pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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... | lucaordronneau/lo-ppo-LunarLander-v2_1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T11:46:03+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-arfa
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the N... | {"license": "apache-2.0", "tags": ["summarization", "arabic", "ar", "fa", "persian", "mt5", "Abstractive Summarization", "generated_from_trainer"], "model-index": [{"name": "mt5-base-finetuned-arfa", "results": []}]} | eslamxm/mt5-base-finetuned-arfa | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"arabic",
"ar",
"fa",
"persian",
"Abstractive Summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region... | null | 2022-05-22T11:55:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #arabic #ar #fa #persian #Abstractive Summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-arfa
=======================
This model is a fine-tuned version of google/mt5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1784
* Rouge-1: 25.68
* Rouge-2: 11.8
* Rouge-l: 22.99
* Gen Len: 18.99
* Bertscore: 71.78
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #arabic #ar #fa #persian #Abstractive Summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
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... | venushong667/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T11:56:02+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | Leizhang/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T12:19:10+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1631
* F1: 0.8579
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
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="diskshima/deep-rl-class-unit02-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "deep-rl-class-unit02-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_sl... | diskshima/deep-rl-class-unit02-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T12:32:38+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"
] |
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. -->
# wav2vec2-4
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-4", "results": []}]} | chrisvinsen/wav2vec2-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T12:37:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-4
==========
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: 3.1442
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* 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: 3... |
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-xlsr-turkish-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-turkish-demo-colab", "results": []}]} | masoumehb/wav2vec2-large-xlsr-turkish-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T12:40:39+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-xlsr-turkish-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training p... | [
"# wav2vec2-large-xlsr-turkish-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 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... | [
"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-xlsr-turkish-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the commo... |
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="diskshima/deep-rl-class-unit02-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_s... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "deep-rl-class-unit02-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward"... | diskshima/deep-rl-class-unit02-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T12:45:31+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | spasis/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T13:03:14+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"... |
null | transformers |
This "model" holds the weights for the positional invariance transformation used in the paper Fine-Grained Controllable Text Generation Using Non-Residual Prompting. It is loaded automatically in the GitHub repository below, if you want to try it out!
Paper: https://aclanthology.org/2022.acl-long.471
Official GitHub... | {"title": "README", "emoji": "\ud83d\ude3b", "colorFrom": "indigo", "colorTo": "purple", "sdk": "gradio", "pinned": false} | Non-Residual-Prompting/GPT2-Large-Post-Transformation | null | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T13:48:37+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #region-us
|
This "model" holds the weights for the positional invariance transformation used in the paper Fine-Grained Controllable Text Generation Using Non-Residual Prompting. It is loaded automatically in the GitHub repository below, if you want to try it out!
Paper: URL
Official GitHub: URL | [] | [
"TAGS\n#transformers #tf #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder", "nielsr/eurosat-demo"], "metrics": ["accuracy"], "widget": [{"src": "https://drive.google.com/uc?id=1trKgvkMRQ3BB0VcqnDwmieLxXhWmS8rq", "example_title": "Annual Crop"}, {"src": "https://drive.google.com/uc?id=1kWQbPNHVa_JscS0age5... | nickmuchi/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"dataset:nielsr/eurosat-demo",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",... | null | 2022-05-22T13:56:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #dataset-nielsr/eurosat-demo #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0536
* Accuracy: 0.9848
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #dataset-nielsr/eurosat-demo #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparam... |
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="esh/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribute... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | esh/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T14:32:26+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **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... | danieladejumo/ppo_lunar-lander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T14:43:08+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... |
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. -->
# mdeberta-v3-base-finetuned-recores
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/mic... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "mdeberta-v3-base-finetuned-recores", "results": []}]} | versae/mdeberta-v3-base-finetuned-recores | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"multiple-choice",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T14:47:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #multiple-choice #generated_from_trainer #license-mit #endpoints_compatible #region-us
| mdeberta-v3-base-finetuned-recores
==================================
This model is a fine-tuned version of microsoft/mdeberta-v3-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6094
* Accuracy: 0.2011
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #multiple-choice #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: 1\n* eval\\_batch\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | ocm/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T14:59:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #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 imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3107
- Accuracy: 0.8767
- F1: 0.8779
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3107\n- Accuracy: 0.8767\n- F1: 0.8779",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #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-base-uncased... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-cord-ner
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-cord-ner", "results": []}]} | renjithks/layoutlmv3-cord-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T15:13:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv3-cord-ner
===================
This model is a fine-tuned version of microsoft/layoutlmv3-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1215
* Precision: 0.9448
* Recall: 0.9520
* F1: 0.9484
* Accuracy: 0.9762
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-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: 5e-05\n* train\\_batch\\_size: 8\n* e... |
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-finetuned-squad1
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/de... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "deberta-base-finetuned-squad1", "results": []}]} | stevemobs/deberta-base-finetuned-squad1 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T15:18:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| deberta-base-finetuned-squad1
=============================
This model is a fine-tuned version of microsoft/deberta-base on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8037
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: 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 #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: 2e-05\n* train\\_batch\\_size: 12\n*... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="saeedHedayatian/q-FrozenLake-v1-4x4", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake... | saeedHedayatian/q-FrozenLake-v1-4x4 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T15:26:23+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Skvayzer/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T15:32:10+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | animalthemuppet/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T15:34:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0633
* Precision: 0.9306
* Recall: 0.9485
* F1: 0.9395
* Accuracy: 0.9859
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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="jonporterjones/q-FrozenLake-v1-4x4-not-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additi... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-not-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type"... | jonporterjones/q-FrozenLake-v1-4x4-not-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T15:35:56+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"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Sangita/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T15:37:27+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description... |
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="vukpetar/q-FrozenLake-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_sli... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-... | vukpetar/q-FrozenLake-v1 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T15:39:01+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 Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-news-v2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-news-v2", "results": []}]} | jbreuch/bert-news-v2 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T15:51:38+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-news-v2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# bert-news-v2\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-news-v2\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"... |
summarization | transformers | ### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Training hyperparameters
The following hyperparameters were used during tra... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["amazon_reviews_multi"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | sumedh/distilbart-cnn-12-6-amazonreviews | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T16:00:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optim... | [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_s... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for m... |
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="saeedHedayatian/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": "7.56 +/... | saeedHedayatian/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T16:25:51+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null |
This repository holds the finetuned weights for Tortoise v2 for the LJSpeech voice. It is a
good demonstration of how powerful fine-tuning Tortoise can be.
Usage:
- Clone Tortoise, jbetker/tortoise-tts-v2 or https://github.com/neonbjb/tortoise-tts
- Clone this repo to download weights
- Run any Tortoise script with t... | {"license": "apache-2.0"} | jbetker/tortoise-tts-finetuned-lj | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-05-22T16:34:59+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
This repository holds the finetuned weights for Tortoise v2 for the LJSpeech voice. It is a
good demonstration of how powerful fine-tuning Tortoise can be.
Usage:
- Clone Tortoise, jbetker/tortoise-tts-v2 or URL
- Clone this repo to download weights
- Run any Tortoise script with the flag '--model_dir=<path_to_where_... | [] | [
"TAGS\n#license-apache-2.0 #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. -->
# zh-adapter-32
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "zh-adapter-32", "results": []}]} | subhasisj/zh-adapter-32 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T16:50:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us
| zh-adapter-32
=============
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2154
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #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: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32... |
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="atsanda/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | atsanda/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T17:09:23+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# wav2vec2-5
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-5", "results": []}]} | chrisvinsen/wav2vec2-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T17:44:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-5
==========
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: 3.0700
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* 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: 0.003\n* train\\_batch\\_size: 32... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-squad-qgen
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "metrics": ["f1"], "model-index": [{"name": "t5-small-finetuned-squad-qgen", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "squad", "type": "squad", "args":... | mrm8488/t5-small-finetuned-squad-qgen | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-22T17:45:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-squad-qgen
=============================
This model is a fine-tuned version of t5-small on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3805
* Em: 0.0
* F1: 0.3643
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
null | null | Julián es un joven homosexual de 17 años
problemas que presenta con sus padres adoptivos
su promiscuidad
inasistencia a clase
venta de drogas a jóvenes | {} | luisamarrugo9/JULIAN | null | [
"region:us"
] | null | 2022-05-22T18:05:18+00:00 | [] | [] | TAGS
#region-us
| Julián es un joven homosexual de 17 años
problemas que presenta con sus padres adoptivos
su promiscuidad
inasistencia a clase
venta de drogas a jóvenes | [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="FreelancerFel/q-FrozenLake-v1-8x8-slippery", 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-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "F... | FreelancerFel/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T18:17:50+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 |
# mBART fine-tuned model for Czech abstractive summarization (AT2H-C)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Abstract + Text to Headl... | {"language": ["cs", "cs"], "license": "apache-2.0", "tags": ["abstractive summarization", "mbart-cc25", "Czech"], "datasets": ["private Czech News Center dataset news-based"], "metrics": ["rouge", "rougeraw"]} | krotima1/mbart-at2h-c | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"abstractive summarization",
"mbart-cc25",
"Czech",
"cs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T18:24:07+00:00 | [] | [
"cs",
"cs"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBART fine-tuned model for Czech abstractive summarization (AT2H-C)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating a one- or ... | [
"# mBART fine-tuned model for Czech abstractive summarization (AT2H-C)\nThis model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.",
"## Task\nThe model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBART fine-tuned model for Czech abstractive summarization (AT2H-C)\nThis model is a fine-tuned checkpoint of facebook/mba... |
text-generation | transformers | # Dummy model
Arthur goes to the beach. Arthur wanted to go to the beach. He thought it would be fun. He went and got a big towel. He set out to get on the water. He was so excited he fell in love with the ocean.
Arthur goes to the beach. Arthur and his family went to the beach on Saturday. They all wanted to go swim... | {} | jppaolim/v35_Baseline | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-22T18:24:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Dummy model
Arthur goes to the beach. Arthur wanted to go to the beach. He thought it would be fun. He went and got a big towel. He set out to get on the water. He was so excited he fell in love with the ocean.
Arthur goes to the beach. Arthur and his family went to the beach on Saturday. They all wanted to go swim... | [
"# Dummy model\nArthur goes to the beach. Arthur wanted to go to the beach. He thought it would be fun. He went and got a big towel. He set out to get on the water. He was so excited he fell in love with the ocean. \nArthur goes to the beach. Arthur and his family went to the beach on Saturday. They all wanted to ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dummy model\nArthur goes to the beach. Arthur wanted to go to the beach. He thought it would be fun. He went and got a big towel. He set out to get on the water. He was so... |
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="FreelancerFel/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | FreelancerFel/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T18:26:07+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | shankinson/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T18:41:09+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 |
# GPT2-small-czech-cs: a Language Model for Czech text generation (and more NLP tasks ...)
## Introduction
GPT2-small-czech-cs is a first experimental model for Czech language based on the GPT-2 small model.
It was trained on Czech Wikipedia using **Transfer Learning and Fine-tuning techniques** in about over a week... | {"language": "cs", "license": "cc-by-sa-4.0", "tags": ["text-generation", "transformers", "pytorch", "gpt2"], "datasets": ["wikipedia"], "widget": [{"text": "Um\u011bl\u00e1 inteligence pom\u016f\u017ee lidstvu p\u0159ekonat budouc\u00ed", "example_title": "Um\u011bl\u00e1 inteligence ..."}, {"text": "Sou\u010dasn\u00f... | spital/gpt2-small-czech-cs | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"cs",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-22T18:41:28+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #cs #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2-small-czech-cs: a Language Model for Czech text generation (and more NLP tasks ...)
## Introduction
GPT2-small-czech-cs is a first experimental model for Czech language based on the GPT-2 small model.
It was trained on Czech Wikipedia using Transfer Learning and Fine-tuning techniques in about over a weekend ... | [
"# GPT2-small-czech-cs: a Language Model for Czech text generation (and more NLP tasks ...)",
"## Introduction\nGPT2-small-czech-cs is a first experimental model for Czech language based on the GPT-2 small model.\n\nIt was trained on Czech Wikipedia using Transfer Learning and Fine-tuning techniques in about over... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #cs #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2-small-czech-cs: a Language Model for Czech text generation (and more NLP tasks ...)",
"## Introduction\nGPT2-small-czec... |
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="nbvanting/unit2-q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "unit2-q-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}... | nbvanting/unit2-q-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T18:47:50+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **Pendulum-v1**
This is a trained model of a **SAC** agent playing **Pendulum-v1**
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
reinforc... | {"library_name": "stable-baselines3", "tags": ["Pendulum-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pendulum-v1", "type": "Pendulum-v1"... | sb3/sac-Pendulum-v1 | null | [
"stable-baselines3",
"Pendulum-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T18:55:26+00:00 | [] | [] | TAGS
#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing Pendulum-v1
This is a trained model of a SAC agent playing Pendulum-v1
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 R... | [
"# SAC Agent playing Pendulum-v1\nThis is a trained model of a SAC agent playing Pendulum-v1\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.",
"## Us... | [
"TAGS\n#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing Pendulum-v1\nThis is a trained model of a SAC agent playing Pendulum-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
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="FreelancerFel/q-Taxi-v3-agg", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fal... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-agg", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "9.2... | FreelancerFel/q-Taxi-v3-agg | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T18:58:16+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"
] |
audio-classification | transformers |
# Prepare and importing
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, AutoModel, Wav2Vec2FeatureExtractor
import librosa
import numpy as np
def speech_file_to_array_fn(path, sampling_rate):
speech_array, _sampling_rate = torc... | {"language": "ru", "license": "mit", "tags": ["audio-classification", "audio", "emotion", "emotion-recognition", "emotion-classification", "speech"], "datasets": ["Aniemore/resd"], "model-index": [{"name": "XLS-R Wav2Vec2 For Russian Speech Emotion Classification by Nikita Davidchuk", "results": [{"task": {"type": "aud... | Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"audio-classification",
"audio",
"emotion",
"emotion-recognition",
"emotion-classification",
"speech",
"custom_code",
"ru",
"dataset:Aniemore/resd",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-05-22T19:10:59+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #audio-classification #audio #emotion #emotion-recognition #emotion-classification #speech #custom_code #ru #dataset-Aniemore/resd #license-mit #model-index #has_space #region-us
| Prepare and importing
=====================
Evoking:
========
Use case
========
Results
=======
s
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #audio-classification #audio #emotion #emotion-recognition #emotion-classification #speech #custom_code #ru #dataset-Aniemore/resd #license-mit #model-index #has_space #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Tanapon/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": ... | Tanapon/q-FrozenLake-v1-4x4-noslippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T19:27:40+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **TQC** Agent playing **Pendulum-v1**
This is a trained model of a **TQC** agent playing **Pendulum-v1**
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
reinforc... | {"library_name": "stable-baselines3", "tags": ["Pendulum-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TQC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pendulum-v1", "type": "Pendulum-v1"... | sb3/tqc-Pendulum-v1 | null | [
"stable-baselines3",
"Pendulum-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T19:35:42+00:00 | [] | [] | TAGS
#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TQC Agent playing Pendulum-v1
This is a trained model of a TQC agent playing Pendulum-v1
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 R... | [
"# TQC Agent playing Pendulum-v1\nThis is a trained model of a TQC agent playing Pendulum-v1\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.",
"## Us... | [
"TAGS\n#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TQC Agent playing Pendulum-v1\nThis is a trained model of a TQC agent playing Pendulum-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
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. -->
# mbart50-finetuned-multi30-en-to-de
This model is a fine-tuned version of [facebook/mbart-large-50-one-to-many-mmt](https://huggi... | {"tags": ["translation"], "metrics": ["bleu"], "model-index": [{"name": "mbart50-finetuned-multi30-en-to-de", "results": []}]} | RaphaelReinauer/mbart50-finetuned-multi30-en-to-de | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T19:39:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #translation #autotrain_compatible #endpoints_compatible #region-us
|
# mbart50-finetuned-multi30-en-to-de
This model is a fine-tuned version of facebook/mbart-large-50-one-to-many-mmt on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5946
- Bleu: 48.2650
## Model description
More information needed
## Intended uses & limitations
More informa... | [
"# mbart50-finetuned-multi30-en-to-de\n\nThis model is a fine-tuned version of facebook/mbart-large-50-one-to-many-mmt on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5946\n- Bleu: 48.2650",
"## Model description\n\nMore information needed",
"## Intended uses & limitati... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #translation #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart50-finetuned-multi30-en-to-de\n\nThis model is a fine-tuned version of facebook/mbart-large-50-one-to-many-mmt on the None dataset.\nIt achieves the following re... |
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... | Mugenor/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T19:51:37+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | transformers | This model, DeLADE+[CLS], is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone.
*[A Dense Representation Framework for Lexical and Semantic Matching](https://arxiv.org/pdf/2112.04666.pdf)* Sheng-Chieh Lin and Jimmy Lin.
You can find the usage of the model i... | {} | jacklin/DeLADE-CLS | null | [
"transformers",
"pytorch",
"arxiv:2112.04666",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T19:52:58+00:00 | [
"2112.04666"
] | [] | TAGS
#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us
| This model, DeLADE+[CLS], is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone.
*A Dense Representation Framework for Lexical and Semantic Matching* Sheng-Chieh Lin and Jimmy Lin.
You can find the usage of the model in our DHR repo: (1) Inference on MSMARCO... | [] | [
"TAGS\n#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# mBART fine-tuned model for Czech abstractive summarization (HT2A-C)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Headline + Text to Abstr... | {"language": ["cs", "cs"], "license": "apache-2.0", "tags": ["abstractive summarization", "mbart-cc25", "Czech"], "datasets": ["private CNC dataset news-based"], "metrics": ["rouge", "rougeraw"]} | krotima1/mbart-ht2a-c | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"abstractive summarization",
"mbart-cc25",
"Czech",
"cs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T19:59:39+00:00 | [] | [
"cs",
"cs"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBART fine-tuned model for Czech abstractive summarization (HT2A-C)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ''Headline + Text to Abstract'' (HT2A) which consists in generating a multi-se... | [
"# mBART fine-tuned model for Czech abstractive summarization (HT2A-C)\nThis model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.",
"## Task\nThe model deals with the task ''Headline + Text to Abstract'' (HT2A) which consists in generating... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBART fine-tuned model for Czech abstractive summarization (HT2A-C)\nThis model is a fine-tuned checkpoint of facebook/mba... |
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-finetuned-squad1-aqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-squad1](https://huggin... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["adversarial_qa"], "model-index": [{"name": "deberta-base-finetuned-squad1-aqa", "results": []}]} | stevemobs/deberta-base-finetuned-squad1-aqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"dataset:adversarial_qa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T19:59:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #dataset-adversarial_qa #license-mit #endpoints_compatible #region-us
| deberta-base-finetuned-squad1-aqa
=================================
This model is a fine-tuned version of stevemobs/deberta-base-finetuned-squad1 on the adversarial\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5912
Model description
-----------------
More information needed
... | [
"### 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 #dataset-adversarial_qa #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\\_si... |
text-classification | transformers |
# First - you should prepare few functions to talk to model
```python
import torch
from transformers import BertForSequenceClassification, AutoTokenizer
LABELS = ['neutral', 'happiness', 'sadness', 'enthusiasm', 'fear', 'anger', 'disgust']
tokenizer = AutoTokenizer.from_pretrained('Aniemore/rubert-tiny2-russian-emot... | {"language": ["ru"], "license": "mit", "tags": ["russian", "classification", "emotion", "emotion-detection", "emotion-recognition", "multiclass"], "datasets": ["Aniemore/cedr-m7"], "widget": [{"text": "\u041a\u0430\u043a \u0434\u0435\u043b\u0430?"}, {"text": "\u0414\u0443\u0440\u0430\u043a \u0442\u0432\u043e\u0439 \u04... | Aniemore/rubert-tiny2-russian-emotion-detection | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"russian",
"classification",
"emotion",
"emotion-detection",
"emotion-recognition",
"multiclass",
"ru",
"dataset:Aniemore/cedr-m7",
"doi:10.57967/hf/1275",
"license:mit",
"model-index",
"autotrain_compatible",
... | null | 2022-05-22T20:00:03+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #emotion #emotion-detection #emotion-recognition #multiclass #ru #dataset-Aniemore/cedr-m7 #doi-10.57967/hf/1275 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# First - you should prepare few functions to talk to model
# And then - just gently ask a model to predict your emotion
# Or, just simply use our package (GitHub), that can do whatever you want (or maybe not)
s
| [
"# First - you should prepare few functions to talk to model",
"# And then - just gently ask a model to predict your emotion",
"# Or, just simply use our package (GitHub), that can do whatever you want (or maybe not)\n\n\ns"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #emotion #emotion-detection #emotion-recognition #multiclass #ru #dataset-Aniemore/cedr-m7 #doi-10.57967/hf/1275 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# First - you shoul... |
null | transformers | This model, DeLADE, is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone.
*[A Dense Representation Framework for Lexical and Semantic Matching](https://arxiv.org/pdf/2112.04666.pdf)* Sheng-Chieh Lin and Jimmy Lin.
You can find the usage of the model in our ... | {} | jacklin/DeLADE | null | [
"transformers",
"pytorch",
"arxiv:2112.04666",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T20:21:09+00:00 | [
"2112.04666"
] | [] | TAGS
#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us
| This model, DeLADE, is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone.
*A Dense Representation Framework for Lexical and Semantic Matching* Sheng-Chieh Lin and Jimmy Lin.
You can find the usage of the model in our DHR repo: (1) Inference on MSMARCO Passa... | [] | [
"TAGS\n#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Nanatan/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T20:21:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2169
* Accuracy: 0.9215
* F1: 0.9215
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
# Homer Simpson Chatbot | {"tags": ["conversational"]} | HomerChatbot/HomerSimpson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-22T21:00:19+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Homer Simpson Chatbot | [
"# Homer Simpson Chatbot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Homer Simpson Chatbot"
] |
fill-mask | transformers |
# ScholarBERT_100 Model
This is the **ScholarBERT_100** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**221B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model... | {"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]} | globuslabs/ScholarBERT | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-22T21:15:16+00:00 | [
"2205.11342"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ScholarBERT\_100 Model
======================
This is the ScholarBERT\_100 variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (221B tokens).
This is a cased (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by defaul... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# ScholarBERT-XL_100 Model
This is the **ScholarBERT-XL_100** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**221B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The... | {"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]} | globuslabs/ScholarBERT-XL | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:17:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #science #multi-displinary #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ScholarBERT-XL\_100 Model
=========================
This is the ScholarBERT-XL\_100 variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (221B tokens).
This is a cased (case-sensitive) model. The tokenizer will not convert all inputs to lower-case ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #science #multi-displinary #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #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. -->
# deberta-base-combined-squad1-aqa
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-base-combined-squad1-aqa", "results": []}]} | stevemobs/deberta-base-combined-squad1-aqa | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:18:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-base-combined-squad1-aqa
================================
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: 0.9442
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: 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\\... |
fill-mask | transformers |
# ScholarBERT_10 Model
This is the **ScholarBERT_10** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**22.1B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model ... | {"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]} | globuslabs/ScholarBERT_10 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:22:02+00:00 | [
"2205.11342"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ScholarBERT\_10 Model
=====================
This is the ScholarBERT\_10 variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (22.1B tokens).
This is a cased (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# ScholarBERT_1 Model
This is the **ScholarBERT_1** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**2.2B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model is ... | {"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]} | globuslabs/ScholarBERT_1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:24:22+00:00 | [
"2205.11342"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ScholarBERT\_1 Model
====================
This is the ScholarBERT\_1 variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (2.2B tokens).
This is a cased (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
T... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# ScholarBERT_100_WB Model
This is the **ScholarBERT_100_WB** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**221B tokens**).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the [BERT-base]... | {"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]} | globuslabs/ScholarBERT_100_WB | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:27:22+00:00 | [
"2205.11342"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ScholarBERT\_100\_WB Model
==========================
This is the ScholarBERT\_100\_WB variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (221B tokens).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pr... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# ScholarBERT_10_WB Model
This is the **ScholarBERT_10_WB** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**22.1B tokens**).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the [BERT-base](... | {"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]} | globuslabs/ScholarBERT_10_WB | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:30:01+00:00 | [
"2205.11342"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ScholarBERT\_10\_WB Model
=========================
This is the ScholarBERT\_10\_WB variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (22.1B tokens).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pret... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# ScholarBERT-XL_1 Model
This is the **ScholarBERT-XL_1** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**2.2B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The mod... | {"language": "en", "license": "apache-2.0", "tags": ["science", "multi-displinary"]} | globuslabs/ScholarBERT-XL_1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:32:14+00:00 | [
"2205.11342"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ScholarBERT-XL\_1 Model
=======================
This is the ScholarBERT-XL\_1 variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (2.2B tokens).
This is a cased (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by def... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #science #multi-displinary #en #arxiv-2205.11342 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
T5-base model for text summarization finetuned on subset of amazon reviews for english language.
## Rouge scores
- Rouge 1 : 0.5019
- Rouge 2 : 0.4226
- Rouge L : 0.4877
- Rouge Lsum : 0.4877 | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["amazon_reviews_multi"]} | sumedh/t5-base-amazonreviews | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"en",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-22T21:33:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
T5-base model for text summarization finetuned on subset of amazon reviews for english language.
## Rouge scores
- Rouge 1 : 0.5019
- Rouge 2 : 0.4226
- Rouge L : 0.4877
- Rouge Lsum : 0.4877 | [
"## Rouge scores\n- Rouge 1 : 0.5019\n- Rouge 2 : 0.4226\n- Rouge L : 0.4877\n- Rouge Lsum : 0.4877"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Rouge scores\n- Rouge 1 : 0.5019\n- Rouge 2 : 0.4226\n- Rouge L : 0.4877\n- Rouge Lsum ... |
text2text-generation | transformers |
# mBART fine-tuned model for Czech abstractive summarization (HT2A-S)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Headline + Text to Abstr... | {"language": ["cs", "cs"], "license": "apache-2.0", "tags": ["abstractive summarization", "mbart-cc25", "Czech"], "datasets": ["SumeCzech dataset news-based"], "metrics": ["rouge", "rougeraw"]} | krotima1/mbart-ht2a-s | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"abstractive summarization",
"mbart-cc25",
"Czech",
"cs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:49:55+00:00 | [] | [
"cs",
"cs"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBART fine-tuned model for Czech abstractive summarization (HT2A-S)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ''Headline + Text to Abstract'' (HT2A) which consists in generating a multi-se... | [
"# mBART fine-tuned model for Czech abstractive summarization (HT2A-S)\nThis model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.",
"## Task\nThe model deals with the task ''Headline + Text to Abstract'' (HT2A) which consists in generating... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBART fine-tuned model for Czech abstractive summarization (HT2A-S)\nThis model is a fine-tuned checkpoint of facebook/mba... |
text2text-generation | transformers |
# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Abstract + Text to Headl... | {"language": ["cs", "cs"], "license": "apache-2.0", "tags": ["abstractive summarization", "mbart-cc25", "Czech"], "datasets": ["SumeCzech dataset news-based"], "metrics": ["rouge", "rougeraw"]} | krotima1/mbart-at2h-s | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"abstractive summarization",
"mbart-cc25",
"Czech",
"cs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T21:59:59+00:00 | [] | [
"cs",
"cs"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating a one- or ... | [
"# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)\nThis model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.",
"## Task\nThe model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)\nThis model is a fine-tuned checkpoint of facebook/mba... |
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. -->
# wav2vec2-6
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-6", "results": []}]} | chrisvinsen/wav2vec2-6 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T22:08:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-6
==========
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: 5.2459
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* 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: 0.003\n* train\\_batch\\_size: 32... |
null | null | Work in progress <br>
Finetuned model for abstractive summarization coming soon <br> | {} | sumedh/pegasus | null | [
"region:us"
] | null | 2022-05-22T22:23:36+00:00 | [] | [] | TAGS
#region-us
| Work in progress <br>
Finetuned model for abstractive summarization coming soon <br> | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotinons-jinesh
This model is a fine-tuned version of [distilbert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotinons-jinesh", "results": []}]} | jinesh90/distilbert-base-uncased-finetuned-emotinons-jinesh | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T22:40:12+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-emotinons-jinesh
==================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2175
* Accuracy: 0.9275
* F1: 0.9274
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 #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... |
text2text-generation | transformers |
# mBART fine-tuned model for Czech abstractive summarization (AT2H-CS)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Abstract + Text to Head... | {"language": ["cs", "cs"], "license": "apache-2.0", "tags": ["abstractive summarization", "mbart-cc25", "Czech"], "datasets": ["private Czech News Center dataset news-based", "SumeCzech dataset news-based"], "metrics": ["rouge", "rougeraw"]} | krotima1/mbart-at2h-cs | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"abstractive summarization",
"mbart-cc25",
"Czech",
"cs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T22:40:34+00:00 | [] | [
"cs",
"cs"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBART fine-tuned model for Czech abstractive summarization (AT2H-CS)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating a one- or... | [
"# mBART fine-tuned model for Czech abstractive summarization (AT2H-CS)\nThis model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.",
"## Task\nThe model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generatin... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBART fine-tuned model for Czech abstractive summarization (AT2H-CS)\nThis model is a fine-tuned checkpoint of facebook/mb... |
text2text-generation | transformers |
# mBART fine-tuned model for Czech abstractive summarization (HT2A-CS)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Headline + Text to Abst... | {"language": ["cs", "cs"], "license": "apache-2.0", "tags": ["Summarization", "abstractive summarization", "mbart-cc25", "Czech"], "datasets": ["private Czech News Center dataset news-based", "SumeCzech dataset news-based"], "metrics": ["rouge", "rougeraw"]} | krotima1/mbart-ht2a-cs | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"Summarization",
"abstractive summarization",
"mbart-cc25",
"Czech",
"cs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T22:41:07+00:00 | [] | [
"cs",
"cs"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #Summarization #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBART fine-tuned model for Czech abstractive summarization (HT2A-CS)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ''Headline + Text to Abstract'' (HT2A) which consists in generating a multi-s... | [
"# mBART fine-tuned model for Czech abstractive summarization (HT2A-CS)\nThis model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.",
"## Task\nThe model deals with the task ''Headline + Text to Abstract'' (HT2A) which consists in generatin... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #Summarization #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBART fine-tuned model for Czech abstractive summarization (HT2A-CS)\nThis model is a fine-tuned checkpoint... |
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="gitierrez/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | gitierrez/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-22T22:46:36+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Krill/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-22T23:09:00+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/belarusian_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/as... | {"language": "be", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/belarusian_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"be",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-22T23:50:06+00:00 | [
"1804.00015"
] | [
"be"
] | TAGS
#espnet #audio #automatic-speech-recognition #be #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/belarusian\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu May 19 18:39:24 EDT 2022'
* python version: '3.9.5 (default, Jun 4 202... | [
"### 'espnet/belarusian\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu May 19 18:39:24 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #be #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/belarusian\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | hamidov02/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T23:51:07+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-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3701
* Wer: 0.2946
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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_sentence_classifier
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "bert_sentence_classifier", "results": []}]} | juancavallotti/bert_sentence_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T23:51:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_sentence\_classifier
==========================
This model is a fine-tuned version of bert-large-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0040
* F1: 0.6123
* Precision: 0.6123
* Recall: 0.6123
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 8",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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: 1e-05\n* train\\_batch\\... |
feature-extraction | transformers | ERROR: type should be string, got "\nhttps://github.com/BM-K/Sentence-Embedding-is-all-you-need\n\n# Korean-Sentence-Embedding\n🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.\n\n## Quick tour\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\ndef cal_score(a, b):\n if len(a.shape) == 1: a = a.unsqueeze(0)\n if len(b.shape) == 1: b = b.unsqueeze(0)\n\n a_norm = a / a.norm(dim=1)[:, None]\n b_norm = b / b.norm(dim=1)[:, None]\n return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100\n\nmodel = AutoModel.from_pretrained('BM-K/KoSimCSE-bert') \nAutoTokenizer.from_pretrained('BM-K/KoSimCSE-bert')\n\nsentences = ['치타가 들판을 가로 질러 먹이를 쫓는다.',\n '치타 한 마리가 먹이 뒤에서 달리고 있다.',\n '원숭이 한 마리가 드럼을 연주한다.']\n\ninputs = tokenizer(sentences, padding=True, truncation=True, return_tensors=\"pt\")\nembeddings, _ = model(**inputs, return_dict=False)\n\nscore01 = cal_score(embeddings[0][0], embeddings[1][0])\nscore02 = cal_score(embeddings[0][0], embeddings[2][0])\n```\n\n## Performance\n- Semantic Textual Similarity test set results <br>\n\n| Model | AVG | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |\n|------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n| KoSBERT<sup>†</sup><sub>SKT</sub> | 77.40 | 78.81 | 78.47 | 77.68 | 77.78 | 77.71 | 77.83 | 75.75 | 75.22 |\n| KoSBERT | 80.39 | 82.13 | 82.25 | 80.67 | 80.75 | 80.69 | 80.78 | 77.96 | 77.90 |\n| KoSRoBERTa | 81.64 | 81.20 | 82.20 | 81.79 | 82.34 | 81.59 | 82.20 | 80.62 | 81.25 |\n| | | | | | | | | |\n| KoSentenceBART | 77.14 | 79.71 | 78.74 | 78.42 | 78.02 | 78.40 | 78.00 | 74.24 | 72.15 |\n| KoSentenceT5 | 77.83 | 80.87 | 79.74 | 80.24 | 79.36 | 80.19 | 79.27 | 72.81 | 70.17 |\n| | | | | | | | | |\n| KoSimCSE-BERT<sup>†</sup><sub>SKT</sub> | 81.32 | 82.12 | 82.56 | 81.84 | 81.63 | 81.99 | 81.74 | 79.55 | 79.19 |\n| KoSimCSE-BERT | 83.37 | 83.22 | 83.58 | 83.24 | 83.60 | 83.15 | 83.54 | 83.13 | 83.49 |\n| KoSimCSE-RoBERTa | 83.65 | 83.60 | 83.77 | 83.54 | 83.76 | 83.55 | 83.77 | 83.55 | 83.64 |\n| | | | | | | | | | |\n| KoSimCSE-BERT-multitask | 85.71 | 85.29 | 86.02 | 85.63 | 86.01 | 85.57 | 85.97 | 85.26 | 85.93 |\n| KoSimCSE-RoBERTa-multitask | 85.77 | 85.08 | 86.12 | 85.84 | 86.12 | 85.83 | 86.12 | 85.03 | 85.99 |" | {"language": "ko", "tags": ["korean"]} | BM-K/KoSimCSE-bert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"korean",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-05-22T23:54:43+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #korean #ko #endpoints_compatible #region-us
| URL
Korean-Sentence-Embedding
=========================
Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
Quick tour
----------
Performance
-----------
* Semantic Textual Similarity test set... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #korean #ko #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | BaxterAI/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 | 2022-05-23T00:02:23+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: 0.8170
- Accuracy: 0.9225
- F1: 0.9241
## 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: 0.8170\n- Accuracy: 0.9225\n- F1: 0.9241",
"## 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... |
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="gitierrez/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 +/... | gitierrez/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T00:10:17+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Gusteau
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Gusteau", "results": []}]} | Dizzykong/Gusteau | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T00:22:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Gusteau
This model is a fine-tuned version of gpt2-medium on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyp... | [
"# Gusteau\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hype... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Gusteau\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information need... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/bengali_blstm`
This model was trained by dzeinali using bn_openslr53 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/bn_openslr53/asr1
./run.sh -... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["bn_openslr53"]} | espnet/bengali_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:bn_openslr53",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-23T00:23:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-bn_openslr53 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/bengali\_blstm'
This model was trained by dzeinali using bn\_openslr53 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun May 22 21:21:37 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [... | [
"### 'espnet/bengali\\_blstm'\n\n\nThis model was trained by dzeinali using bn\\_openslr53 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun May 22 21:21:37 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'\n... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-bn_openslr53 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/bengali\\_blstm'\n\n\nThis model was trained by dzeinali using bn\\_openslr53 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# orchid219_ft_vit-large-patch16-224-in21k-finetuned-eurosat
This model is a fine-tuned version of [gary109/orchid219_ft_vit-large... | {"tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "orchid219_ft_vit-large-patch16-224-in21k-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "image_fol... | Vemi/orchid219_ft_vit-large-patch16-224-in21k-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T01:08:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #region-us
| orchid219\_ft\_vit-large-patch16-224-in21k-finetuned-eurosat
============================================================
This model is a fine-tuned version of gary109/orchid219\_ft\_vit-large-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9545
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-analysis-en-id
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "finetuning-sentiment-analysis-en-id", "results": []}]} | viviastaari/finetuning-sentiment-analysis-en-id | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T01:13:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuning-sentiment-analysis-en-id
===================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1654
* Accuracy: 0.9527
* F1: 0.9646
* Precision: 0.9641
* Recall: 0.9652
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### 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: 5e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-analysis-en
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"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "finetuning-sentiment-analysis-en", "results": []}]} | viviastaari/finetuning-sentiment-analysis-en | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T01:48:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuning-sentiment-analysis-en
================================
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: 0.0792
* Accuracy: 0.9803
* F1: 0.9856
* Precision: 0.9875
* Recall: 0.9837
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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: 5e-05\n* train\\_batch\\... |
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-xlsr-mn-eng
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-mn-eng", "results": []}]} | Dulu/wav2vec2-xlsr-mn-eng-v0 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T02:07:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-mn-eng
====================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the IEMOCAP and Common Voice's MN dataset. Can be used to recognize speech on ENG and MN simultaneously.
It achieves the following results on the evaluation set:
* Loss: 0.3087
* Wer: 0.3402
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/330
| {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-narrower-2022-05-13 | null | [
"tensorboard",
"region:us"
] | null | 2022-05-23T02:14:14+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
| [
"# Introduction\n\nSee URL"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL"
] |
text-generation | transformers |
# Wenzhong-GPT2-110M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLG任务，中文版的GPT2-Small。
Focused on handling NLG tasks, Chinese GPT2-Small.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Se... | {"language": ["zh"], "license": "apache-2.0", "tags": ["generate", "gpt2"], "inference": {"parameters": {"temperature": 0.7, "top_p": 0.6, "repetition_penalty": 1.1, "max_new_tokens": 128, "num_return_sequences": 3, "do_sample": true}}, "widget": ["\u5317\u4eac\u662f\u4e2d\u56fd\u7684", "\u897f\u6e56\u7684\u666f\u8272"... | IDEA-CCNL/Wenzhong-GPT2-110M | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generate",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T02:15:36+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generate #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Wenzhong-GPT2-110M
==================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLG任务，中文版的GPT2-Small。
Focused on handling NLG tasks, Chinese GPT2-Small.
模型分类 Model Taxonomy
-------------------
模型信息 Model Information
----------------------
类似于Wenzho... | [
"### 加载模型 Loading Models",
"### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #generate #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### 加载模型 Loading Models",
"### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/330
No random combiner inside.
Tensorboard log: https://tensorboard.dev/experiment/VKoVx6IZTBuGCJN9kt72BQ/
| {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-L-2022-05-23 | null | [
"tensorboard",
"region:us"
] | null | 2022-05-23T02:36:04+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
No random combiner inside.
Tensorboard log: URL
| [
"# Introduction\n\nSee URL\n\nNo random combiner inside.\n\nTensorboard log: URL"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL\n\nNo random combiner inside.\n\nTensorboard log: URL"
] |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/330
No random combiner inside.
Tensorboard logs: https://tensorboard.dev/experiment/vZGRckYUR4eNjnBJ9AOEkg/
| {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-05-23 | null | [
"tensorboard",
"region:us"
] | null | 2022-05-23T02:57:30+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
No random combiner inside.
Tensorboard logs: URL
| [
"# Introduction\n\nSee URL\n\nNo random combiner inside.\n\nTensorboard logs: URL"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL\n\nNo random combiner inside.\n\nTensorboard logs: URL"
] |
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="muks/q-Taxi-v0", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | muks/q-Taxi-v0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T02:57:33+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"
] |
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. -->
# wav2vec2-7
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-7", "results": []}]} | chrisvinsen/wav2vec2-7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T03:07:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-7
==========
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: 0.6017
* Wer: 0.5200
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* 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: 3... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/330
No random combiner inside.
Tensorboard logs: https://tensorboard.dev/experiment/xREbAh7RS9m2TADGRLVx2g/
| {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-narrower-2022-05-23 | null | [
"tensorboard",
"region:us"
] | null | 2022-05-23T03:08:28+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
No random combiner inside.
Tensorboard logs: URL
| [
"# Introduction\n\nSee URL\n\nNo random combiner inside.\n\nTensorboard logs: URL"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL\n\nNo random combiner inside.\n\nTensorboard logs: URL"
] |
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="muks/q-Taxi-v1_100000", 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-v1_100000", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "... | muks/q-Taxi-v1_100000 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T03:10:53+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
feature-extraction | transformers | # KoMiniLM
🐣 Korean mini language model
## Overview
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight korean language mod... | {} | BM-K/KoMiniLM | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2002.10957",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T03:26:31+00:00 | [
"2002.10957"
] | [] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2002.10957 #endpoints_compatible #region-us
| KoMiniLM
========
Korean mini language model
Overview
--------
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight kore... | [
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]](URL were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case in this project.",
"### Data sets",
"#... | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2002.10957 #endpoints_compatible #region-us \n",
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]](URL were distilled from each discrete layer of the teacher model to the student model. W... |
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. -->
# my-awesome-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_rev... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["yelp_review_full"], "metrics": ["accuracy"], "model-index": [{"name": "my-awesome-model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "yelp_review_full", "type": "yelp_review_full", "... | wonscha/my-awesome-model | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T03:34:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| my-awesome-model
================
This model is a fine-tuned version of bert-base-cased on the yelp\_review\_full dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5680
* Accuracy: 0.559
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: 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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_multilingual_XLSum-finetuned-summarization-V2
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](http... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-summarization-V2", "results": []}]} | GiordanoB/mT5_multilingual_XLSum-finetuned-summarization-V2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T03:58:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5\_multilingual\_XLSum-finetuned-summarization-V2
===================================================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5523
* Rouge1: 25.8727
* Rouge2: 16.1688
* Rouge... | [
"### 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 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
fill-mask | transformers |
# deberta-small-japanese-aozora
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 6 hours 42 minutes for training. You can fine-tune `deberta-small-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-small-japa... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/deberta-small-japanese-aozora | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T03:58:53+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-small-japanese-aozora
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 6 hours 42 minutes for training. You can fine-tune 'deberta-small-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# deberta-small-japanese-aozora",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 6 hours 42 minutes for training. You can fine-tune 'deberta-small-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to ... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-small-japanese-aozora",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 6 hours 42... |
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... | bosemessi/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T04:26:25+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... |
translation | transformers | # Roman-Thai Transliterator by Transformer Models
GitHub: https://github.com/wannaphong/thai2rom-v2/tree/main/roman2thai-transformer | {"license": "apache-2.0", "tags": ["translation"], "datasets": ["thai2rom-v2"], "metrics": ["cer"], "widget": [{"text": "maiphai"}]} | wannaphong/Roman2Thai-transliterator | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"dataset:thai2rom-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T04:26:58+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #dataset-thai2rom-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Roman-Thai Transliterator by Transformer Models
GitHub: URL | [
"# Roman-Thai Transliterator by Transformer Models\n\nGitHub: URL"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #dataset-thai2rom-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roman-Thai Transliterator by Transformer Models\n\nGitHub: URL"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-ar-sp
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the ... | {"license": "apache-2.0", "tags": ["summarization", "arabic", "am", "es", "amharic", "mt5", "Abstractive Summarization", "generated_from_trainer"], "model-index": [{"name": "mt5-base-finetuned-ar-sp", "results": []}]} | eslamxm/mt5-base-finetuned-ar-sp | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"arabic",
"am",
"es",
"amharic",
"Abstractive Summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region... | null | 2022-05-23T04:29:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #arabic #am #es #amharic #Abstractive Summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-ar-sp
========================
This model is a fine-tuned version of google/mt5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2772
* Rouge-1: 23.01
* Rouge-2: 10.41
* Rouge-l: 20.94
* Gen Len: 19.0
* Bertscore: 71.56
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #arabic #am #es #amharic #Abstractive Summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
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. -->
# kobart-kormath
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
#... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "kobart-kormath", "results": []}]} | madatnlp/kobart-kormath | null | [
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T04:38:23+00:00 | [] | [] | TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# kobart-kormath
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedu... | [
"# kobart-kormath\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information neede... | [
"TAGS\n#transformers #tf #bart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# kobart-kormath\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMor... |
null | 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"} | thamaine/distilbert-base-cased | null | [
"keras",
"region:us"
] | null | 2022-05-23T05:07:23+00:00 | [] | [] | TAGS
#keras #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': 0.01, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16\n\n\nTraining Metrics\n--------------... | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.01, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16\n\n\n... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.