cewinharhar/iceCream | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T14:12:45+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| cewinharhar/iceCream
====================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1909
* Validation Loss: 3.0925
* Epoch: 92
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
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. -->
# clementgyj/roberta-finetuned-squad-50k
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "clementgyj/roberta-finetuned-squad-50k", "results": []}]} | clementgyj/roberta-finetuned-squad-50k | null | [
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T14:16:42+00:00 | [] | [] | TAGS
#transformers #tf #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
| clementgyj/roberta-finetuned-squad-50k
======================================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5281
* Epoch: 2
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9462, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'Pol... |
text-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-5000-samples3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuning-sentiment-model-5000-samples3", "results": []}]} | joebobby/finetuning-sentiment-model-5000-samples3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T14:24:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-5000-samples3
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# finetuning-sentiment-model-5000-samples3\n\nThis model is a fine-tuned version of bert-base-uncased 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",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-5000-samples3\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model ... |
multiple-choice | 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-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | nadiaqutaiba/bert-base-uncased-finetuned-swag | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T14:31:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# bert-base-uncased-finetuned-swag\n\nThis model is a fine-tuned version of bert-base-uncased on the swag dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-swag\n\nThis model is a fine-tuned version of bert-base-uncased on the swag dataset.",
"## Model description\n\nMore in... |
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="Tinchoroman/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Tinchoroman/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T14:41:37+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"
] |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | Mikey8943/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T15:14:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Bleu: 50.1695
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.9643\n- Bleu: 50.1695",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `imdanboy/kss_tts_train_jets_raw_phn_korean_cleaner_korean_jaso_train.total_count.ave`
This model was trained by satoshi.2020 using kss recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c173c30930631731e6836c274a591ad5717... | {"language": "ko", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["kss"]} | imdanboy/kss_tts_train_jets_raw_phn_korean_cleaner_korean_jaso_train.total_count.ave | null | [
"espnet",
"audio",
"text-to-speech",
"ko",
"dataset:kss",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-30T15:24:15+00:00 | [
"1804.00015"
] | [
"ko"
] | TAGS
#espnet #audio #text-to-speech #ko #dataset-kss #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'imdanboy/kss_tts_train_jets_raw_phn_korean_cleaner_korean_jaso_train.total_count.ave'
This model was trained by satoshi.2020 using kss recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
... | [
"## ESPnet2 TTS model",
"### 'imdanboy/kss_tts_train_jets_raw_phn_korean_cleaner_korean_jaso_train.total_count.ave'\n\nThis model was trained by satoshi.2020 using kss recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ... | [
"TAGS\n#espnet #audio #text-to-speech #ko #dataset-kss #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'imdanboy/kss_tts_train_jets_raw_phn_korean_cleaner_korean_jaso_train.total_count.ave'\n\nThis model was trained by satoshi.2020 using kss recipe in espnet.",
"### Demo: How... |
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/1362800111118659591/O6gx... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/erinkhoo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T15:48:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
erinkhoo.x
@erinkhoo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT finetuned for sentiment analysis on DBRD
This is a finetuned model based on [RobBERT (v2)](https://huggingface.co/pdelobelle... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT"], "datasets": ["dbrd"], "widget": [{"text": "Ik erken dat dit een boek is, daarmee is alles gezegd."}, {"text": "Prachtig verhaal, heel mooi verteld en een verrassend einde... Een topper!"}], "thumbnail": "https://github.com/iPieter/... | DTAI-KULeuven/robbert-v2-dutch-sentiment | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"dataset:dbrd",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-30T15:53:44+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #Dutch #Flemish #RoBERTa #RobBERT #nl #dataset-dbrd #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
RobBERT finetuned for sentiment analysis on DBRD
================================================
This is a finetuned model based on RobBERT (v2). We used DBRD, which consists of book reviews from URL. Hence our example sentences about books. We did some limited experiments to test if this also work... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #Dutch #Flemish #RoBERTa #RobBERT #nl #dataset-dbrd #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jijo/opus-mt-en-ml-finetuned-en-to-ml
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ml](https://huggingface.co/Helsin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jijo/opus-mt-en-ml-finetuned-en-to-ml", "results": []}]} | jijo/opus-mt-en-ml-finetuned-en-to-ml | null | [
"transformers",
"tf",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T16:09:58+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jijo/opus-mt-en-ml-finetuned-en-to-ml
=====================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ml on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5102
* Validation Loss: 2.2501
* Train Bleu: 3.8750
* Train Gen Len: 20.6042
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 0.0002, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",... | [
"TAGS\n#transformers #tf #tensorboard #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | miesnerjacob/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T16:17:42+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
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="voleg44/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"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": ... | voleg44/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T16:25:56+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
fill-mask | transformers |
<!-- 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-scratch-powo_mgh_pt
This model is a fine-tuned version of [](https://huggingface.co/) on the None datase... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-scratch-powo_mgh_pt", "results": []}]} | ViktorDo/distilbert-base-uncased-scratch-powo_mgh_pt | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T16:36:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-scratch-powo\_mgh\_pt
=============================================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0408
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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: 5e-05\n* train\\_batch\\_size: 5\n* eval\\_batch... |
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="PraveenKishore/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add addition... | {"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": ... | PraveenKishore/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T16:46:51+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="PraveenKishore/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False ... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/... | PraveenKishore/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T16:53:49+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-science-v3-e1
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-cnn-science-v3-e1", "results": []}]} | theojolliffe/bart-cnn-science-v3-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T17:01:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-science-v3-e1
======================
This model is a fine-tuned version of theojolliffe/bart-cnn-science on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nouman10/robertabase-claims-3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown ... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nouman10/robertabase-claims-3", "results": []}]} | nouman10/robertabase-claims-3 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T17:22:34+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nouman10/robertabase-claims-3
=============================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0310
* Validation Loss: 0.1227
* Epoch: 1
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\... |
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-tweets
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion-tweets", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "... | Yuliya-HV/distilbert-base-uncased-finetuned-emotion-tweets | 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 | 2022-05-30T17:23:11+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-tweets
================================================
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: 0.1572
* Accuracy: 0.9355
* F1: 0.9359
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# deberta-base-combined-squad1-aqa-newsqa-50
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-newsqa-50", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-newsqa-50 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T17:31:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-newsqa-50
==========================================
This model is a fine-tuned version of microsoft/deberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7756
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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 #deberta #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: 12\n* eval\\_batch\\... |
summarization | transformers |
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schlute... | {"language": ["da"], "tags": ["summarization"], "widget": [{"text": "Det nye studie Cognitive Science p\u00e5 Aarhus Universitet, som i \u00e5r havde \u00d8stjyllands h\u00f8jeste adgangskrav p\u00e5 11,7 i karaktergennemsnit, udkl\u00e6kker det f\u00f8rste hold bachelorer til sommer.\nMen n\u00e5r de skal l\u00e6se vi... | sarakolding/daT5-summariser | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"da",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T18:12:46+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #da #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schlute... | [] | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #da #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-segmentation | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras", "tags": ["image-segmentation"]} | keja/deeplab-v3 | null | [
"keras",
"tensorboard",
"image-segmentation",
"region:us"
] | null | 2022-05-30T18:14:45+00:00 | [] | [] | TAGS
#keras #tensorboard #image-segmentation #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #tensorboard #image-segmentation #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\... |
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-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_polarity"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_polarity", "t... | daniel780/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_polarity",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T19:23:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4356
- Accuracy: 0.8067
- F1: 0.8079
## Model description
More information needed
## Intended uses & limitatio... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the amazon_polarity dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4356\n- Accuracy: 0.8067\n- F1: 0.8079",
"## Model description\n\nMore information needed",
"## Intende... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-b... |
text-classification | transformers |
# Roberta-Pubhealth model
This model is a fine-tuned version of [RoBERTa Base](https://huggingface.co/roberta-base) on the health_fact dataset.
It achieves the following results on the evaluation set:
- micro f1 (accuracy): 0.7137
- macro f1: 0.6056
- weighted f1: 0.7106
- samples predicted per second: 9.31
## Datas... | {} | hhhhzy/roberta-pubhealth | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T19:37:57+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# Roberta-Pubhealth model
This model is a fine-tuned version of RoBERTa Base on the health_fact dataset.
It achieves the following results on the evaluation set:
- micro f1 (accuracy): 0.7137
- macro f1: 0.6056
- weighted f1: 0.7106
- samples predicted per second: 9.31
## Dataset desctiption
PUBHEALTHis a comprehens... | [
"# Roberta-Pubhealth model\n\nThis model is a fine-tuned version of RoBERTa Base on the health_fact dataset.\nIt achieves the following results on the evaluation set:\n- micro f1 (accuracy): 0.7137\n- macro f1: 0.6056\n- weighted f1: 0.7106\n- samples predicted per second: 9.31",
"## Dataset desctiption\nPUBHEALT... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta-Pubhealth model\n\nThis model is a fine-tuned version of RoBERTa Base on the health_fact dataset.\nIt achieves the following results on the evaluation set:\n- micro f1 (accuracy): 0.7... |
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="ykirpichev/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | ykirpichev/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T19:45:16+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot]( https://arxiv.org/abs/2004.13637)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural ... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | ElMuchoDingDong/AudreyBotBlenderBot | null | [
"transformers",
"pytorch",
"tf",
"jax",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:2004.13637",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T19:46:43+00:00 | [
"2004.13637"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-2004.13637 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
"TAGS\n#transformers #pytorch #tf #jax #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-2004.13637 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-science-v3-e2
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-science-v3-e2", "results": []}]} | theojolliffe/bart-cnn-science-v3-e2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T19:49:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-science-v3-e2
======================
This model is a fine-tuned version of theojolliffe/bart-cnn-science on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9352
* Rouge1: 52.5497
* Rouge2: 32.5507
* Rougel: 35.0014
* Rougelsum: 50.0575
* Gen Len: 141.5741
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ykirpichev/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slipper... | {"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 +/... | ykirpichev/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-30T19:54:46+00:00 | [] | [] | TAGS
#Taxi-v3 #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#Taxi-v3 #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-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. -->
# beto-sentiment-analysis-finetuned-onpremise
This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "beto-sentiment-analysis-finetuned-onpremise", "results": []}]} | Cristian-dcg/beto-sentiment-analysis-finetuned-onpremise | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T20:10:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| beto-sentiment-analysis-finetuned-onpremise
===========================================
This model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7939
* Accuracy: 0.8301
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_... |
text-generation | transformers | # My Story model
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. He found out he couldn't swim. Arthur went to the doctor. The doctor said he could not swim. Arthur decides to stay at home and get his prescription.
Arthur goes to the beach. Arthur went to the beach one day. He was excited... | {} | jppaolim/v37_Best2Epoch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T20:31:59+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. He found out he couldn't swim. Arthur went to the doctor. The doctor said he could not swim. Arthur decides to stay at home and get his prescription.
Arthur goes to the beach. Arthur went to the beach one day. He was excited... | [
"# My Story model\nArthur goes to the beach. Arthur wanted to go to the beach with his friends. He found out he couldn't swim. Arthur went to the doctor. The doctor said he could not swim. Arthur decides to stay at home and get his prescription. \nArthur goes to the beach. Arthur went to the beach one day. He was ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\nArthur goes to the beach. Arthur wanted to go to the beach with his friends. He found out he couldn't swim. Arthur went to the doctor. The doctor said he c... |
null | transformers |
## Multilingual-clip: XLM-Roberta-Large-Vit-B-16Plus
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `Vit-B-16Plus` can be retrieved via instructions found on `mlfoundations` [open_clip reposi... | {"language": ["multilingual", "af", "sq", "am", "ar", "az", "bn", "bs", "bg", "ca", "zh", "hr", "cs", "da", "nl", "en", "et", "fr", "de", "el", "hi", "hu", "is", "id", "it", "ja", "mk", "ml", "mr", "pl", "pt", "ro", "ru", "sr", "sl", "es", "sw", "sv", "tl", "te", "tr", "tk", "uk", "ur", "ug", "uz", "vi", "xh"]} | M-CLIP/XLM-Roberta-Large-Vit-B-16Plus | null | [
"transformers",
"pytorch",
"tf",
"multilingual",
"af",
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"bn",
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"ca",
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"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr... | null | 2022-05-30T20:33:14+00:00 | [] | [
"multilingual",
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"sq",
"am",
"ar",
"az",
"bn",
"bs",
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"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"t... | TAGS
#transformers #pytorch #tf #multilingual #af #sq #am #ar #az #bn #bs #bg #ca #zh #hr #cs #da #nl #en #et #fr #de #el #hi #hu #is #id #it #ja #mk #ml #mr #pl #pt #ro #ru #sr #sl #es #sw #sv #tl #te #tr #tk #uk #ur #ug #uz #vi #xh #endpoints_compatible #has_space #region-us
| Multilingual-clip: XLM-Roberta-Large-Vit-B-16Plus
-------------------------------------------------
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model 'Vit-B-16Plus' can be retrieved via instruct... | [] | [
"TAGS\n#transformers #pytorch #tf #multilingual #af #sq #am #ar #az #bn #bs #bg #ca #zh #hr #cs #da #nl #en #et #fr #de #el #hi #hu #is #id #it #ja #mk #ml #mr #pl #pt #ro #ru #sr #sl #es #sw #sv #tl #te #tr #tk #uk #ur #ug #uz #vi #xh #endpoints_compatible #has_space #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. -->
# results
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H3... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "results", "results": []}]} | haritzpuerto/MiniLM-L12-H384-uncased-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T20:35:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* exact\_match: 77.57805108798486
* f1: 85.73943867549627
* Loss: 1.0744
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* ev... |
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/1387065127208247299/bni0... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/billieeilish-nakedbibii-unjaded_jade | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T20:38:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AI CYBORG
billie eilish & BIBI & Jade Bowler
@billieeilish-nakedbibii-unjaded\_jade
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... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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/1105554414427885569/Xkyf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/sun_soony-unjaded_jade-veganhollyg/1654724750416/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/sun_soony-unjaded_jade-veganhollyg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T20:50:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Jade Bowler & soony & Holly Gabrielle
@sun\_soony-unjaded\_jade-veganhollyg
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 develop... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
PragS1: Pragmatic Masked Language Modeling with Hashtag_end dataset followed by Emoji-Based Surrogate Fine-Tuning
You can load this model and use for downstream fine-tuning. For example:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('UB... | {"license": "cc-by-nc-3.0"} | UBC-NLP/prags1 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-nc-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T21:37:33+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #license-cc-by-nc-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
PragS1: Pragmatic Masked Language Modeling with Hashtag_end dataset followed by Emoji-Based Surrogate Fine-Tuning
You can load this model and use for downstream fine-tuning. For example:
More details are in our paper:
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #license-cc-by-nc-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
PragS2: Pragmatic Masked Language Modeling with Emoji_any dataset followed by Hashtag-Based Surrogate Fine-Tuning
You can load this model and use for downstream fine-tuning. For example:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('UB... | {"license": "cc-by-nc-3.0"} | UBC-NLP/prags2 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-nc-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T21:47:15+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #license-cc-by-nc-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
PragS2: Pragmatic Masked Language Modeling with Emoji_any dataset followed by Hashtag-Based Surrogate Fine-Tuning
You can load this model and use for downstream fine-tuning. For example:
More details are in our paper:
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #license-cc-by-nc-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers | mBERT base uncased model trained on 50% SQUAD data. This model can further be used to fine-tune using dev data for QA system on a specific language. The process is similar to what was followed in MLQA paper[https://aclanthology.org/2020.acl-main.421.pdf].
| {} | roshnir/bert-multi-uncased-trained-squadv2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T22:00:51+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| mBERT base uncased model trained on 50% SQUAD data. This model can further be used to fine-tune using dev data for QA system on a specific language. The process is similar to what was followed in MLQA paper[URL
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# T5-base model trained for text paraphrase
You can load this model by:
```python
from transformers import T5ForConditionalGeneration,T5TokenizerFast
model = T5ForConditionalGeneration.from_pretrained(model_name_or_path)
tokenizer = T5TokenizerFast.from_pretrained(model_name_or_path)
```
A prefix "paraphrase: " sh... | {"license": "cc-by-nc-3.0"} | UBC-NLP/ptsm_t5_paraphraser | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.04611",
"license:cc-by-nc-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T22:05:23+00:00 | [
"2204.04611"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2204.04611 #license-cc-by-nc-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-base model trained for text paraphrase
You can load this model by:
A prefix "paraphrase: " should be added in font of the input sequence, i.e.:
You can find our scripts for generation in our project GitHub
Please find more training details in our paper:
Decay No More: A Persistent Twitter Dataset for Learni... | [
"# T5-base model trained for text paraphrase \n\nYou can load this model by:\n\n\nA prefix \"paraphrase: \" should be added in font of the input sequence, i.e.:\n\nYou can find our scripts for generation in our project GitHub\nPlease find more training details in our paper:\n\nDecay No More: A Persistent Twitter Da... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2204.04611 #license-cc-by-nc-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-base model trained for text paraphrase \n\nYou can load this model by:\n\n\nA prefix \"paraphrase: \" should be added in font... |
text-to-speech | fairseq | # fastspeech2-mf1 | {"language": "en", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "task": "text-to-speech", "widget": [{"text": "Hello, this is a test run.", "example_title": "Hello, this is a test run."}]} | Voicemod/fastspeech2-mf1 | null | [
"fairseq",
"audio",
"text-to-speech",
"en",
"has_space",
"region:us"
] | null | 2022-05-30T22:05:40+00:00 | [] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #en #has_space #region-us
| # fastspeech2-mf1 | [
"# fastspeech2-mf1"
] | [
"TAGS\n#fairseq #audio #text-to-speech #en #has_space #region-us \n",
"# fastspeech2-mf1"
] |
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. -->
# deberta-base-combined-squad1-aqa-newsqa-50-and-newsqa-50
This model is a fine-tuned version of [stevemobs/deberta-base-combined-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa-newsqa-50-and-newsqa-50", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa-newsqa-50-and-newsqa-50 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T22:18:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa-newsqa-50-and-newsqa-50
========================================================
This model is a fine-tuned version of stevemobs/deberta-base-combined-squad1-aqa-newsqa-50 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4881
Model descriptio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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 #deberta #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: 12\n* eval\\_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... | geninhu/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-30T22:31:13+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... |
text-generation | transformers |
#rickfinal | {"tags": ["conversational"]} | stfuowned/rickfinal | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T22:51:54+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#rickfinal | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln49")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln49")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln49 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T23:18:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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. -->
# xtreme_s_xlsr_300m_minds14.en-US_2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"language": ["en-US"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_minds14.en-US_2", "results": []}]} | Splend1dchan/xtreme_s_xlsr_300m_minds14.en-US_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-30T23:39:11+00:00 | [] | [
"en-US"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_minds14.en-US\_2
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5685
* F1: 0.8747
* Accuracy: 0.8759
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
text-generation | transformers |
#Homer Simpson DialogGPT Model | {"tags": ["conversational"]} | DuskSigma/DialogGPTHomerSimpson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-30T23:47:34+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Homer Simpson DialogGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Question generation using T5 transformer trained on SQuAD
<h2> <i>Input format: context: "..." answer(optional): "..." </i></h2>
Import the pretrained model as well as tokenizer:
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/... | {"license": "afl-3.0"} | AbhilashDatta/T5_qgen-squad_v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T00:25:11+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Question generation using T5 transformer trained on SQuAD
<h2> <i>Input format: context: "..." answer(optional): "..." </i></h2>
Import the pretrained model as well as tokenizer:
Then use the tokenizer to encode/decode and model to generate:
Output:
| [
"# Question generation using T5 transformer trained on SQuAD\n\n<h2> <i>Input format: context: \"...\" answer(optional): \"...\" </i></h2>\n\nImport the pretrained model as well as tokenizer:\n\n\nThen use the tokenizer to encode/decode and model to generate: \n\n\n\nOutput:"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Question generation using T5 transformer trained on SQuAD\n\n<h2> <i>Input format: context: \"...\" answer(optional): \"...\" </i></h2>\n\nImport the p... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5_fill_puntuation
This model is a fine-tuned version of [jamie613/mt5_fill_puntuation](https://huggingface.co/jamie613/mt5_fil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mt5_fill_puntuation", "results": []}]} | jamie613/mt5_fill_puntuation | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T00:33:50+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5\_fill\_puntuation
=====================
This model is a fine-tuned version of jamie613/mt5\_fill\_puntuation on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0717
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #mt5 #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: 5e-05\n* t... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_300m_minds14.en-US_2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"language": ["en-US"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_minds14.en-US_2", "results": []}]} | Splend1dchan/xtreme_s_xlsr_300m_mt5-small_minds14.en-US_my | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T00:52:30+00:00 | [] | [
"en-US"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
|
# xtreme_s_xlsr_300m_minds14.en-US_2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m--concat-->mt5 on the GOOGLE/XTREME_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5685
- F1: 0.83333
- Accuracy: 0.83258
## Model description
More information need... | [
"# xtreme_s_xlsr_300m_minds14.en-US_2\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m--concat-->mt5 on the GOOGLE/XTREME_S - MINDS14.EN-US dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5685\n- F1: 0.83333\n- Accuracy: 0.83258",
"## Model description\n\nMore in... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"# xtreme_s_xlsr_300m_minds14.en-US_2\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m--concat-->mt5 on the GOOGLE... |
null | null | # Model-UBNet-Segmentation
UBNet architectural model for X-ray image segmentation of Brain MRI. We also build MRI Image Analysis System based on PyQt for diagnose Covid-19 patient using UBNet-Seg architecture.
| {"license": "cc"} | masdar/UBNet_Segmentation | null | [
"license:cc",
"region:us"
] | null | 2022-05-31T01:17:00+00:00 | [] | [] | TAGS
#license-cc #region-us
| # Model-UBNet-Segmentation
UBNet architectural model for X-ray image segmentation of Brain MRI. We also build MRI Image Analysis System based on PyQt for diagnose Covid-19 patient using UBNet-Seg architecture.
| [
"# Model-UBNet-Segmentation\nUBNet architectural model for X-ray image segmentation of Brain MRI. We also build MRI Image Analysis System based on PyQt for diagnose Covid-19 patient using UBNet-Seg architecture."
] | [
"TAGS\n#license-cc #region-us \n",
"# Model-UBNet-Segmentation\nUBNet architectural model for X-ray image segmentation of Brain MRI. We also build MRI Image Analysis System based on PyQt for diagnose Covid-19 patient using UBNet-Seg architecture."
] |
text2text-generation | transformers | # Kogi Python-Code Generation Model | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["mc4"]} | kkuramitsu/kogi-mt5-test | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"dataset:mc4",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T01:32:23+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-mc4 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| # Kogi Python-Code Generation Model | [
"# Kogi Python-Code Generation Model"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-mc4 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# Kogi Python-Code Generation Model"
] |
automatic-speech-recognition | transformers | Wav2vec2 model trained with audio clips from Arabic shows using the Emirati dialect. | {"license": "gpl-3.0"} | eabayed/wav2vec2emiratidialict_1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T01:48:44+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #license-gpl-3.0 #endpoints_compatible #region-us
| Wav2vec2 model trained with audio clips from Arabic shows using the Emirati dialect. | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #license-gpl-3.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
GPT-2 chatbot - talk to Sonic (improved?) | {"tags": ["conversational"]} | hireddivas/dialoGPT-small-sonic2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T02:50:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2 chatbot - talk to Sonic (improved?) | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_w2v2_minds14.en-US
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook... | {"language": ["en-US"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_w2v2_minds14.en-US", "results": []}]} | Splend1dchan/xtreme_s_w2v2_minds14.en-US | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T03:05:13+00:00 | [] | [
"en-US"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_w2v2\_minds14.en-US
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the GOOGLE/XTREME\_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5337
* F1: 0.9144
* Accuracy: 0.9113
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_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. -->
# xlm-roberta-base-finetuned-removed-0530
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robe... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-removed-0530", "results": []}]} | YeRyeongLee/xlm-roberta-base-finetuned-removed-0530 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T04:12:27+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-removed-0530
=======================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9944
* Accuracy: 0.8717
* F1: 0.8719
Model description
-----------------
More informati... | [
"### 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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_w2v2_t5lephone-small_minds14.en-US
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://hugging... | {"language": ["en-US"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_w2v2_t5lephone-small_minds14.en-US", "results": []}]} | Splend1dchan/xtreme_s_w2v2_t5lephone-small_minds14.en-US | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T04:12:55+00:00 | [] | [
"en-US"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_w2v2\_t5lephone-small\_minds14.en-US
===============================================
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the GOOGLE/XTREME\_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5203
* F1: 0.7526
* Accuracy: 0.7518
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_b... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | N0NAne/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T04:15:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-vi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-vi-colab", "results": []}]} | bdh240901/wav2vec2-large-xls-r-300m-vi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-31T04:20:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-vi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# wav2vec2-large-xls-r-300m-vi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-vi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice... |
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="voleg44/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 +/... | voleg44/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-31T04:32:28+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GauravDesai85/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"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": ... | GauravDesai85/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-31T04:59:27+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-iir-en-finetuned-fa-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-iir-en](https://huggingface.co/Hel... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_infopankki"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-iir-en-finetuned-fa-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "opus_infopankki"... | PontifexMaximus/opus-mt-iir-en-finetuned-fa-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-31T05:08:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-iir-en-finetuned-fa-to-en
=================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-iir-en on the opus\_infopankki dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0968
* Bleu: 36.687
* Gen Len: 16.039
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during traini... |
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. -->
# mt5-base-finetuned-summarization-V2
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-summarization-V2", "results": []}]} | GiordanoB/mt5-base-finetuned-summarization-V2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-31T05:36:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-summarization-V2
===================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 8.3409
* Rouge1: 6.1259
* Rouge2: 1.4637
* Rougel: 5.3192
* Rougelsum: 5.7739
* Gen Len: 9.9286
Mode... | [
"### 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 #mt5 #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\\_rat... |
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