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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": ... | usmanazhar/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 | 2022-05-10T03:50:39+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: 0.3346
- Accuracy: 0.8733
- F1: 0.8766
## 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: 0.3346\n- Accuracy: 0.8733\n- F1: 0.8766",
"## 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... |
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. -->
# madatnlp/gamza-bart-for-kormath128
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobar... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "madatnlp/gamza-bart-for-kormath128", "results": []}]} | madatnlp/gamza-bart-for-kormath128 | null | [
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T04:01:54+00:00 | [] | [] | TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| madatnlp/gamza-bart-for-kormath128
==================================
This model is a fine-tuned version of gogamza/kobart-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1429
* Validation Loss: 0.3575
* Epoch: 42
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 1e-04, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bart #text2text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 1e-04, 'dec... |
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
| {"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... | ironbar/ppo-lunarlander-v2-local-train-bigger | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T04:32:30+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | promsoft/ll2022-05-09-lunar4 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T05:13:26+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | vincentbonnet/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T05:53:01+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | Slientea/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T06:12:34+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | RicardFos/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T06:21:46+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
#
# gpt2-ft-id-puisi
This model is a fine-tuned on an [Indonesian Recipe](https://huggingface.co/datasets/Sultannn/id_recipe).
It achiev... | {"license": "mit", "tags": ["generated_from_keras_callback", "id", "Indonesian"], "dataset": ["id_puisi"], "widget": [{"text": "SENJA"}, {"text": "BERANI"}], "model-index": [{"name": "Sultannn/gpt2-ft-id-puisi", "results": []}]} | Sultannn/gpt2-ft-id-puisi | null | [
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"id",
"Indonesian",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T06:22:22+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #id #Indonesian #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
gpt2-ft-id-puisi
================
This model is a fine-tuned on an Indonesian Recipe.
It achieves the following results on the evaluation set:
* Train Loss: 5.3628
* Validation Loss: 5.8179
### Training results
Licenese
========
The MIT license
| [
"### Training results\n\n\n\nLicenese\n========\n\n\nThe MIT license"
] | [
"TAGS\n#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #id #Indonesian #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training results\n\n\n\nLicenese\n========\n\n\nThe MIT license"
] |
text-classification | transformers |
`deberta-v3-base`, fine tuned on the debiased NLI dataset from "Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets", Wu et al., 2022.
Tuned using the code at https://github.com/jimmycode/gen-debiased-nli
| {"license": "apache-2.0", "widget": [{"text": "[CLS] Rover is a dog. [SEP] Rover is a cat. [SEP]"}]} | tomhosking/deberta-v3-base-debiased-nli | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T06:35:49+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
'deberta-v3-base', fine tuned on the debiased NLI dataset from "Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets", Wu et al., 2022.
Tuned using the code at URL
| [] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | jabot/PPPO_LunarLanderV2_1000000Steps | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T06:53:57+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# electricidad-base-finetuned-parmex
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggi... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "electricidad-base-finetuned-parmex", "results": []}]} | mrm8488/electricidad-base-finetuned-parmex | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T06:56:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| electricidad-base-finetuned-parmex
==================================
This model is a fine-tuned version of mrm8488/electricidad-base-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0372
* F1: 0.9764
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.309269976237555e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2"... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.309269976237555e-05\n* train\\_batch\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | naomiyjchen/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T07:25:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
null | null |
This gBert-base model was finetuned on a sentiment prediction task with tweets from German politician during the German Federal Election in 2021.
## Model Description:
This model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It a... | {"language": ["de"], "tags": ["Text Classification", "sentiment", "Simpletransformers", "deepset/gbert-base"]} | mox/gBERt_base_twitter_sentiment_politicians | null | [
"Text Classification",
"sentiment",
"Simpletransformers",
"deepset/gbert-base",
"de",
"region:us"
] | null | 2022-05-10T07:33:33+00:00 | [] | [
"de"
] | TAGS
#Text Classification #sentiment #Simpletransformers #deepset/gbert-base #de #region-us
|
This gBert-base model was finetuned on a sentiment prediction task with tweets from German politician during the German Federal Election in 2021.
## Model Description:
This model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It a... | [
"## Model Description:\nThis model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It achieved an accuracy of 93% on the specific dataset.",
"## Model Implementation\nYou can implement this model for example with Simpletransforme... | [
"TAGS\n#Text Classification #sentiment #Simpletransformers #deepset/gbert-base #de #region-us \n",
"## Model Description:\nThis model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It achieved an accuracy of 93% on the specific ... |
fill-mask | transformers |
# nlp-waseda/roberta-large-japanese
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
t... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "mask_token": "[MASK]", "widget": [{"text": "\u65e9\u7a32\u7530 \u5927\u5b66 \u3067 \u81ea\u7136 \u8a00\u8a9e \u51e6\u7406 \u3092 [MASK] \u3059\u308b \u3002"}]} | nlp-waseda/roberta-large-japanese | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T07:37:48+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# nlp-waseda/roberta-large-japanese
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to use
You can use this model for masked language modeling as follows:
You can fine-tune this model on downstream tasks.
## Tokenization
Th... | [
"# nlp-waseda/roberta-large-japanese",
"## Model description\n\nThis is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.",
"## How to use\n\nYou can use this model for masked language modeling as follows:\n\n\nYou can fine-tune this model on downstream tasks.",... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# nlp-waseda/roberta-large-japanese",
"## Model description\n\nThis is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and th... |
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
| {"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... | veddm/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T07:38:38+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
null | null | # **Acoustic and language models**
Acoustic model built using [QuartzNet15x5](https://arxiv.org/pdf/1910.10261.pdf) architecture and trained using [NeMo toolkit](https://github.com/NVIDIA/NeMo/tree/r1.0.0b4)
Three n-gram language models created using [KenLM Language Model Toolkit](https://kheafield.com/code/kenlm)
... | {"datasets": "SberDevices/Golos"} | SberDevices/quartznet-russian | null | [
"dataset:SberDevices/Golos",
"arxiv:1910.10261",
"arxiv:2106.10161",
"region:us"
] | null | 2022-05-10T07:46:12+00:00 | [
"1910.10261",
"2106.10161"
] | [] | TAGS
#dataset-SberDevices/Golos #arxiv-1910.10261 #arxiv-2106.10161 #region-us
| Acoustic and language models
============================
Acoustic model built using QuartzNet15x5 architecture and trained using NeMo toolkit
Three n-gram language models created using KenLM Language Model Toolkit
* LM built on Common Crawl Russian dataset
* LM built on Golos train set
* LM built on Common Crawl... | [] | [
"TAGS\n#dataset-SberDevices/Golos #arxiv-1910.10261 #arxiv-2106.10161 #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | Pausaxo/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T07:56:41+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | melodisease/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T07:56:43+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | arampacha/LunarLander-v2-ppo-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T07:59:50+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | etsymba/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T08:23:14+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
reinforcement-learning | stable-baselines3 | My very first models for DL RL | {"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... | vgonisanz/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T08:42:34+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
| My very first models for DL RL | [] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model-1h
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "model-1h", "results": []}]} | ebonazza2910/model-1h | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T08:45:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| model-1h
========
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8317
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-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... |
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-base-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003",... | SreyanG-NVIDIA/bert-base-cased-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 | 2022-05-10T08:56:26+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-base-cased-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: 0.0650
* Precision: 0.9325
* Recall: 0.9375
* F1: 0.9350
* Accuracy: 0.9840
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418445526375223297/XdAg... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/marcfriedrich7/1652179164370/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/marcfriedrich7 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T09:36:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
marc friedrich
@marcfriedrich7
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | k2 |
# Introduction
See https://github.com/k2-fsa/icefall/pull/363 | {"language": ["en"], "library_name": "k2", "tags": ["automatic-speech-recognition", "k2"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/resolve/main/test_wavs/1089-134686-0001.wav"}, {"example_title": "Libr... | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13 | null | [
"k2",
"tensorboard",
"onnx",
"automatic-speech-recognition",
"en",
"has_space",
"region:us"
] | null | 2022-05-10T09:56:50+00:00 | [] | [
"en"
] | TAGS
#k2 #tensorboard #onnx #automatic-speech-recognition #en #has_space #region-us
|
# Introduction
See URL | [
"# Introduction\nSee URL"
] | [
"TAGS\n#k2 #tensorboard #onnx #automatic-speech-recognition #en #has_space #region-us \n",
"# Introduction\nSee URL"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | patrickvonplaten/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T10:02:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5185
* Wer: 0.3370
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
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. -->
# madatnlp/ket5-config-scratch
This model is a fine-tuned version of [madatnlp/ket5-config-scratch](https://huggingface.co/madatnlp/ket5... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "madatnlp/ket5-config-scratch", "results": []}]} | madatnlp/ket5-config-scratch | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T10:13:12+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| madatnlp/ket5-config-scratch
============================
This model is a fine-tuned version of madatnlp/ket5-config-scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.3667
* Validation Loss: 1.4931
* Epoch: 18
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 1e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate':... |
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
| {"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... | runjivu/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T10:13:50+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1522425562895044608/H93g... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/broductmanager/1652182609331/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/broductmanager | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T10:35:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
rahul
@broductmanager
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Udi-Aharon/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T10:50:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0615
* Precision: 0.9243
* Recall: 0.9347
* F1: 0.9295
* Accuracy: 0.9837
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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* le... |
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. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It ac... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]} | eduardopds/dummy-model | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T10:55:43+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-model
This model is a fine-tuned version of camembert-base 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
## Tr... | [
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base 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 inf... | [
"TAGS\n#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1472922431396331520/eqT1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/_avichalp_/1652183801632/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/_avichalp_ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T10:55:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
avi
@\_avichalp\_
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# MultiCite: Multi-label Citation Intent Analysis as paper-level Q&A (NAACL 2022)
This model has been trained on the data available here: https://github.com/allenai/multicite. | {"language": "en", "license": "mit"} | allenai/multicite-qa-qasper | null | [
"transformers",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T11:04:24+00:00 | [] | [
"en"
] | TAGS
#transformers #en #license-mit #endpoints_compatible #region-us
|
# MultiCite: Multi-label Citation Intent Analysis as paper-level Q&A (NAACL 2022)
This model has been trained on the data available here: URL | [
"# MultiCite: Multi-label Citation Intent Analysis as paper-level Q&A (NAACL 2022)\n\nThis model has been trained on the data available here: URL"
] | [
"TAGS\n#transformers #en #license-mit #endpoints_compatible #region-us \n",
"# MultiCite: Multi-label Citation Intent Analysis as paper-level Q&A (NAACL 2022)\n\nThis model has been trained on the data available here: URL"
] |
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-triviaqa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-triviaqa", "results": []}]} | FabianWillner/distilbert-base-uncased-finetuned-triviaqa | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T11:20:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-triviaqa
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9949
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
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
| {"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... | rairo/landing-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T11:21:38+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text2text-generation | transformers |
# QA2D-t5-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).
It achieves the following results on the evaluation set:
- Loss: 0.3236
- Rouge1: 89.8753
- Rouge2: 81.8104
- Rougel: 85.4253
- Rougelsum: 85.4236
- Bleu: 72... | {"license": "apache-2.0", "tags": ["text2text-generation", "generated_from_trainer"], "datasets": ["domenicrosati/QA2D"], "metrics": ["rouge", "bleu"], "widget": [{"text": "where in the world is carmen sandiego. she is in abruzzo", "example_title": "Where is Carmen Sandiego?"}, {"text": "which province is halifax in. n... | domenicrosati/QA2D-t5-small | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:domenicrosati/QA2D",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T11:23:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-domenicrosati/QA2D #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| QA2D-t5-small
=============
This model is a fine-tuned version of t5-small on QA2D.
It achieves the following results on the evaluation set:
* Loss: 0.3236
* Rouge1: 89.8753
* Rouge2: 81.8104
* Rougel: 85.4253
* Rougelsum: 85.4236
* Bleu: 72.1080
See: URL for training and eval stats and URL for the code!
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 20\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-domenicrosati/QA2D #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were use... |
sentence-similarity | sentence-transformers |
# florentgbelidji/setfit_emotion
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | florentgbelidji/setfit_emotion | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T11:25:57+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# florentgbelidji/setfit_emotion
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:... | [
"# florentgbelidji/setfit_emotion\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers ... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# florentgbelidji/setfit_emotion\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering ... |
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
| {"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... | sswt/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T11:26:09+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# con_gpt_med_model
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "con_gpt_med_model", "results": []}]} | akozlo/con_gpt_med | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T11:47:23+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# con_gpt_med_model
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 fol... | [
"# con_gpt_med_model\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",
"### Tra... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# con_gpt_med_model\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed"... |
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-arman-fa
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer", "token-classification"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-arman-fa", "results": []}]} | BK-V/xlm-roberta-base-finetuned-arman-fa | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-10T11:52:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-arman-fa
===================================
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: 0.0077
* F1: 0.9855
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
text-generation | transformers | hello
| {} | akozlo/lib_gpt_med | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T11:58:39+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | arunprasadh/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T12:03:10+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vanichandna/bert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vanichandna/bert-base-multilingual-cased-finetuned-squad", "results": []}]} | vanichandna/bert-base-multilingual-cased-finetuned-squadv1 | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T12:14:15+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| vanichandna/bert-base-multilingual-cased-finetuned-squad
========================================================
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5313
* Epoch: 3
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\_steps': 43880, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
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... | moghis/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 | 2022-05-10T12:16:21+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: 0.2141
* Accuracy: 0.924
* F1: 0.9241
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5
This model is a fine-tuned version of [husnu/wav2vec2-large-xls-r-300m-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5", "results": []}]} | husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T12:23:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab\_common\_voice-8\_5
===========================================================
This model is a fine-tuned version of husnu/wav2vec2-large-xls-r-300m-turkish-colab\_common\_voice-8\_4 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Los... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
reinforcement-learning | stable-baselines3 |
# **PPO-MLP** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MLP** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO-MLP", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "Lu... | jcmc/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T12:28:08+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO-MLP Agent playing LunarLander-v2
This is a trained model of a PPO-MLP agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO-MLP Agent playing LunarLander-v2\n This is a trained model of a PPO-MLP agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO-MLP Agent playing LunarLander-v2\n This is a trained model of a PPO-MLP agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-peyma-fa
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-peyma-fa", "results": []}]} | BK-V/xlm-roberta-base-finetuned-peyma-fa | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-10T13:11:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-peyma-fa
===================================
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: 0.0937
* F1: 0.9249
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* tra... |
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. -->
# roberta-base-finetuned-scrambled-squad-5
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-finetuned-scrambled-squad-5", "results": []}]} | huxxx657/roberta-base-finetuned-scrambled-squad-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T13:19:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| roberta-base-finetuned-scrambled-squad-5
========================================
This model is a fine-tuned version of roberta-base on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7078
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 7e-05\n* train\\_batch\\_size: 16\n*... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | arunprasadh/ppo-LunarLander-v3 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T13:32:18+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# reaprtripr/distill-bert-csn
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "reaprtripr/distill-bert-csn", "results": []}]} | reaprtripr/distill-bert-csn | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T13:33:36+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reaprtripr/distill-bert-csn
===========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: nan
* Validation Loss: nan
* Epoch: 0
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
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-osdg
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-osdg", "results": []}]} | peter2000/distilbert-base-uncased-finetuned-osdg | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T13:34:09+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-osdg
======================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8193
* F1 Score: 0.7962
* Accuracy: 0.8434
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"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... |
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
| {"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... | leumastai/LunarLander-v1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T13:48:32+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["beans"], "model-index": [{"name": "vit-base-beans", "results": []}]} | karthiksv/vit-base-beans | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T14:08:52+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# vit-base-beans
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperp... | [
"# vit-base-beans\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proc... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit-base-beans\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.",
"## Model descripti... |
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... | pglauner/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 | 2022-05-10T14:12:34+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: 0.2251
* Accuracy: 0.9265
* F1: 0.9265
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... |
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
| {"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... | joitandr/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T14:12:47+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | chancar/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T14:15:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
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: 0.9780
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.7891
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_... |
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-crypto-tweet-sentiment-test
This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](h... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "finetuning-crypto-tweet-sentiment-test", "results": []}]} | laurens88/finetuning-crypto-tweet-sentiment-test | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T14:18:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-crypto-tweet-sentiment-test
This model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## ... | [
"# finetuning-crypto-tweet-sentiment-test\n\nThis model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore i... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-crypto-tweet-sentiment-test\n\nThis model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset.",
... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 848227025
- CO2 Emissions (in grams): 5.096755166899446
## Validation Metrics
- Loss: 2.1917402744293213
- Accuracy: 0.44666666666666666
- Macro F1: 0.20291677804725128
- Micro F1: 0.44666666666666666
- Weighted F1: 0.37709801275... | {"language": "fr", "tags": "autotrain", "datasets": ["ziedhajyahia/autotrain-data-ok"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.096755166899446} | ziedhajyahia/autotrain-ok-848227025 | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain",
"fr",
"dataset:ziedhajyahia/autotrain-data-ok",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T14:19:32+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain #fr #dataset-ziedhajyahia/autotrain-data-ok #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 848227025
- CO2 Emissions (in grams): 5.096755166899446
## Validation Metrics
- Loss: 2.1917402744293213
- Accuracy: 0.44666666666666666
- Macro F1: 0.20291677804725128
- Micro F1: 0.44666666666666666
- Weighted F1: 0.37709801275... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 848227025\n- CO2 Emissions (in grams): 5.096755166899446",
"## Validation Metrics\n\n- Loss: 2.1917402744293213\n- Accuracy: 0.44666666666666666\n- Macro F1: 0.20291677804725128\n- Micro F1: 0.44666666666666666\n- Weighted... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain #fr #dataset-ziedhajyahia/autotrain-data-ok #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 848227025\n- CO2 Emissions ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | lazyrama/ppo-BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T14:36:15+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TO... |
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
| {"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... | shun2741/TESTppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T14:39:04+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
token-classification | transformers | <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting primary outcomes from articles reporting clinical trials.
This is the second version of the model; the original model development was reported in:
Anna Koroleva, Sanjay Kamath, Patrick Paroubek. Extracting primary and reported outcomes from ... | {} | aakorolyova/primary_outcome_extraction | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T15:03:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting primary outcomes from articles reporting clinical trials.
This is the second version of the model; the original model development was reported in:
Anna Koroleva, Sanjay Kamath, Patrick Paroubek. Extracting primary and reported outcomes from ... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-rater", "results": []}]} | paultimothymooney/distilbert-rater | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T15:11:45+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-rater
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# distilbert-rater\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-rater\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore informa... |
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
| {"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... | Extred/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T15:23:20+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text2text-generation | transformers |
# QA2D-t5-base
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).
It achieves the following results on the evaluation set:
- Loss: 0.2563
- Rouge1: 90.1064
- Rouge2: 82.378
- Rougel: 85.7963
- Rougelsum: 85.8004
- Bleu: 72.73... | {"license": "apache-2.0", "tags": ["text2text-generation", "generated_from_trainer"], "datasets": ["domenicrosati/QA2D"], "metrics": ["rouge", "bleu"], "widget": [{"text": "where in the world is carmen sandiego. she is in abruzzo", "example_title": "Where is Carmen Sandiego?"}, {"text": "which province is halifax in. n... | domenicrosati/QA2D-t5-base | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:domenicrosati/QA2D",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T15:29:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-domenicrosati/QA2D #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| QA2D-t5-base
============
This model is a fine-tuned version of t5-base on QA2D.
It achieves the following results on the evaluation set:
* Loss: 0.2563
* Rouge1: 90.1064
* Rouge2: 82.378
* Rougel: 85.7963
* Rougelsum: 85.8004
* Bleu: 72.7328
See: URL for training and eval stats and URL for the code!
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 3\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-domenicrosati/QA2D #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were use... |
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
| {"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... | flydsc/first-ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T15:34:18+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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, and huggingface_sb3)
To use this model make sure you are running Python versi... | {"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... | kalmufti/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T15:37:17+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, and huggingface_sb3)
To use this model make sure you are running Python version 3.7.13. You can use pyenv to manage multiple versions of Pyt... | [
"# PPO Agent Playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3, and huggingface_sb3)\n To use this model make sure you are running Python version 3.7.13. You can use pyenv to manage multiple versions ... | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3, and hugging... |
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
| {"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... | dtiapkin/ppo-LunalLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T15:38:30+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
feature-extraction | transformers |
# HPD-MiniLM-F128
This repository contains the pre-trained models for our paper [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://arxiv.org/abs/2203.07687). The sentence embedding model contains only 23M parameters and the model size is only 87MB.
## Overvi... | {"license": "apache-2.0"} | Xuandong/HPD-MiniLM-F128 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2203.07687",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T16:01:40+00:00 | [
"2203.07687"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2203.07687 #license-apache-2.0 #endpoints_compatible #region-us
| HPD-MiniLM-F128
===============
This repository contains the pre-trained models for our paper Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation. The sentence embedding model contains only 23M parameters and the model size is only 87MB.
Overview
--------
We propose ... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2203.07687 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# HPD-TinyBERT-F128
This repository contains the pre-trained models for our paper [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://arxiv.org/abs/2203.07687). The sentence embedding model contains only 14M parameters and the model size is only 55MB.
## Over... | {"license": "apache-2.0"} | Xuandong/HPD-TinyBERT-F128 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2203.07687",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T16:04:19+00:00 | [
"2203.07687"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2203.07687 #license-apache-2.0 #endpoints_compatible #region-us
| HPD-TinyBERT-F128
=================
This repository contains the pre-trained models for our paper Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation. The sentence embedding model contains only 14M parameters and the model size is only 55MB.
Overview
--------
We prop... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2203.07687 #license-apache-2.0 #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. -->
# roberta-base-finetuned-scrambled-squad-10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-ba... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-finetuned-scrambled-squad-10", "results": []}]} | huxxx657/roberta-base-finetuned-scrambled-squad-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T16:05:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| roberta-base-finetuned-scrambled-squad-10
=========================================
This model is a fine-tuned version of roberta-base on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7200
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 7e-05\n* train\\_batch\\_size: 16\n*... |
null | 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. -->
## Model description
This is a gpt2 model trained on 142 612 different Lithuanian Wikipedia articles + 11 405 articles taken from delfi.... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmpq0jhm_jh", "results": []}]} | BenasSabalys/gpt2-lithuanian-wiki | null | [
"transformers",
"tf",
"gpt2",
"generated_from_keras_callback",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T16:10:07+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #generated_from_keras_callback #endpoints_compatible #text-generation-inference #region-us
|
## Model description
This is a gpt2 model trained on 142 612 different Lithuanian Wikipedia articles + 11 405 articles taken from URL, URL and URL news portals.
## Intended uses & limitations
This is a model I trained when writing my bachelors. You can use it anywhere you want.
### Training results
Model reache... | [
"## Model description\n\nThis is a gpt2 model trained on 142 612 different Lithuanian Wikipedia articles + 11 405 articles taken from URL, URL and URL news portals.",
"## Intended uses & limitations\n\nThis is a model I trained when writing my bachelors. You can use it anywhere you want.",
"### Training results... | [
"TAGS\n#transformers #tf #gpt2 #generated_from_keras_callback #endpoints_compatible #text-generation-inference #region-us \n",
"## Model description\n\nThis is a gpt2 model trained on 142 612 different Lithuanian Wikipedia articles + 11 405 articles taken from URL, URL and URL news portals.",
"## Intended uses ... |
text-to-speech | espnet | ## RyanSpeech model (based on ESPnet2)
### `espnet/english_male_ryanspeech_fastspeech`
This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the vocoder se... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ryanspeech"], "widget": [{"text": "This seems a very pleasant place, and I think I shall enjoy myself very much."}]} | espnet/english_male_ryanspeech_fastspeech | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ryanspeech",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-05-10T16:28:53+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #region-us
| ## RyanSpeech model (based on ESPnet2)
### 'espnet/english_male_ryanspeech_fastspeech'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:
## Download the dataset
You can download Ryan... | [
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_fastspeech'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:",
"## Download the dataset\nYou can d... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #region-us \n",
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_fastspeech'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the... |
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. -->
# KenP/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "KenP/mt5-small-finetuned-amazon-en-es", "results": []}]} | KenP/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T16:31:10+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| KenP/mt5-small-finetuned-amazon-en-es
=====================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.0378
* Validation Loss: 3.3712
* Epoch: 7
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
null | null |
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone http... | {"tags": ["opt_metasq"]} | patrickvonplaten/opt_metaseq_125m | null | [
"opt_metasq",
"region:us"
] | null | 2022-05-10T16:31:43+00:00 | [] | [] | TAGS
#opt_metasq #region-us
|
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
## 2. Install Megatron
## 3. Install fairscale
## 4. Install metaseq
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
| [
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\")",
"## 6. Run the following:"
] | [
"TAGS\n#opt_metasq #region-us \n",
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\"... |
null | null |
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone http... | {"tags": ["opt_metasq"]} | patrickvonplaten/opt_metaseq_1300m | null | [
"opt_metasq",
"region:us"
] | null | 2022-05-10T16:31:55+00:00 | [] | [] | TAGS
#opt_metasq #region-us
|
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
## 2. Install Megatron
## 3. Install fairscale
## 4. Install metaseq
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
| [
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\")",
"## 6. Run the following:"
] | [
"TAGS\n#opt_metasq #region-us \n",
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\"... |
null | null |
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone http... | {"tags": ["opt_metasq"]} | patrickvonplaten/opt_metaseq_2700m | null | [
"opt_metasq",
"region:us"
] | null | 2022-05-10T16:32:06+00:00 | [] | [] | TAGS
#opt_metasq #region-us
|
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
## 2. Install Megatron
## 3. Install fairscale
## 4. Install metaseq
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
| [
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\")",
"## 6. Run the following:"
] | [
"TAGS\n#opt_metasq #region-us \n",
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\"... |
feature-extraction | transformers |
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone http... | {"tags": ["opt_metasq"]} | patrickvonplaten/opt_metaseq_6700m | null | [
"transformers",
"opt",
"feature-extraction",
"opt_metasq",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-10T16:32:24+00:00 | [] | [] | TAGS
#transformers #opt #feature-extraction #opt_metasq #endpoints_compatible #text-generation-inference #region-us
|
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
## 2. Install Megatron
## 3. Install fairscale
## 4. Install metaseq
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
| [
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\")",
"## 6. Run the following:"
] | [
"TAGS\n#transformers #opt #feature-extraction #opt_metasq #endpoints_compatible #text-generation-inference #region-us \n",
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
... |
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_common_voice-8_6
This model is a fine-tuned version of [husnu/wav2vec2-large-xls-r-300m-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_6", "results": []}]} | husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_6 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T16:38:23+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\_common\_voice-8\_6
===========================================================
This model is a fine-tuned version of husnu/wav2vec2-large-xls-r-300m-turkish-colab\_common\_voice-8\_5 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Los... | [
"### 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. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-rater", "results": []}]} | cmcmorrow/distilbert-rater | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T16:47:22+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-rater
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# distilbert-rater\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-rater\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore informa... |
text-to-speech | espnet | ## RyanSpeech model (based on ESPnet2)
### `espnet/english_male_ryanspeech_tacotron`
This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the vocoder sepa... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ryanspeech"], "widget": [{"text": "This seems a very pleasant place, and I think I shall enjoy myself very much."}]} | espnet/english_male_ryanspeech_tacotron | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ryanspeech",
"license:cc-by-nc-4.0",
"has_space",
"region:us"
] | null | 2022-05-10T17:05:51+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #has_space #region-us
| ## RyanSpeech model (based on ESPnet2)
### 'espnet/english_male_ryanspeech_tacotron'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:
## Download the dataset
You can download RyanSp... | [
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_tacotron'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:",
"## Download the dataset\nYou can dow... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #has_space #region-us \n",
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_tacotron'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to dow... |
text-to-speech | espnet | ## RyanSpeech model (based on ESPnet2)
### `espnet/english_male_ryanspeech_fastspeech2`
This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the vocoder s... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ryanspeech"], "widget": [{"text": "This seems a very pleasant place, and I think I shall enjoy myself very much."}]} | espnet/english_male_ryanspeech_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ryanspeech",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-05-10T17:13:25+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #region-us
| ## RyanSpeech model (based on ESPnet2)
### 'espnet/english_male_ryanspeech_fastspeech2'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:
## Download the dataset
You can download Rya... | [
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_fastspeech2'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:",
"## Download the dataset\nYou can ... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #region-us \n",
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_fastspeech2'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download th... |
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. -->
# distilbart-cnn-arxiv-pubmed-pubmed-v3-e16
This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-arxiv-pubmed-pubmed-v3-e16", "results": []}]} | theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed-v3-e16 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T17:15:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbart-cnn-arxiv-pubmed-pubmed-v3-e16
=========================================
This model is a fine-tuned version of theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8306
* Rouge1: 56.4519
* Rouge2: 41.6818
* Rougel: 44.78... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | transformers |
Model usage:
```
tokenizer = MBart50Tokenizer.from_pretrained("IljaSamoilov/MBART-estonian-subtitles", src_lang="et_EE", tgt_lang="et_EE")
model = MBartForConditionalGeneration.from_pretrained("IljaSamoilov/MBART-estonian-subtitles")
``` | {"language": ["et"], "widget": [{"text": "te olete ka noh, noh, p\u00e4ris korralikult ka Rahvusringh\u00e4\u00e4lingu teatud m\u00f5ttes sellisesse keerulisse olukorda pannud,"}, {"text": "Et, et, et miks mitte olla siis tasakaalus, ma noh, h\u00fcpoteetiliselt viskan selle palli \u00fcles,"}]} | IljaSamoilov/MBART-estonian-subtitles | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"et",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-10T17:17:53+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #et #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Model usage:
| [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #et #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | Extred/TEST2ppo-LunarLander-v2-MlpLnLstmPolicy | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T17:17:58+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-to-speech | espnet | ## RyanSpeech model (based on ESPnet2)
### `espnet/english_male_ryanspeech_conformer_fastspeech2`
This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ryanspeech"], "widget": [{"text": "This seems a very pleasant place, and I think I shall enjoy myself very much."}]} | espnet/english_male_ryanspeech_conformer_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ryanspeech",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-05-10T17:20:09+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #region-us
| ## RyanSpeech model (based on ESPnet2)
### 'espnet/english_male_ryanspeech_conformer_fastspeech2'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:
## Download the dataset
You can d... | [
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_conformer_fastspeech2'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:",
"## Download the dataset... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #region-us \n",
"## RyanSpeech model (based on ESPnet2)",
"### 'espnet/english_male_ryanspeech_conformer_fastspeech2'\nThis model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to d... |
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
| {"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... | Tanchik/TESTppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T17:20:28+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | FollishBoi/ppo-LunarLander-v2-try4 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T17:23:41+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-classification | transformers | Since the start of the COVID-19 pandemic, there has been a widespread increase in the amount of hate-speech being propagated online against the Asian community. This project builds upon and explores the work of He et al. Their COVID-HATE dataset contains 206 million tweets focused around anti-Asian hate speech. Using t... | {"language": "en", "license": "mit", "tags": ["transfer-learning", "bert", "hatespeech", "covid19"], "datasets": ["COVID-HATE"], "metrics": ["f1-score"]} | nihaldsouza1/covid-hatespeech-detection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"transfer-learning",
"bert",
"hatespeech",
"covid19",
"en",
"dataset:COVID-HATE",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T17:37:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #transfer-learning #bert #hatespeech #covid19 #en #dataset-COVID-HATE #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Since the start of the COVID-19 pandemic, there has been a widespread increase in the amount of hate-speech being propagated online against the Asian community. This project builds upon and explores the work of He et al. Their COVID-HATE dataset contains 206 million tweets focused around anti-Asian hate speech. Using t... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #transfer-learning #bert #hatespeech #covid19 #en #dataset-COVID-HATE #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | # Basic Information
This is the Dr. Decr model used in XOR-TyDi leaderboard task 1 whitebox submission.
https://nlp.cs.washington.edu/xorqa/
The detailed implementation of the model can be found in:
https://arxiv.org/pdf/2112.08185.pdf
Source code to train the model can be found via PrimeQA's IR component:
https... | {} | PrimeQA/DrDecr_XOR-TyDi_whitebox | null | [
"arxiv:2112.08185",
"region:us"
] | null | 2022-05-10T17:46:07+00:00 | [
"2112.08185"
] | [] | TAGS
#arxiv-2112.08185 #region-us
| # Basic Information
This is the Dr. Decr model used in XOR-TyDi leaderboard task 1 whitebox submission.
URL
The detailed implementation of the model can be found in:
URL
Source code to train the model can be found via PrimeQA's IR component:
URL
It is a Neural IR model built on top of the ColBERTv1 api and not ... | [
"# Basic Information\n\nThis is the Dr. Decr model used in XOR-TyDi leaderboard task 1 whitebox submission. \n\nURL\n\n\nThe detailed implementation of the model can be found in:\n\nURL\n\nSource code to train the model can be found via PrimeQA's IR component:\nURL\n\nIt is a Neural IR model built on top of the Col... | [
"TAGS\n#arxiv-2112.08185 #region-us \n",
"# Basic Information\n\nThis is the Dr. Decr model used in XOR-TyDi leaderboard task 1 whitebox submission. \n\nURL\n\n\nThe detailed implementation of the model can be found in:\n\nURL\n\nSource code to train the model can be found via PrimeQA's IR component:\nURL\n\nIt i... |
token-classification | transformers |
Importing the model and tokenizer:
```
tokenizer = AutoTokenizer.from_pretrained("IljaSamoilov/EstBERT-estonian-subtitles-token-classification")
model = AutoModelForTokenClassification.from_pretrained("IljaSamoilov/EstBERT-estonian-subtitles-token-classification")
``` | {"language": ["et"], "widget": [{"text": "Et, et, et miks mitte olla siis tasakaalus, ma noh, h\u00fcpoteetiliselt viskan selle palli \u00fcles,"}, {"text": "te olete ka noh, noh, p\u00e4ris korralikult ka Rahvusringh\u00e4\u00e4lingu teatud m\u00f5ttes sellisesse keerulisse olukorda pannud,"}]} | IljaSamoilov/EstBERT-estonian-subtitles-token-classification | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"et",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T17:53:58+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #bert #token-classification #et #autotrain_compatible #endpoints_compatible #region-us
|
Importing the model and tokenizer:
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #et #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | Extred/TEST2ppo-LunarLander-v2-CustomMLPNet | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T18:03:07+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# roberta-base-finetuned-scrambled-squad-15
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-ba... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-finetuned-scrambled-squad-15", "results": []}]} | huxxx657/roberta-base-finetuned-scrambled-squad-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T18:13:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| roberta-base-finetuned-scrambled-squad-15
=========================================
This model is a fine-tuned version of roberta-base on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8722
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 7e-05\n* train\\_batch\\_size: 16\n*... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | m-luebbers/mb-LunarLander-v1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T18:16:46+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | AngelUrq/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T18:23:18+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
reinforcement-learning | stable-baselines3 |
# **mlp** Agent playing **LunarLander-v2**
This is a trained model of a **mlp** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "mlp", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | jadermcs/ppo-lunar-lander | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T18:27:03+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# mlp Agent playing LunarLander-v2
This is a trained model of a mlp agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# mlp Agent playing LunarLander-v2\n This is a trained model of a mlp agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# mlp Agent playing LunarLander-v2\n This is a trained model of a mlp agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# tests-finetuned-squad-full
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberban... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "tests-finetuned-squad-full", "results": []}]} | ruselkomp/sber-full-framebank | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T18:34:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| tests-finetuned-squad-full
==========================
This model is a fine-tuned version of sberbank-ai/sbert\_large\_nlu\_ru on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5672
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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed:... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kw_pubmed_1000_0.0003
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](http... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["keyword_pubmed_dataset"], "metrics": ["accuracy"], "model-index": [{"name": "kw_pubmed_1000_0.0003", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "keyword_pubmed_dataset", "type": "keyword_pubmed_... | enoriega/kw_pubmed_1000_0.0003 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:keyword_pubmed_dataset",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T18:37:10+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-keyword_pubmed_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| kw\_pubmed\_1000\_0.0003
========================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the keyword\_pubmed\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 4.7086
* Accuracy: 0.3394
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 250\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and ep... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-keyword_pubmed_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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-finetuned-squad-qa-minilmv2-32
This model is a fine-tuned version of [subhasisj/zh-TAPT-MLM-MiniLM](https://huggingface.co/su... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "zh-finetuned-squad-qa-minilmv2-32", "results": []}]} | subhasisj/zh-finetuned-squad-qa-minilmv2-32 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T18:52:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| zh-finetuned-squad-qa-minilmv2-32
=================================
This model is a fine-tuned version of subhasisj/zh-TAPT-MLM-MiniLM on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4311
Model description
-----------------
More information needed
Intended uses & limita... | [
"### 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 #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* see... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"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... | DanilaBay/PPO_LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T19:05:10+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | kosta-naumenko/ppo-LunarLander-v2-2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-10T19:06:22+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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