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image-classification | transformers |
# dog-food-vit-base-patch16-224-in21k
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "datasets": ["sasha/dog-food"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "dog-food-vit-base-patch16-224-in21k", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "Dog Food", "type": "sash... | sasha/dog-food-vit-base-patch16-224-in21k | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"dataset:sasha/dog-food",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T18:12:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #dataset-sasha/dog-food #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# dog-food-vit-base-patch16-224-in21k
This model was trained on the 'train' split of the Dogs vs Food dataset -- try training your own using the
the demo on Google Colab!
## Example Images
#### dog
!dog
#### food
!food | [
"# dog-food-vit-base-patch16-224-in21k\n\n\nThis model was trained on the 'train' split of the Dogs vs Food dataset -- try training your own using the \nthe demo on Google Colab!",
"## Example Images",
"#### dog\n\n!dog",
"#### food\n\n!food"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #dataset-sasha/dog-food #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# dog-food-vit-base-patch16-224-in21k\n\n\nThis model was trained on the 'train' split of the Dogs vs Food dataset -- try training yo... |
text-generation | transformers |
# Spongebob DialoGPT | {"tags": ["conversational"]} | mcimmy/DialoGPT-small-bob | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T19:02:30+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Spongebob DialoGPT | [
"# Spongebob DialoGPT"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Spongebob DialoGPT"
] |
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. -->
# model_trained_by_me2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "model_trained_by_me2", "results": []}]} | fourthbrain-demo/model_trained_by_me2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-20T19:33:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# model_trained_by_me2
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: 0.4258
- Accuracy: 0.7983
- F1: 0.7888
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# model_trained_by_me2\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4258\n- Accuracy: 0.7983\n- F1: 0.7888",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# model_trained_by_me2\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieve... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": []}]} | ornil1/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T20:03:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the k... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-parsbert-uncased-finetuned-squad
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-parsbert-uncased-finetuned-squad", "results": []}]} | mhmsadegh/bert-base-parsbert-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T20:09:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| bert-base-parsbert-uncased-finetuned-squad
==========================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2932
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* see... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="sevlabr/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | sevlabr/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-20T20:55:53+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
question-answering | transformers |
# bert-base-japanese-wikipedia-ud-head
## Model Description
This is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extend... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "wikipedia", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b6... | KoichiYasuoka/bert-base-japanese-wikipedia-ud-head | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"japanese",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T20:58:52+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# bert-base-japanese-wikipedia-ud-head
## Model Description
This is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from bert-base-japanese-char-extended and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguit... | [
"# bert-base-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from bert-base-japanese-char-extended and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoi... | [
"TAGS\n#transformers #pytorch #bert #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# bert-base-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a BERT model pretrained on Japanese Wikipedi... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sevlabr/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | sevlabr/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-20T21:02:57+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
sentence-similarity | sentence-transformers |
# mcontriever-base-msmarco
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.
This model was converted from the facebook [mcontriever-msmarco model](https://huggingface.co... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | nthakur/mcontriever-base-msmarco | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2112.09118",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-20T21:12:04+00:00 | [
"2112.09118"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2112.09118 #endpoints_compatible #has_space #region-us
|
# mcontriever-base-msmarco
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.
This model was converted from the facebook mcontriever-msmarco model. When using this model, have a look at the public... | [
"# mcontriever-base-msmarco\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.\n\nThis model was converted from the facebook mcontriever-msmarco model. When using this model, have a look at th... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2112.09118 #endpoints_compatible #has_space #region-us \n",
"# mcontriever-base-msmarco\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and ca... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ytung/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | ytung/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-20T21:52:20+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ytung/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.44 +/... | ytung/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-20T21:57:27+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | ericntay/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 | 2022-06-20T22:19:59+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\\_... |
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... | scjones/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 | 2022-06-20T22:43:04+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: 0.1630
* Accuracy: 0.9315
* F1: 0.9318
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-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... | fouad-shammary/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 | 2022-06-20T23:27:20+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: 0.2349
* Accuracy: 0.9165
* F1: 0.9164
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... |
feature-extraction | transformers | This model has been trained without supervision following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Tr... | {"tags": "feature-extraction", "pipeline_tag": "feature-extraction"} | spencer/contriever_pipeline | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2112.09118",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T23:32:09+00:00 | [
"2112.09118"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2112.09118 #endpoints_compatible #region-us
| This model has been trained without supervision following the approach described in Towards Unsupervised Dense Information Retrieval with Contrastive Learning. The associated GitHub repository is available here URL
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers requi... | [
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2112.09118 #endpoints_compatible #region-us \n",
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
text-generation | transformers |
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<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/1517890310642278400/p9HN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dav_erage/1655773043560/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dav_erage | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T23:56:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
blooming 'bold
@dav\_erage
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-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/1517890310642278400/p9HN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dav_erage-dozendav/1655773693107/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dav_erage-dozendav | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T00:07:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
blooming 'bold & ˣʸzed
@dav\_erage-dozendav
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.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1010733562
- CO2 Emissions (in grams): 60.24263131580023
## Validation Metrics
- Loss: 0.1812974065542221
- Accuracy: 0.9252564102564103
- Precision: 0.9409888357256778
- Recall: 0.9074596257369905
- AUC: 0.9809618001947271
- F1: 0.92... | {"language": "en", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-GlueModels"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 60.24263131580023} | deepesh0x/autotrain-GlueModels-1010733562 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:deepesh0x/autotrain-data-GlueModels",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T00:21:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-GlueModels #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1010733562
- CO2 Emissions (in grams): 60.24263131580023
## Validation Metrics
- Loss: 0.1812974065542221
- Accuracy: 0.9252564102564103
- Precision: 0.9409888357256778
- Recall: 0.9074596257369905
- AUC: 0.9809618001947271
- F1: 0.92... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1010733562\n- CO2 Emissions (in grams): 60.24263131580023",
"## Validation Metrics\n\n- Loss: 0.1812974065542221\n- Accuracy: 0.9252564102564103\n- Precision: 0.9409888357256778\n- Recall: 0.9074596257369905\n- AUC: 0.980961800... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-GlueModels #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1010733562\n- CO2 Emissions (in ... |
image-classification | transformers |
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been wri... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"]} | Sampson2022/test2 | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T01:34:13+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## M... | [
"# ResNet-50 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. \n\nDisclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face tea... | [
"TAGS\n#transformers #pytorch #resnet #image-classification #vision #dataset-imagenet-1k #arxiv-1512.03385 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ResNet-50 v1.5\n\nResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residua... |
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/1017172371080470528/K6wT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/maxfitemaster/1655780681704/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/maxfitemaster | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T02:00:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
James Swartout
@maxfitemaster
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Klinsc/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T03:07:59+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
question-answering | transformers |
# roberta-base-japanese-aozora-ud-head
## Model Description
This is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [roberta-base-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora-char) and [UD_Jap... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b66\u5e74\u306b... | KoichiYasuoka/roberta-base-japanese-aozora-ud-head | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"japanese",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T04:21:38+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# roberta-base-japanese-aozora-ud-head
## Model Description
This is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from roberta-base-japanese-aozora-char and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when specifyin... | [
"# roberta-base-japanese-aozora-ud-head",
"## Model Description\n\nThis is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from roberta-base-japanese-aozora-char and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# roberta-base-japanese-aozora-ud-head",
"## Model Description\n\nThis is a RoBERTa model pretrained on 青空文庫 for dependency-pa... |
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... | BellaAndBria/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 | 2022-06-21T04:36:46+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: 0.1611
* Accuracy: 0.9425
* F1: 0.9424
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: 3.0",
"### Trai... | [
"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... |
null | keras |
# Timeseries classification from scratch
Based on the _Timeseries classification from scratch_ example on [keras.io](https://keras.io/examples/timeseries/timeseries_classification_from_scratch/) created by [hfawaz](https://github.com/hfawaz/).
## Model description
The model is a Fully Convolutional Neural Network o... | {"library_name": "keras", "tags": ["timeseries"]} | keras-io/timeseries-classification-from-scratch | null | [
"keras",
"tensorboard",
"timeseries",
"arxiv:1611.06455",
"has_space",
"region:us"
] | null | 2022-06-21T05:30:32+00:00 | [
"1611.06455"
] | [] | TAGS
#keras #tensorboard #timeseries #arxiv-1611.06455 #has_space #region-us
| Timeseries classification from scratch
======================================
Based on the *Timeseries classification from scratch* example on URL created by hfawaz.
Model description
-----------------
The model is a Fully Convolutional Neural Network originally proposed in this paper.
The implementation is based... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\n\nModel reproduced by [Edoardo Abati](URL target=)"
] | [
"TAGS\n#keras #tensorboard #timeseries #arxiv-1611.06455 #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\n\nModel reproduced by [Edoardo Abati](URL target=)"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Corianas/dqn-SpaceInvadersNoFrameskip-v4_21.6.22 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T05:33:06+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Corianas/dqn-SpaceInvadersNoFrameskip-v4_21.6.22.LoadBest | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T05:35:08+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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... | ArneD/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 | 2022-06-21T05:42:29+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: 0.2147
* Accuracy: 0.922
* F1: 0.9219
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... |
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. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | kjunelee/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T06:09:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2314
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\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: 16",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_bat... |
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-ru
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-ru", "results": []}]} | UrukHan/wav2vec2-ru | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T06:11:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-ru
===========
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5468
* Wer: 0.4124
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 1\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 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_si... |
null | null | `DISCLAIMER: THIS MODEL IS TO BE USED FOR EDUCATIONAL PURPOSES ONLY, IT HAS NOT BEEN PERMITTED FOR CLINICAL USAGE`
# Psycho-distilbert
Classification model for the detection of depression & suicide texts
Trained on depression classification dataset
Based on `all-distilroberta-v1`
| {"license": "cc-by-sa-4.0"} | urseamajoris/psycho_distilbert | null | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-06-21T06:20:16+00:00 | [] | [] | TAGS
#license-cc-by-sa-4.0 #region-us
| 'DISCLAIMER: THIS MODEL IS TO BE USED FOR EDUCATIONAL PURPOSES ONLY, IT HAS NOT BEEN PERMITTED FOR CLINICAL USAGE'
# Psycho-distilbert
Classification model for the detection of depression & suicide texts
Trained on depression classification dataset
Based on 'all-distilroberta-v1'
| [
"# Psycho-distilbert\n\nClassification model for the detection of depression & suicide texts\nTrained on depression classification dataset\nBased on 'all-distilroberta-v1'"
] | [
"TAGS\n#license-cc-by-sa-4.0 #region-us \n",
"# Psycho-distilbert\n\nClassification model for the detection of depression & suicide texts\nTrained on depression classification dataset\nBased on 'all-distilroberta-v1'"
] |
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. -->
# robingeibel/longformer-large-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/longformer-large-finetuned-big_pa... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "robingeibel/longformer-large-finetuned-big_patent", "results": []}]} | robingeibel/longformer-large-finetuned-big_patent | null | [
"transformers",
"tf",
"longformer",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T06:29:34+00:00 | [] | [] | TAGS
#transformers #tf #longformer #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| robingeibel/longformer-large-finetuned-big\_patent
==================================================
This model is a fine-tuned version of robingeibel/longformer-large-finetuned-big\_patent on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1706
* Epoch: 0
Model descr... | [
"### 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 #longformer #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': '... |
question-answering | transformers |
# bert-large-japanese-wikipedia-ud-head
## Model Description
This is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-ext... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "wikipedia", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b6... | KoichiYasuoka/bert-large-japanese-wikipedia-ud-head | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"japanese",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T06:38:19+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# bert-large-japanese-wikipedia-ud-head
## Model Description
This is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from bert-large-japanese-char-extended and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambigu... | [
"# bert-large-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from bert-large-japanese-char-extended and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to av... | [
"TAGS\n#transformers #pytorch #bert #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# bert-large-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a BERT model pretrained on Japanese Wikiped... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | furyhawk/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T06:46:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7788
* Accuracy: 0.9155
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
null | transformers |
# GENA-LM (gena-lm-bert-base)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
GENA-LM models are transformer masked language models trained on human DNA sequence.
Differences between GENA-LM (`gena-lm-bert-base`) and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size... | {"tags": ["dna", "human_genome"]} | AIRI-Institute/gena-lm-bert-base | null | [
"transformers",
"pytorch",
"bert",
"dna",
"human_genome",
"custom_code",
"arxiv:2002.04745",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T06:53:13+00:00 | [
"2002.04745"
] | [] | TAGS
#transformers #pytorch #bert #dna #human_genome #custom_code #arxiv-2002.04745 #endpoints_compatible #region-us
|
# GENA-LM (gena-lm-bert-base)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
GENA-LM models are transformer masked language models trained on human DNA sequence.
Differences between GENA-LM ('gena-lm-bert-base') and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size... | [
"# GENA-LM (gena-lm-bert-base)\n\nGENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.\n\nGENA-LM models are transformer masked language models trained on human DNA sequence.\n\nDifferences between GENA-LM ('gena-lm-bert-base') and DNABERT:\n- BPE tokenization instead of k-mers;\n- input s... | [
"TAGS\n#transformers #pytorch #bert #dna #human_genome #custom_code #arxiv-2002.04745 #endpoints_compatible #region-us \n",
"# GENA-LM (gena-lm-bert-base)\n\nGENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.\n\nGENA-LM models are transformer masked language models trained on human DNA... |
feature-extraction | transformers |
# Model Card: GroupViT
This checkpoint is uploaded by Jiarui Xu.
## Model Details
The GroupViT model was proposed in [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
In... | {"tags": ["vision"]} | nvidia/groupvit-gcc-yfcc | null | [
"transformers",
"pytorch",
"tf",
"groupvit",
"feature-extraction",
"vision",
"arxiv:2202.11094",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T07:48:32+00:00 | [
"2202.11094"
] | [] | TAGS
#transformers #pytorch #tf #groupvit #feature-extraction #vision #arxiv-2202.11094 #endpoints_compatible #region-us
|
# Model Card: GroupViT
This checkpoint is uploaded by Jiarui Xu.
## Model Details
The GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
Inspired by CLIP, GroupViT is a vision... | [
"# Model Card: GroupViT\n\nThis checkpoint is uploaded by Jiarui Xu.",
"## Model Details\n\nThe GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.\nInspired by CLIP, GroupViT... | [
"TAGS\n#transformers #pytorch #tf #groupvit #feature-extraction #vision #arxiv-2202.11094 #endpoints_compatible #region-us \n",
"# Model Card: GroupViT\n\nThis checkpoint is uploaded by Jiarui Xu.",
"## Model Details\n\nThe GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervi... |
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. -->
# image-classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/micro... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mnist", "autoevaluate/mnist-sample"], "metrics": ["accuracy"], "model-index": [{"name": "image-classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "mnist", "type": "mnist",... | autoevaluate/image-multi-class-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:mnist",
"dataset:autoevaluate/mnist-sample",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T07:52:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-mnist #dataset-autoevaluate/mnist-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| image-classification
====================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the mnist dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0556
* Accuracy: 0.9833
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-mnist #dataset-autoevaluate/mnist-sample #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used d... |
feature-extraction | transformers |
# Model Card: GroupViT
This checkpoint is uploaded by Jiarui Xu.
## Model Details
The GroupViT model was proposed in [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
In... | {"tags": ["vision"], "datasets": ["red_caps"]} | nvidia/groupvit-gcc-redcaps | null | [
"transformers",
"pytorch",
"safetensors",
"groupvit",
"feature-extraction",
"vision",
"dataset:red_caps",
"arxiv:2202.11094",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T08:12:45+00:00 | [
"2202.11094"
] | [] | TAGS
#transformers #pytorch #safetensors #groupvit #feature-extraction #vision #dataset-red_caps #arxiv-2202.11094 #endpoints_compatible #region-us
|
# Model Card: GroupViT
This checkpoint is uploaded by Jiarui Xu.
## Model Details
The GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
Inspired by CLIP, GroupViT is a vision... | [
"# Model Card: GroupViT\n\nThis checkpoint is uploaded by Jiarui Xu.",
"## Model Details\n\nThe GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.\nInspired by CLIP, GroupViT... | [
"TAGS\n#transformers #pytorch #safetensors #groupvit #feature-extraction #vision #dataset-red_caps #arxiv-2202.11094 #endpoints_compatible #region-us \n",
"# Model Card: GroupViT\n\nThis checkpoint is uploaded by Jiarui Xu.",
"## Model Details\n\nThe GroupViT model was proposed in GroupViT: Semantic Segmentatio... |
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. -->
# ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v4
This model is a fine-tuned version of [gary109/ai-light-dance_singing_... | {"tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v4", "results": []}]} | gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T08:18:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us
| ai-light-dance\_singing\_ft\_wav2vec2-large-xlsr-53-5gram-v4
============================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing\_ft\_wav2vec2-large-xlsr-53-5gram-v2 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING dataset.
It achieves the following result... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\n* train\\_batch\\_size... |
null | null | # Introduction | {} | tonne/grow | null | [
"region:us"
] | null | 2022-06-21T08:29:38+00:00 | [] | [] | TAGS
#region-us
| # Introduction | [
"# Introduction"
] | [
"TAGS\n#region-us \n",
"# Introduction"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-tr-en-finetuned-tr-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tr-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_infopankki"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-tr-en-finetuned-tr-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "opus_infopankki",... | PontifexMaximus/Turkish2 | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T09:22:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-tr-en-finetuned-tr-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-tr-en on the opus\_infopankki dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6321
* Bleu: 56.617
* Gen Len: 13.5983
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\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: 30\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-opus_infopankki #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
text-generation | transformers | # Fairseq-dense 13B - Nerys
## Model Description
Fairseq-dense 13B-Nerys is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most pa... | {"language": "en", "license": "mit"} | KoboldAI/fairseq-dense-13B-Nerys-v2 | null | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-21T09:36:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Fairseq-dense 13B - Nerys
## Model Description
Fairseq-dense 13B-Nerys is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most pa... | [
"# Fairseq-dense 13B - Nerys",
"## Model Description\nFairseq-dense 13B-Nerys is a finetune created using Fairseq's MoE dense model.",
"## Training data\nThe training data contains around 2500 ebooks in various genres (the \"Pike\" dataset), a CYOA dataset called \"CYS\" and 50 Asian \"Light Novels\" (the \"Man... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Fairseq-dense 13B - Nerys",
"## Model Description\nFairseq-dense 13B-Nerys is a finetune created using Fairseq's MoE dense model.",
"## Training data\nThe training da... |
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/1523748536168464384/feZm... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/coinmamba/1655808256840/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/coinmamba | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T09:42:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
CoinMamba
@coinmamba
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"
] |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-subjqa-vanilla-electronics-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-vanilla-electronics-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
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"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T09:58:11+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-subjqa-vanilla-electronics-qg'
======================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Language model: t5-... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTrain... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Trainin... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Anjan-finetuned-iitbombay-en-to-hi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Hel... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Anjan-finetuned-iitbombay-en-to-hi", "results": []}]} | anjankumar/Anjan-finetuned-iitbombay-en-to-hi | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-21T10:08:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Anjan-finetuned-iitbombay-en-to-hi
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-hi on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7924
- Bleu: 6.3001
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# Anjan-finetuned-iitbombay-en-to-hi\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-hi on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.7924\n- Bleu: 6.3001",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore ... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Anjan-finetuned-iitbombay-en-to-hi\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-hi on an u... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# camembert-base_tuned_model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "camembert-base_tuned_model", "results": []}]} | lisastf/camembert-base_tuned_model | null | [
"transformers",
"tf",
"camembert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T10:34:49+00:00 | [] | [] | TAGS
#transformers #tf #camembert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# camembert-base_tuned_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 informatio... | [
"# camembert-base_tuned_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 d... | [
"TAGS\n#transformers #tf #camembert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# camembert-base_tuned_model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the ev... |
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. -->
# tiny_focal_ckpt
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation s... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_focal_ckpt", "results": []}]} | kktoto/tiny_focal_ckpt | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T11:03:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_focal\_ckpt
=================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0561
* Precision: 0.6529
* Recall: 0.6366
* F1: 0.6446
* Accuracy: 0.9516
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"n... | JeremiahZ/bert-base-uncased-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T11:20:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-mrpc
======================
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5572
* Accuracy: 0.8578
* F1: 0.9024
* Combined Score: 0.8801
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\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\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-subjqa-vanilla-grocery-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi417/lm-q... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-vanilla-grocery-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T11:32:16+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-subjqa-vanilla-grocery-qg'
==================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: t5-large
* Lang... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Trainin... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-rte
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "... | JeremiahZ/bert-base-uncased-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T11:40:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-rte
=====================
This model is a fine-tuned version of bert-base-uncased on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6972
* Accuracy: 0.6895
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GWW-finetuned-cola
This model is a fine-tuned version of [dunlp/GWW](https://huggingface.co/dunlp/GWW) on the glue dataset.
It a... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "GWW-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_... | dunlp/GWW-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T11:50:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| GWW-finetuned-cola
==================
This model is a fine-tuned version of dunlp/GWW on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6609
* Matthews Correlation: 0.1696
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
image-classification | transformers |
# densenet121-res224-all
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/densenet121-res224-all | null | [
"transformers",
"vision",
"image-classification",
"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2002.02497",
"arxiv:2111.00595",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-21T12:01:42+00:00 | [
"2002.02497",
"2111.00595"
] | [] | TAGS
#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2002.02497 #arxiv-2111.00595 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# densenet121-res224-all
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs... | [
"# densenet121-res224-all\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs fr... | [
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"# densenet121-res224-all\n\nA DenseNet is a type of convolutional neural network that utilises dense connecti... |
image-classification | transformers |
# densenet121-res224-nih
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/densenet121-res224-nih | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:02:19+00:00 | [
"2111.00595",
"2002.02497"
] | [] | TAGS
#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us
|
# densenet121-res224-nih
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs... | [
"# densenet121-res224-nih\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs fr... | [
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"# densenet121-res224-nih\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between... |
image-classification | transformers |
# densenet121-res224-pc
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs ... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/densenet121-res224-pc | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:03:00+00:00 | [
"2111.00595",
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] | [] | TAGS
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|
# densenet121-res224-pc
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs ... | [
"# densenet121-res224-pc\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs fro... | [
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"# densenet121-res224-pc\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between ... |
image-classification | transformers |
# densenet121-res224-chex
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional input... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/densenet121-res224-chex | null | [
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"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2111.00595",
"arxiv:2002.02497",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:03:37+00:00 | [
"2111.00595",
"2002.02497"
] | [] | TAGS
#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us
|
# densenet121-res224-chex
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional input... | [
"# densenet121-res224-chex\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs f... | [
"TAGS\n#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# densenet121-res224-chex\n\nA DenseNet is a type of convolutional neural network that utilises dense connections betwee... |
image-classification | transformers |
# densenet121-res224-rsna
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional input... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/densenet121-res224-rsna | null | [
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"arxiv:2111.00595",
"arxiv:2002.02497",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:04:14+00:00 | [
"2111.00595",
"2002.02497"
] | [] | TAGS
#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us
|
# densenet121-res224-rsna
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional input... | [
"# densenet121-res224-rsna\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs f... | [
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"# densenet121-res224-rsna\n\nA DenseNet is a type of convolutional neural network that utilises dense connections betwee... |
image-classification | transformers |
# densenet121-res224-mimic_nb
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional i... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/densenet121-res224-mimic_nb | null | [
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"image-classification",
"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2111.00595",
"arxiv:2002.02497",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:04:51+00:00 | [
"2111.00595",
"2002.02497"
] | [] | TAGS
#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us
|
# densenet121-res224-mimic_nb
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional i... | [
"# densenet121-res224-mimic_nb\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inpu... | [
"TAGS\n#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# densenet121-res224-mimic_nb\n\nA DenseNet is a type of convolutional neural network that utilises dense connections be... |
image-classification | transformers |
# densenet121-res224-mimic_ch
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional i... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/densenet121-res224-mimic_ch | null | [
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"image-classification",
"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2111.00595",
"arxiv:2002.02497",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:05:28+00:00 | [
"2111.00595",
"2002.02497"
] | [] | TAGS
#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us
|
# densenet121-res224-mimic_ch
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional i... | [
"# densenet121-res224-mimic_ch\n\nA DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inpu... | [
"TAGS\n#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# densenet121-res224-mimic_ch\n\nA DenseNet is a type of convolutional neural network that utilises dense connections be... |
image-classification | transformers |
# resnet50-res512-all
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This model was trained on the datasets pc-nih-rsna-siim-vin at a 512x512 resolution.
### How to use
... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["nih-pc-chex-mimic_ch-google-openi-rsna"]} | torchxrayvision/resnet50-res512-all | null | [
"transformers",
"vision",
"image-classification",
"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2111.00595",
"arxiv:2002.02497",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:06:05+00:00 | [
"2111.00595",
"2002.02497"
] | [] | TAGS
#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us
|
# resnet50-res512-all
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This model was trained on the datasets pc-nih-rsna-siim-vin at a 512x512 resolution.
### How to use
... | [
"# resnet50-res512-all\n\nResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.\n \nThis model was trained on the datasets pc-nih-rsna-siim-vin at a 512x512 resolution.",
"### How to us... | [
"TAGS\n#transformers #vision #image-classification #dataset-nih-pc-chex-mimic_ch-google-openi-rsna #arxiv-2111.00595 #arxiv-2002.02497 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet50-res512-all\n\nResNet (Residual Network) is a convolutional neural network that democratized the concepts of r... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type": "g... | JeremiahZ/bert-base-uncased-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:11:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-cola
======================
This model is a fine-tuned version of bert-base-uncased on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5406
* Matthews Correlation: 0.5880
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
image-classification | transformers |
# dog-food-swin-tiny-patch4-window7-224
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "datasets": ["sasha/dog-food"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "dog-food-swin-tiny-patch4-window7-224", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "Dog Food", "type": "sa... | sasha/dog-food-swin-tiny-patch4-window7-224 | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"huggingpics",
"dataset:sasha/dog-food",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:40:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #huggingpics #dataset-sasha/dog-food #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# dog-food-swin-tiny-patch4-window7-224
This model was trained on the 'train' split of the Dogs vs Food dataset -- try training your own using the
the demo on Google Colab!
## Example Images
#### dog
!dog
#### food
!food | [
"# dog-food-swin-tiny-patch4-window7-224\n\n\nThis model was trained on the 'train' split of the Dogs vs Food dataset -- try training your own using the \nthe demo on Google Colab!",
"## Example Images",
"#### dog\n\n!dog",
"#### food\n\n!food"
] | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #huggingpics #dataset-sasha/dog-food #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# dog-food-swin-tiny-patch4-window7-224\n\n\nThis model was trained on the 'train' split of the Dogs vs Food dataset -- try training... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw`
This model was trained by Wangyou Zhang using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/wsj0_2mix/enh1
./run.sh --skip_data_prep fal... | {"license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["wsj0-2mix"]} | espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:wsj0-2mix",
"arxiv:1804.00015",
"arxiv:2011.03706",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-21T12:43:33+00:00 | [
"1804.00015",
"2011.03706"
] | [] | TAGS
#espnet #audio #audio-to-audio #dataset-wsj0-2mix #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us
|
## ESPnet2 ENH model
### 'espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw'
This model was trained by Wangyou Zhang using wsj0_2mix recipe in espnet.
### Demo: How to use in ESPnet2
## ENH config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw'\n\nThis model was trained by Wangyou Zhang using wsj0_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ENH config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-wsj0-2mix #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw'\n\nThis model was trained by Wangyou Zhang using wsj0_2mix recipe in espnet.",
"### Demo: How t... |
fill-mask | transformers | # Cross-Encoder for MS Marco
This model is a generic masked language model fine tuned on stack overflow data. It's base pre-trained model was the cross-encoder/ms-marco-MiniLM-L-12-v2 model.
The model can be used for creating vectors for search applications. It was trained to be used in conjunction with a knn searc... | {"license": "apache-2.0"} | M-Chimiste/MiniLM-L-12-StackOverflow | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T12:45:16+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder for MS Marco
This model is a generic masked language model fine tuned on stack overflow data. It's base pre-trained model was the cross-encoder/ms-marco-MiniLM-L-12-v2 model.
The model can be used for creating vectors for search applications. It was trained to be used in conjunction with a knn searc... | [
"# Cross-Encoder for MS Marco\n\nThis model is a generic masked language model fine tuned on stack overflow data. It's base pre-trained model was the cross-encoder/ms-marco-MiniLM-L-12-v2 model.\n\nThe model can be used for creating vectors for search applications. It was trained to be used in conjunction with a ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for MS Marco\n\nThis model is a generic masked language model fine tuned on stack overflow data. It's base pre-trained model was the cross-encoder/ms-marco-... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-subjqa-vanilla-movies-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-que... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-vanilla-movies-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T13:09:50+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-subjqa-vanilla-movies-qg'
=================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: t5-large
* Languag... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Trainin... |
image-classification | transformers |
# dog-food-convnext-tiny-224
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
## Examp... | {"tags": ["image-classification", "pytorch", "huggingpics"], "datasets": ["sasha/dog-food"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "dog-food-convnext-tiny-224", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "Dog Food", "type": "sasha/dog-foo... | sasha/dog-food-convnext-tiny-224 | null | [
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"huggingpics",
"dataset:sasha/dog-food",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T13:10:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #convnext #image-classification #huggingpics #dataset-sasha/dog-food #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# dog-food-convnext-tiny-224
This model was trained on the 'train' split of the Dogs vs Food dataset -- try training your own using the
the demo on Google Colab!
## Example Images
#### dog
!dog
#### food
!food | [
"# dog-food-convnext-tiny-224\n\n\nThis model was trained on the 'train' split of the Dogs vs Food dataset -- try training your own using the \nthe demo on Google Colab!",
"## Example Images",
"#### dog\n\n!dog",
"#### food\n\n!food"
] | [
"TAGS\n#transformers #pytorch #tensorboard #convnext #image-classification #huggingpics #dataset-sasha/dog-food #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# dog-food-convnext-tiny-224\n\n\nThis model was trained on the 'train' split of the Dogs vs Food dataset -- try training your o... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": ... | JeremiahZ/bert-base-uncased-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"en",
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"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T13:48:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-sst2
======================
This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2478
* Accuracy: 0.9323
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: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-stsb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name":... | JeremiahZ/bert-base-uncased-stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
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"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T13:52:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-stsb
======================
This model is a fine-tuned version of bert-base-uncased on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5144
* Pearson: 0.8875
* Spearmanr: 0.8843
* Combined Score: 0.8859
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilrubert_tiny-2nd-finetune-epru
This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_sin... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert_tiny-2nd-finetune-epru", "results": []}]} | mmillet/distilrubert_tiny-2nd-finetune-epru | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T13:53:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilrubert\_tiny-2nd-finetune-epru
====================================
This model is a fine-tuned version of mmillet/distilrubert-tiny-cased-conversational-v1\_single\_finetuned\_on\_cedr\_augmented on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4467
* Accuracy: 0.8712
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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: 0.0001\n* train\\_batch\\_size: 64\n* ... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | fabianmmueller/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T13:55:58+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-Ru-Golos
The Wav2Vec2 model is based on [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), fine-tuned in Russian using [Sberdevices Golos](https://huggingface.co/datasets/SberDevices/Golos) with audio augmentations like as pitch shift, acceleration/deceleration... | {"language": "ru", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["SberDevices/Golos", "bond005/sova_rudevices", "bond005/rulibrispeech"], "metrics": ["wer", "cer"], "widget": [{"example_title": "test sound with Russian speech \"\u043d\u0435\u... | bond005/wav2vec2-large-ru-golos | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ru",
"dataset:SberDevices/Golos",
"dataset:bond005/sova_rudevices",
"dataset:bond005/rulibrispeech",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space... | null | 2022-06-21T14:26:37+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-SberDevices/Golos #dataset-bond005/sova_rudevices #dataset-bond005/rulibrispeech #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large-Ru-Golos
=======================
The Wav2Vec2 model is based on facebook/wav2vec2-large-xlsr-53, fine-tuned in Russian using Sberdevices Golos with audio augmentations like as pitch shift, acceleration/deceleration of sound, reverberation etc.
When using this model, make sure that your speech input i... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-SberDevices/Golos #dataset-bond005/sova_rudevices #dataset-bond005/rulibrispeech #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-subjqa-vanilla-restaurants-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/asahi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-vanilla-restaurants-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T14:28:41+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-subjqa-vanilla-restaurants-qg'
======================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Language model: t5-... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTrain... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Trainin... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | mmartu/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T14:32:56+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | null | # Why now is a good time for startups to release their AI solutions
As AI technology continues to advance rapidly, there has never been a better time for startups to release their own AI solutions. Here are a few reasons why:
## Increased demand for AI solutions
More and more businesses are recognizing the potential... | {"license": "afl-3.0"} | ped4enko/dall-e-2 | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-06-21T14:33:49+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| # Why now is a good time for startups to release their AI solutions
As AI technology continues to advance rapidly, there has never been a better time for startups to release their own AI solutions. Here are a few reasons why:
## Increased demand for AI solutions
More and more businesses are recognizing the potential... | [
"# Why now is a good time for startups to release their AI solutions\n\nAs AI technology continues to advance rapidly, there has never been a better time for startups to release their own AI solutions. Here are a few reasons why:",
"## Increased demand for AI solutions\n\nMore and more businesses are recognizing ... | [
"TAGS\n#license-afl-3.0 #region-us \n",
"# Why now is a good time for startups to release their AI solutions\n\nAs AI technology continues to advance rapidly, there has never been a better time for startups to release their own AI solutions. Here are a few reasons why:",
"## Increased demand for AI solutions\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. -->
# bert-base-uncased-qnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": ... | JeremiahZ/bert-base-uncased-qnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:25:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-qnli
======================
This model is a fine-tuned version of bert-base-uncased on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3208
* Accuracy: 0.9125
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: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-wnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": ... | JeremiahZ/bert-base-uncased-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:25:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-wnli
======================
This model is a fine-tuned version of bert-base-uncased on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6959
* Accuracy: 0.5634
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: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-mnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": ... | JeremiahZ/bert-base-uncased-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:26:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-mnli
======================
This model is a fine-tuned version of bert-base-uncased on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4056
* Accuracy: 0.8501
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: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | vjeansel/dqn-SI | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T15:28:08+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-qqp
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"na... | JeremiahZ/bert-base-uncased-qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:28:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-qqp
=====================
This model is a fine-tuned version of bert-base-uncased on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2829
* Accuracy: 0.9100
* F1: 0.8788
* Combined Score: 0.8944
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
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. -->
# test-masca
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "test-masca", "results": []}]} | Mascariddu8/test-masca | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:41:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test-masca
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follow... | [
"# test-masca\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-masca\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model description\n\nMor... |
token-classification | transformers |
# Disease mention recognizer for Spanish clinical texts 🦠🔬
This model derives from participation of SINAI team in [DISease TExt Mining Shared Task (DISTEMIST)](https://temu.bsc.es/distemist/). The DISTEMIST-entities subtrack required automatically finding disease mentions in clinical cases. Taking into account the ... | {"language": ["es"], "license": "cc-by-4.0", "tags": ["biomedical", "clinical", "ner"], "metrics": ["f1"], "widget": [{"text": "Se realiz\u00f3 angiotomograf\u00eda urgente de arterias pulmonares, que mostr\u00f3 tromboembolia pulmonar bilateral con dilataci\u00f3n ventricular derecha, adem\u00e1s de opacidades perif\u... | chizhikchi/Spanish_disease_finder | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"biomedical",
"clinical",
"ner",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:47:26+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #biomedical #clinical #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Disease mention recognizer for Spanish clinical texts
=====================================================
This model derives from participation of SINAI team in DISease TExt Mining Shared Task (DISTEMIST). The DISTEMIST-entities subtrack required automatically finding disease mentions in clinical cases. Taking into... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #biomedical #clinical #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364
## Validation Metrics
- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9... | {"language": "en", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-mlsec"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 33.183779535405364} | deepesh0x/autotrain-mlsec-1013333726 | null | [
"transformers",
"pytorch",
"julien",
"text-classification",
"autotrain",
"en",
"dataset:deepesh0x/autotrain-data-mlsec",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:55:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #julien #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-mlsec #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364
## Validation Metrics
- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013333726\n- CO2 Emissions (in grams): 33.183779535405364",
"## Validation Metrics\n\n- Loss: 0.1998898833990097\n- Accuracy: 0.9226923076923077\n- Precision: 0.9269808389435525\n- Recall: 0.9177134068187645\n- AUC: 0.97853809... | [
"TAGS\n#transformers #pytorch #julien #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-mlsec #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013333726\n- CO2 Emissions (in gra... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013333734
- CO2 Emissions (in grams): 308.7012650779217
## Validation Metrics
- Loss: 0.20877738296985626
- Accuracy: 0.9396153846153846
- Precision: 0.9291791791791791
- Recall: 0.9518072289156626
- AUC: 0.9671522989580735
- F1: 0.9... | {"language": "en", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-mlsec"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 308.7012650779217} | deepesh0x/autotrain-mlsec-1013333734 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:deepesh0x/autotrain-data-mlsec",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T15:56:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-mlsec #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013333734
- CO2 Emissions (in grams): 308.7012650779217
## Validation Metrics
- Loss: 0.20877738296985626
- Accuracy: 0.9396153846153846
- Precision: 0.9291791791791791
- Recall: 0.9518072289156626
- AUC: 0.9671522989580735
- F1: 0.9... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013333734\n- CO2 Emissions (in grams): 308.7012650779217",
"## Validation Metrics\n\n- Loss: 0.20877738296985626\n- Accuracy: 0.9396153846153846\n- Precision: 0.9291791791791791\n- Recall: 0.9518072289156626\n- AUC: 0.96715229... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-mlsec #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013333734\n- CO2 Emissions (in gr... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-subjqa-vanilla-tripadvisor-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/asahi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-vanilla-tripadvisor-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T16:19:11+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-subjqa-vanilla-tripadvisor-qg'
======================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Language model: t5-... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTrain... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Trainin... |
text-generation | transformers |
# Luke DialoGPT Model | {"tags": ["conversational"]} | Laggrif/DialoGPT-medium-Luke | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T16:23:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Luke DialoGPT Model | [
"# Luke DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Luke DialoGPT Model"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013533786
- CO2 Emissions (in grams): 57.79463560530838
## Validation Metrics
- Loss: 0.18257243931293488
- Accuracy: 0.9261538461538461
- Precision: 0.9244319632371713
- Recall: 0.9282235324275827
- AUC: 0.9800523984255356
- F1: 0.9... | {"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-GlueFineTunedModel"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 57.79463560530838} | deepesh0x/autotrain-GlueFineTunedModel-1013533786 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:deepesh0x/autotrain-data-GlueFineTunedModel",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T16:38:16+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-GlueFineTunedModel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013533786
- CO2 Emissions (in grams): 57.79463560530838
## Validation Metrics
- Loss: 0.18257243931293488
- Accuracy: 0.9261538461538461
- Precision: 0.9244319632371713
- Recall: 0.9282235324275827
- AUC: 0.9800523984255356
- F1: 0.9... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013533786\n- CO2 Emissions (in grams): 57.79463560530838",
"## Validation Metrics\n\n- Loss: 0.18257243931293488\n- Accuracy: 0.9261538461538461\n- Precision: 0.9244319632371713\n- Recall: 0.9282235324275827\n- AUC: 0.98005239... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-GlueFineTunedModel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013533786\n- CO2 Emiss... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013533798
- CO2 Emissions (in grams): 56.65990763623749
## Validation Metrics
- Loss: 0.693366527557373
- Accuracy: 0.4998717948717949
- Precision: 0.0
- Recall: 0.0
- AUC: 0.5
- F1: 0.0
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-GlueFineTunedModel"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 56.65990763623749} | deepesh0x/autotrain-GlueFineTunedModel-1013533798 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:deepesh0x/autotrain-data-GlueFineTunedModel",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T16:49:25+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-GlueFineTunedModel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013533798
- CO2 Emissions (in grams): 56.65990763623749
## Validation Metrics
- Loss: 0.693366527557373
- Accuracy: 0.4998717948717949
- Precision: 0.0
- Recall: 0.0
- AUC: 0.5
- F1: 0.0
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013533798\n- CO2 Emissions (in grams): 56.65990763623749",
"## Validation Metrics\n\n- Loss: 0.693366527557373\n- Accuracy: 0.4998717948717949\n- Precision: 0.0\n- Recall: 0.0\n- AUC: 0.5\n- F1: 0.0",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-GlueFineTunedModel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1013533798\n- CO2 Emiss... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | ubermenchh/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T18:29:57+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
translation | keras |
## Keras Implementation of Character-level recurrent sequence-to-sequence model
This repo contains the model and the notebook [to this Keras example on Character-level recurrent sequence-to-sequence model](https://keras.io/examples/nlp/lstm_seq2seq/).
Full credits to : [fchollet](https://twitter.com/fchollet)
Mode... | {"language": ["en", "fr"], "license": "apache-2.0", "library_name": "keras", "tags": ["seq2seq", "translation"]} | sumedh/lstm-seq2seq | null | [
"keras",
"tensorboard",
"seq2seq",
"translation",
"en",
"fr",
"license:apache-2.0",
"region:us"
] | null | 2022-06-21T19:21:20+00:00 | [] | [
"en",
"fr"
] | TAGS
#keras #tensorboard #seq2seq #translation #en #fr #license-apache-2.0 #region-us
| Keras Implementation of Character-level recurrent sequence-to-sequence model
----------------------------------------------------------------------------
This repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.
Full credits to : fchollet
Model re... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #seq2seq #translation #en #fr #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad_v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad_v2", "results": []}]} | wiselinjayajos/distilbert-base-uncased-finetuned-squad_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T19:38:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad\_v2
===========================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3949
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text-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. -->
# ECHR_test_2_task_B
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["lex_glue"], "model-index": [{"name": "ECHR_test_2_task_B", "results": []}]} | QuentinKemperino/ECHR_test_2_task_B | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:lex_glue",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T19:56:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-lex_glue #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ECHR\_test\_2\_task\_B
======================
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the lex\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2092
* Macro-f1: 0.5250
* Micro-f1: 0.6190
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-lex_glue #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-0... |
null | transformers |
# LeBenchmark 2.0: wav2vec2 large model trained on 14K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended ver... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec2-FR-14K-large | null | [
"transformers",
"wav2vec2",
"fr",
"arxiv:2309.05472",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T19:56:53+00:00 | [
"2309.05472"
] | [
"fr"
] | TAGS
#transformers #wav2vec2 #fr #arxiv-2309.05472 #license-apache-2.0 #endpoints_compatible #region-us
|
# LeBenchmark 2.0: wav2vec2 large model trained on 14K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended ver... | [
"# LeBenchmark 2.0: wav2vec2 large model trained on 14K hours of French speech\n\n \n\nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an exte... | [
"TAGS\n#transformers #wav2vec2 #fr #arxiv-2309.05472 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# LeBenchmark 2.0: wav2vec2 large model trained on 14K hours of French speech\n\n \n\nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous,... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Alian3785/dqn-SpaceInvadersNoFrameskip-v4new | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-21T20:30:23+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-generation | transformers |
# C-3PO DialoGPT Model | {"tags": ["conversational"]} | Laggrif/DialoGPT-medium-3PO | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T20:39:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# C-3PO DialoGPT Model | [
"# C-3PO DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# C-3PO DialoGPT Model"
] |
text-generation | transformers | # The world machine DialoGPT model | {"tags": ["conversational"]} | ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-21T20:57:55+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # The world machine DialoGPT model | [
"# The world machine DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# The world machine DialoGPT model"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1015534072
- CO2 Emissions (in grams): 0.013170440014043236
## Validation Metrics
- Loss: 1.493847370147705
- Accuracy: 0.7333333333333333
- Macro F1: 0.6777777777777777
- Micro F1: 0.7333333333333333
- Weighted F1: 0.67777777777... | {"language": "en", "tags": "autotrain", "datasets": ["lucianpopa/autotrain-data-qn-classification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.013170440014043236} | lucianpopa/autotrain-qn-classification-1015534072 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:lucianpopa/autotrain-data-qn-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T21:23:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-lucianpopa/autotrain-data-qn-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1015534072
- CO2 Emissions (in grams): 0.013170440014043236
## Validation Metrics
- Loss: 1.493847370147705
- Accuracy: 0.7333333333333333
- Macro F1: 0.6777777777777777
- Micro F1: 0.7333333333333333
- Weighted F1: 0.67777777777... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1015534072\n- CO2 Emissions (in grams): 0.013170440014043236",
"## Validation Metrics\n\n- Loss: 1.493847370147705\n- Accuracy: 0.7333333333333333\n- Macro F1: 0.6777777777777777\n- Micro F1: 0.7333333333333333\n- Weighted... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-lucianpopa/autotrain-data-qn-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1015534072\n- CO... |
null | null | Mick Lynch ocean beach club | {} | CliveMart/Clive1 | null | [
"region:us"
] | null | 2022-06-21T22:37:26+00:00 | [] | [] | TAGS
#region-us
| Mick Lynch ocean beach club | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
# roberta-large-japanese-aozora-ud-head
## Model Description
This is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [roberta-large-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora-char) and [UD_... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b66\u5e74\u306b... | KoichiYasuoka/roberta-large-japanese-aozora-ud-head | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"japanese",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T23:49:08+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# roberta-large-japanese-aozora-ud-head
## Model Description
This is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from roberta-large-japanese-aozora-char and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when specify... | [
"# roberta-large-japanese-aozora-ud-head",
"## Model Description\n\nThis is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from roberta-large-japanese-aozora-char and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity wh... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# roberta-large-japanese-aozora-ud-head",
"## Model Description\n\nThis is a RoBERTa model pretrained on 青空文庫 for dependency-p... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MIX3_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MIX3_ja-en_helsinki", "results": []}]} | twieland/MIX3_ja-en_helsinki | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-21T23:54:09+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MIX3\_ja-en\_helsinki
=====================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4832
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: 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: 4\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #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: 0.0003\n* train\\_batch\\_size: 64... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-squadshifts-new_wiki-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https:... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-new_wiki-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T00:15:52+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-squadshifts-new\_wiki-qg'
========================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: new\_wiki) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (new\\_wiki)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data:... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-squadshifts-vanilla-new_wiki-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via ... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-vanilla-new_wiki-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T00:24:52+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-squadshifts-vanilla-new\_wiki-qg'
===========================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squadshifts (dataset\_name: new\_wiki) via 'lmqg'.
### Overview... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (new\\_wiki)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric fi... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: l... |
text-classification | pytorch |
# MyModelName
asdf | {"language": "en", "license": "mit", "library_name": "pytorch", "tags": "text-classification", "datasets": "glue", "metrics": "acc"} | yourusername/push-to-hub-68284633-43ff-45ca-9300-ea115e5ed1ff | null | [
"pytorch",
"text-classification",
"en",
"dataset:glue",
"license:mit",
"region:us"
] | null | 2022-06-22T00:31:07+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-classification #en #dataset-glue #license-mit #region-us
|
# MyModelName
asdf | [
"# MyModelName\n\nasdf"
] | [
"TAGS\n#pytorch #text-classification #en #dataset-glue #license-mit #region-us \n",
"# MyModelName\n\nasdf"
] |
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