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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bertino-focus-assassin
This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indig... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bertino-focus-assassin", "results": []}]} | gossminn/predict-perception-bertino-focus-assassin | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:26:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bertino-focus-assassin
=========================================
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3409
* R2: 0.3205
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 47",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\... |
null | transformers |
# EVA
## Model Description
EVA is the largest open-source Chinese dialogue model with up to 2.8B parameters. The 1.0 version model is pre-trained on [WudaoCorpus-Dialog](https://resource.wudaoai.cn/home), and the 2.0 version is pre-trained on a carefully cleaned version of WudaoCorpus-Dialog which yields better perf... | {"language": "zh", "license": "mit", "tags": ["pytorch"]} | thu-coai/EVA2.0-xlarge | null | [
"transformers",
"pytorch",
"zh",
"arxiv:2108.01547",
"arxiv:2203.09313",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:33:45+00:00 | [
"2108.01547",
"2203.09313"
] | [
"zh"
] | TAGS
#transformers #pytorch #zh #arxiv-2108.01547 #arxiv-2203.09313 #license-mit #endpoints_compatible #region-us
| EVA
===
Model Description
-----------------
EVA is the largest open-source Chinese dialogue model with up to 2.8B parameters. The 1.0 version model is pre-trained on WudaoCorpus-Dialog, and the 2.0 version is pre-trained on a carefully cleaned version of WudaoCorpus-Dialog which yields better performance than the 1... | [] | [
"TAGS\n#transformers #pytorch #zh #arxiv-2108.01547 #arxiv-2203.09313 #license-mit #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bertino-focus-victim
This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bertino-focus-victim", "results": []}]} | gossminn/predict-perception-bertino-focus-victim | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:34:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bertino-focus-victim
=======================================
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2497
* R2: 0.6131
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 47",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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. -->
# predict-perception-bertino-focus-object
This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bertino-focus-object", "results": []}]} | gossminn/predict-perception-bertino-focus-object | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:42:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bertino-focus-object
=======================================
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2766
* R2: 0.5460
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 47",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | JoonJoon/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:50:42+00:00 | [] | [] | TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0814
* Accuracy: 0.9726
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 384\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, ... | ericklerouge123/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T13:59:42+00:00 | [] | [] | TAGS
#transformers #pytorch #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-fr
=====================================
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: 0.4043
* F1: 0.6886
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #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\\_rate: 5e-05\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | jslowik/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T14:01:13+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2156
* Accuracy: 0.9265
* F1: 0.9262
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 #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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. -->
# reformer-finetuned-big_patent-wikipedia-arxiv-16384
This model is a fine-tuned version of [robingeibel/reformer-finetuned-big_pa... | {"tags": ["generated_from_trainer"], "datasets": ["wikipedia"], "model-index": [{"name": "reformer-finetuned-big_patent-wikipedia-arxiv-16384", "results": []}]} | robingeibel/reformer-finetuned-big_patent-wikipedia-arxiv-16384 | null | [
"transformers",
"pytorch",
"tensorboard",
"reformer",
"fill-mask",
"generated_from_trainer",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T14:11:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #reformer #fill-mask #generated_from_trainer #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us
| reformer-finetuned-big\_patent-wikipedia-arxiv-16384
====================================================
This model is a fine-tuned version of robingeibel/reformer-finetuned-big\_patent-wikipedia-arxiv-16384 on the wikipedia dataset.
It achieves the following results on the evaluation set:
* Loss: 6.5256
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #reformer #fill-mask #generated_from_trainer #dataset-wikipedia #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-06\n* train\\_batch\\_size... |
null | null | Blue with spikes | {} | Furrydestroyer/Sonic | null | [
"region:us"
] | null | 2022-07-14T14:19:53+00:00 | [] | [] | TAGS
#region-us
| Blue with spikes | [] | [
"TAGS\n#region-us \n"
] |
image-classification | transformers |
# rare-puppers3
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggin... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Samlit/rare-puppers3 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T14:39:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers3
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Marcelle Lender doing the Bolero in Chilperic
!Marcelle Lender doing the Bolero in Chilperic
#### Moulin ... | [
"# rare-puppers3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Marcelle Lender doing the Bolero in Chilperic\n\n!Marcelle Lender doing the Bolero in Chil... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... |
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. -->
# pixel-base-finetuned-korquadv1
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/p... | {"tags": ["generated_from_trainer"], "datasets": ["squad_kor_v1"], "model-index": [{"name": "pixel-base-finetuned-korquadv1", "results": []}]} | Team-PIXEL/pixel-base-finetuned-korquadv1 | null | [
"transformers",
"pytorch",
"pixel",
"question-answering",
"generated_from_trainer",
"dataset:squad_kor_v1",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T14:55:25+00:00 | [] | [] | TAGS
#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-squad_kor_v1 #endpoints_compatible #region-us
|
# pixel-base-finetuned-korquadv1
This model is a fine-tuned version of Team-PIXEL/pixel-base on the squad_kor_v1 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# pixel-base-finetuned-korquadv1\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the squad_kor_v1 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-squad_kor_v1 #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-korquadv1\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the squad_kor_v1 dataset.",
"## Model description\n\nMore information n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | yunbaree/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T15:01:48+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: 0.2244
* Accuracy: 0.924
* F1: 0.9240
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... |
null | fastai |
# Model card
## Model description
"While the cuisine of Ethiopia is gradually becoming better known, it's no overstatement to say it remains one of the world's best-kept secrets." [CNN](https://edition.cnn.com/travel/article/ethiopian-food-best-dishes-africa/index.html)
This model is an Ethiopian food image cla... | {"tags": ["fastai", "Weights & Biases", "Ethiopian", "Food", "Classifier"]} | hugginglearners/Ethiopian-Food-Classifier | null | [
"fastai",
"Weights & Biases",
"Ethiopian",
"Food",
"Classifier",
"region:us"
] | null | 2022-07-14T15:03:30+00:00 | [] | [] | TAGS
#fastai #Weights & Biases #Ethiopian #Food #Classifier #region-us
|
# Model card
## Model description
"While the cuisine of Ethiopia is gradually becoming better known, it's no overstatement to say it remains one of the world's best-kept secrets." CNN
This model is an Ethiopian food image classifier trained on the following food categories:
- Beyaynetu
- Chechebsa
- Doro wat
- ... | [
"# Model card",
"## Model description\n\"While the cuisine of Ethiopia is gradually becoming better known, it's no overstatement to say it remains one of the world's best-kept secrets.\" CNN\n\nThis model is an Ethiopian food image classifier trained on the following food categories:\n- Beyaynetu\n- Chechebsa\n- ... | [
"TAGS\n#fastai #Weights & Biases #Ethiopian #Food #Classifier #region-us \n",
"# Model card",
"## Model description\n\"While the cuisine of Ethiopia is gradually becoming better known, it's no overstatement to say it remains one of the world's best-kept secrets.\" CNN\n\nThis model is an Ethiopian food image cl... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | dbarbedillo/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-14T15:04:46+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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. -->
# pixel-base-finetuned-jaquad
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixe... | {"tags": ["generated_from_trainer"], "datasets": ["SkelterLabsInc/JaQuAD"], "model-index": [{"name": "pixel-base-finetuned-jaquad", "results": []}]} | Team-PIXEL/pixel-base-finetuned-jaquad | null | [
"transformers",
"pytorch",
"pixel",
"question-answering",
"generated_from_trainer",
"dataset:SkelterLabsInc/JaQuAD",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T15:05:07+00:00 | [] | [] | TAGS
#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-SkelterLabsInc/JaQuAD #endpoints_compatible #region-us
|
# pixel-base-finetuned-jaquad
This model is a fine-tuned version of Team-PIXEL/pixel-base on the SkelterLabsInc/JaQuAD dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# pixel-base-finetuned-jaquad\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the SkelterLabsInc/JaQuAD dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #pixel #question-answering #generated_from_trainer #dataset-SkelterLabsInc/JaQuAD #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-jaquad\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the SkelterLabsInc/JaQuAD dataset.",
"## Model description\n\nMor... |
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-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | ericklerouge123/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T15:18:06+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1348
* F1: 0.8844
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 #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
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-cased-cv-studio_name-medium
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Egresado de la carrera Ingenier\u00eda en Computaci\u00f3n Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en \u00e1mbitos de redes de peque\u00f1a y mediana escala. Ingl\u00e9s H... | jhonparra18/bert-base-cased-cv-studio_name-medium | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T16:14:23+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-cv-studio\_name-medium
======================================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3310
* F1 Micro: 0.6388
* F1 Macro: 0.5001
Model description
-----------------
Predicts a st... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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 #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* ... |
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. -->
# finetuned_parsinlu_en_fa
This model is a fine-tuned version of [persiannlp/mt5-small-parsinlu-translation_en_fa](https://hugging... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "finetuned_parsinlu_en_fa", "results": []}]} | mhdr78/finetuned_parsinlu_en_fa | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-14T16:26:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| finetuned\_parsinlu\_en\_fa
===========================
This model is a fine-tuned version of persiannlp/mt5-small-parsinlu-translation\_en\_fa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5214
* Bleu: 13.5318
* Gen Len: 12.1251
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k-Chinese-finetuned
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-base-patch4-window7-224-in22k-Chinese-finetuned", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "... | liyijing024/swin-base-patch4-window7-224-in22k-Chinese-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T16:28:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-base-patch4-window7-224-in22k-Chinese-finetuned
====================================================
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Accuracy: 1.0
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
null | null | This is a modified adverse event classifier using binary classification. | {} | budgekins/ae-classification | null | [
"region:us"
] | null | 2022-07-14T16:43:33+00:00 | [] | [] | TAGS
#region-us
| This is a modified adverse event classifier using binary classification. | [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | benji2264/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-14T17:00:58+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Overview
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [M... | {"language": "en", "tags": ["generated_from_trainer"], "datasets": ["allenai/mslr2022"], "base_model": "allenai/led-base-16384", "model-index": [{"name": "baseline", "results": []}]} | allenai/led-base-16384-ms2 | null | [
"transformers",
"pytorch",
"safetensors",
"led",
"text2text-generation",
"generated_from_trainer",
"en",
"dataset:allenai/mslr2022",
"arxiv:2104.06486",
"base_model:allenai/led-base-16384",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-14T17:36:40+00:00 | [
"2104.06486"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #led #text2text-generation #generated_from_trainer #en #dataset-allenai/mslr2022 #arxiv-2104.06486 #base_model-allenai/led-base-16384 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Overview
This model is a fine-tuned version of allenai/led-base-16384 on the MS^2 dataset. The model received as input the background section and the titles and abstracts of up to 25 included studies for each example, concatenated by the '"</s>"' token. Global attention is applied to the special start token '"<s>... | [
"# Overview\n\nThis model is a fine-tuned version of allenai/led-base-16384 on the MS^2 dataset. The model received as input the background section and the titles and abstracts of up to 25 included studies for each example, concatenated by the '\"</s>\"' token. Global attention is applied to the special start token... | [
"TAGS\n#transformers #pytorch #safetensors #led #text2text-generation #generated_from_trainer #en #dataset-allenai/mslr2022 #arxiv-2104.06486 #base_model-allenai/led-base-16384 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Overview\n\nThis model is a fine-tuned version of allenai/led-b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pixel-base-finetuned-sst2
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "pixel-base-finetuned-sst2", "results": []}]} | Team-PIXEL/pixel-base-finetuned-sst2 | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T18:14:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-sst2
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE SST2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# pixel-base-finetuned-sst2\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE SST2 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-sst2\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE SST2 dataset.",
"## Model description\n\nMore in... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | meln1k/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-14T18:27:50+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
null | null | Hosts the pre-tained extracted model from glove.twitter.27B.100d.txt from https://huggingface.co/stanfordnlp/glove/tree/main
Used in: https://github.com/Juliano-rb/experiments_fault_injection_mlaas | {} | Juliano/fault_injection_mlaas | null | [
"region:us"
] | null | 2022-07-14T18:40:59+00:00 | [] | [] | TAGS
#region-us
| Hosts the pre-tained extracted model from URL from URL
Used in: URL | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
# roberta-small-hi-char-mlm
## Model Description
This is a RoBERTa model pre-trained on HI texts with character tokenizer.
This uses `is_decoder=False` option.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("nakamura196/roberta-small-hi-char-... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u5165[MASK]\u5916\u7121\u4e4b\u5019\u6c5f\u6238\u5927\u6c34\u53c8\u30cf\u5927\u5730\u9707\u306a\u3068"}, {"text": "\u65e5[MASK]\u5b88\u5fa1\u671b\u4e4b\u7531\u53e... | nakamura196/roberta-small-hi-char-mlm | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T19:34:59+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-small-hi-char-mlm
## Model Description
This is a RoBERTa model pre-trained on HI texts with character tokenizer.
This uses 'is_decoder=False' option.
## How to Use
| [
"# roberta-small-hi-char-mlm",
"## Model Description\n\nThis is a RoBERTa model pre-trained on HI texts with character tokenizer.\nThis uses 'is_decoder=False' option.",
"## How to Use"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-small-hi-char-mlm",
"## Model Description\n\nThis is a RoBERTa model pre-trained on HI texts with character tokenizer.\nThis uses 'is_decoder=Fa... |
image-classification | timm | # Model card for resnet18-random | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/resnet18-random | null | [
"timm",
"pytorch",
"image-classification",
"has_space",
"region:us"
] | null | 2022-07-14T19:46:39+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #has_space #region-us
| # Model card for resnet18-random | [
"# Model card for resnet18-random"
] | [
"TAGS\n#timm #pytorch #image-classification #has_space #region-us \n",
"# Model card for resnet18-random"
] |
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-cased-wikitext2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | JoonJoon/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T19:46:53+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.9846
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* see... |
null | null |
# Predicting Effects and Aromas
<div align="center" style="text-align:center; margin-top:1rem; margin-bottom: 1rem;">
<img width="240px" alt="" src="https://firebasestorage.googleapis.com/v0/b/cannlytics.appspot.com/o/public%2Fimages%2Flogos%2Fskunkfx_logo.png?alt=media&token=1a75b3cc-3230-446c-be7d-5c06012c8e30">
... | {"license": "mit"} | cannlytics/skunkfx | null | [
"license:mit",
"region:us"
] | null | 2022-07-14T19:54:17+00:00 | [] | [] | TAGS
#license-mit #region-us
| Predicting Effects and Aromas
=============================
![](URL
</div>
<blockquote>
<p>"It's been hard to breathe and the smell's been just horrendous... [It's] like you've literally been sprayed by a
skunk." - Resident of Prague, Oklahoma in
<em>'It's nasty': Prague neighbors push back on area cannabis facility... | [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-classification | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-cv-studio_name-medium
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Egresado de la carrera Ingenier\u00eda en Computaci\u00f3n Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en \u00e1mbitos de redes de peque\u00f1a y mediana escala. Ingl\u00e9s H... | jhonparra18/roberta-base-cv-studio_name-medium | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T20:02:40+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-cased-cv-studio_name-medium
This model is a fine-tuned version of roberta-base on the None dataset.
## Model description
Predicts a studio name based on a CV text
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eva... | [
"# bert-base-cased-cv-studio_name-medium\n\nThis model is a fine-tuned version of roberta-base on the None dataset.",
"## Model description\n\nPredicts a studio name based on a CV text",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_ba... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-cased-cv-studio_name-medium\n\nThis model is a fine-tuned version of roberta-base on the None dataset.",
"## Model description\n\nPredi... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-paraphrase-paws-msrp-opinosis-finetuned-parasci
This model is a fine-tuned version of [ceshine/t5-paraphrase-paws-msrp-opinos... | {"license": "apache-2.0", "tags": ["paraphrasing", "generated_from_trainer"], "model-index": [{"name": "t5-paraphrase-paws-msrp-opinosis-finetuned-parasci", "results": []}]} | domenicrosati/t5-paraphrase-paws-msrp-opinosis-finetuned-parasci | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"paraphrasing",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-14T21:29:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #paraphrasing #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-paraphrase-paws-msrp-opinosis-finetuned-parasci
This model is a fine-tuned version of ceshine/t5-paraphrase-paws-msrp-opinosis on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed... | [
"# t5-paraphrase-paws-msrp-opinosis-finetuned-parasci\n\nThis model is a fine-tuned version of ceshine/t5-paraphrase-paws-msrp-opinosis on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nM... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #paraphrasing #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-paraphrase-paws-msrp-opinosis-finetuned-parasci\n\nThis model is a fine-tuned version of ceshi... |
token-classification | flair |
This is a very small model I use for testing my [ner eval dashboard](https://github.com/helpmefindaname/ner-eval-dashboard)
F1-Score: **48,73** (CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC ... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]} | helpmefindaname/mini-sequence-tagger-conll03 | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"region:us"
] | null | 2022-07-14T22:30:10+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #region-us
| This is a very small model I use for testing my ner eval dashboard
F1-Score: 48,73 (CoNLL-03)
Predicts 4 tags:
Based on huggingface minimal testing embeddings
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"##... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labe... |
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-large-uncased-finetuned-youcook_1
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-la... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-large-uncased-finetuned-youcook_1", "results": []}]} | CennetOguz/bert-large-uncased-finetuned-youcook_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T22:57:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-large-uncased-finetuned-youcook\_1
=======================================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9929
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 5\n... |
text-generation | transformers |
# Rick DialoGPt Model | {"tags": ["conversational"]} | Rick-C137/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-14T23:03:54+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPt Model | [
"# Rick DialoGPt Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPt Model"
] |
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-large-uncased-finetuned-youcook_2
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-la... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-large-uncased-finetuned-youcook_2", "results": []}]} | CennetOguz/bert-large-uncased-finetuned-youcook_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T23:08:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-large-uncased-finetuned-youcook\_2
=======================================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9929
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 5\n... |
null | nemo |
# NVIDIA TitaNet-Large (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Language](h... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["speaker", "speech", "audio", "speaker-verification", "speaker-recognition", "speaker-diarization", "titanet", "NeMo", "pytorch"], "datasets": ["VOXCELEB-1", "VOXCELEB-2", "FISHER", "switchboard", "librispeech_asr", "SRE"], "widget": [{"src":... | nvidia/speakerverification_en_titanet_large | null | [
"nemo",
"speaker",
"speech",
"audio",
"speaker-verification",
"speaker-recognition",
"speaker-diarization",
"titanet",
"NeMo",
"pytorch",
"en",
"dataset:VOXCELEB-1",
"dataset:VOXCELEB-2",
"dataset:FISHER",
"dataset:switchboard",
"dataset:librispeech_asr",
"dataset:SRE",
"license:cc... | null | 2022-07-14T23:26:00+00:00 | [] | [
"en"
] | TAGS
#nemo #speaker #speech #audio #speaker-verification #speaker-recognition #speaker-diarization #titanet #NeMo #pytorch #en #dataset-VOXCELEB-1 #dataset-VOXCELEB-2 #dataset-FISHER #dataset-switchboard #dataset-librispeech_asr #dataset-SRE #license-cc-by-4.0 #model-index #has_space #region-us
|
# NVIDIA TitaNet-Large (en-US)
<style>
img {
display: inline;
}
</style>
| 
| 
| 
This model extracts speaker embeddings from given speech, which is the backbone for speaker verification and diarization tasks.
It is ... | [
"# NVIDIA TitaNet-Large (en-US)\n\n<style>\nimg {\n display: inline;\n}\n</style>\n\n| \n| \n| \n\n\nThis model extracts speaker embeddings from given speech, which is the backbone for speaker verification and diarizat... | [
"TAGS\n#nemo #speaker #speech #audio #speaker-verification #speaker-recognition #speaker-diarization #titanet #NeMo #pytorch #en #dataset-VOXCELEB-1 #dataset-VOXCELEB-2 #dataset-FISHER #dataset-switchboard #dataset-librispeech_asr #dataset-SRE #license-cc-by-4.0 #model-index #has_space #region-us \n",
"# NVIDIA T... |
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-large-uncased-finetuned-youcook_4
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-la... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-large-uncased-finetuned-youcook_4", "results": []}]} | CennetOguz/bert-large-uncased-finetuned-youcook_4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T23:34:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-large-uncased-finetuned-youcook\_4
=======================================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9929
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 5\n... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln54")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln54")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln55 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T00:41:00+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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. -->
# pixel-base-finetuned-cola
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "pixel-base-finetuned-cola", "results": []}]} | Team-PIXEL/pixel-base-finetuned-cola | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T01:35:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-cola
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE COLA dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# pixel-base-finetuned-cola\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE COLA dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-cola\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE COLA dataset.",
"## Model description\n\nMore in... |
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. -->
# pixel-base-finetuned-mnli
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "pixel-base-finetuned-mnli", "results": []}]} | Team-PIXEL/pixel-base-finetuned-mnli | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T01:39:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-mnli
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE MNLI dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# pixel-base-finetuned-mnli\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE MNLI dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-mnli\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE MNLI dataset.",
"## Model description\n\nMore in... |
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. -->
# pixel-base-finetuned-mrpc
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "pixel-base-finetuned-mrpc", "results": []}]} | Team-PIXEL/pixel-base-finetuned-mrpc | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T01:43:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-mrpc
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE MRPC dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# pixel-base-finetuned-mrpc \n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE MRPC dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-mrpc \n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE MRPC dataset.",
"## Model description\n\nMore i... |
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. -->
# training
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the cynthiachan/Feed... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cynthiachan/FeedRef_10pct"], "model-index": [{"name": "training", "results": []}]} | cynthiachan/finetuned-bert-base-10pct | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:cynthiachan/FeedRef_10pct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T01:47:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-cynthiachan/FeedRef_10pct #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| training
========
This model is a fine-tuned version of bert-base-cased on the cynthiachan/FeedRef\_10pct dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1291
* Attackid Precision: 1.0
* Attackid Recall: 1.0
* Attackid F1: 1.0
* Attackid Number: 6
* Cve Precision: 0.8333
* Cve Recall: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-cynthiachan/FeedRef_10pct #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: 5... |
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. -->
# pixel-base-finetuned-qnli
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "pixel-base-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "metrics": ... | Team-PIXEL/pixel-base-finetuned-qnli | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T01:50:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| pixel-base-finetuned-qnli
=========================
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9503
* Accuracy: 0.8860
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 64\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 #pixel #text-classification #generated_from_trainer #en #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: 3e-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. -->
# 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... | ecnmchedsgn/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T01:52:22+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: 0.2237
* Accuracy: 0.929
* F1: 0.9290
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... |
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. -->
# pixel-base-finetuned-rte
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-b... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "pixel-base-finetuned-rte", "results": []}]} | Team-PIXEL/pixel-base-finetuned-rte | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T01:57:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-rte
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE RTE dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# pixel-base-finetuned-rte\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE RTE dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-rte\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE RTE dataset.",
"## Model description\n\nMore info... |
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. -->
# pixel-base-finetuned-stsb
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "pixel-base-finetuned-stsb", "results": []}]} | Team-PIXEL/pixel-base-finetuned-stsb | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T02:02:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-stsb
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE STSB dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# pixel-base-finetuned-stsb\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE STSB dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-stsb\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE STSB dataset.",
"## Model description\n\nMore in... |
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. -->
# pixel-base-finetuned-wnli
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "pixel-base-finetuned-wnli", "results": []}]} | Team-PIXEL/pixel-base-finetuned-wnli | null | [
"transformers",
"pytorch",
"pixel",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T02:06:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
|
# pixel-base-finetuned-wnli
This model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE WNLI dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# pixel-base-finetuned-wnli\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE WNLI dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #pixel #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us \n",
"# pixel-base-finetuned-wnli\n\nThis model is a fine-tuned version of Team-PIXEL/pixel-base on the GLUE WNLI dataset.",
"## Model description\n\nMore in... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hardik1313X/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Hardik1313X/mt5-small-finetuned-amazon-en-es", "results": []}]} | Hardik1313X/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T03:16:10+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hardik1313X/mt5-small-finetuned-amazon-en-es
============================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.0749
* Validation Loss: 3.3854
* Epoch: 7
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
text-generation | transformers |
side project, not final working good product
see [this repo](https://huggingface.co/crumb/gpt-j-6b-shakespeare) for more information, finetuned with [8-bit Adam](https://arxiv.org/abs/2110.02861) on custom transformed super glue tasks, transformed somewhat like the [T0 paper](https://arxiv.org/abs/2110.08207)
```pyt... | {"language": ["en"], "tags": ["pytorch", "causal-lm", "8bit"], "datasets": ["The Pile", "super_glue"], "inference": false} | crumb/gpt-j-6b-finetune-super-glue | null | [
"transformers",
"pytorch",
"tensorboard",
"gptj",
"text-generation",
"causal-lm",
"8bit",
"en",
"arxiv:2110.02861",
"arxiv:2110.08207",
"autotrain_compatible",
"region:us"
] | null | 2022-07-15T03:37:47+00:00 | [
"2110.02861",
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #gptj #text-generation #causal-lm #8bit #en #arxiv-2110.02861 #arxiv-2110.08207 #autotrain_compatible #region-us
|
side project, not final working good product
see this repo for more information, finetuned with 8-bit Adam on custom transformed super glue tasks, transformed somewhat like the T0 paper
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #gptj #text-generation #causal-lm #8bit #en #arxiv-2110.02861 #arxiv-2110.08207 #autotrain_compatible #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-base-itquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gener... | {"language": "it", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_itquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere t... | lmqg/mt5-base-itquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"it",
"dataset:lmqg/qg_itquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T03:57:19+00:00 | [
"2210.03992"
] | [
"it"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #it #dataset-lmqg/qg_itquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-base-itquad-qg'
=======================================
This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg\_itquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-base
* Language: it
* Training data: lmqg/qg\_itqua... | [
"### Overview\n\n\n* Language model: google/mt5-base\n* Language: it\n* Training data: lmqg/qg\\_itquad (default)\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\n* ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #it #dataset-lmqg/qg_itquad #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: google/mt5-base\n* Language: it\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/1321337599332593664/tqNL... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/thomastrainrek/1668569886926/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/thomastrainrek | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T04:29:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
thomas the trainwreck
@thomastrainrek
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 ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-conll2003
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-conll2003", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll... | worknick/bert-base-cased-finetuned-conll2003 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T04:36:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-conll2003
===================================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0643
* Precision: 0.9410
* Recall: 0.9469
* F1: 0.9439
* Accuracy: 0.9860
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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. -->
# koelectra-base-v3-discriminator-finetuned-ner
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "koelectra-base-v3-discriminator-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "... | JoonJoon/koelectra-base-v3-discriminator-finetuned-ner | null | [
"transformers",
"pytorch",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:klue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T05:28:38+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #token-classification #generated_from_trainer #dataset-klue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| koelectra-base-v3-discriminator-finetuned-ner
=============================================
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1957
* Precision: 0.6665
* Recall: 0.7350
* F1: 0.6991
* ... | [
"### 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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #electra #token-classification #generated_from_trainer #dataset-klue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
text-generation | transformers |
# debyve/tobbmud Model | {"tags": ["conversational"]} | debyve/dumbbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T06:15:20+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# debyve/tobbmud Model | [
"# debyve/tobbmud Model"
] | [
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"# debyve/tobbmud Model"
] |
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. -->
# ViTFineTuned
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "ViTFineTuned", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "KTH-TIPS2-b", "type": "images", "args": "default"}, "me... | pthpth/ViTFineTuned | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T06:28:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| ViTFineTuned
============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the KTH-TIPS2-b dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0075
* Accuracy: 1.0
Model description
-----------------
Transfer learning by fine tuning the Vision Transformer by Goo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
tabular-classification | sklearn |
## Baseline Model trained on irisg444_4c0 to apply classification on Species
**Metrics of the best model:**
accuracy 0.953333
recall_macro 0.953333
precision_macro 0.956229
f1_macro 0.953216
Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64
**See mod... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | ieborhan/irisg444_4c0-Species-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] | null | 2022-07-15T06:42:23+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us
|
## Baseline Model trained on irisg444_4c0 to apply classification on Species
Metrics of the best model:
accuracy 0.953333
recall_macro 0.953333
precision_macro 0.956229
f1_macro 0.953216
Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64
See model plo... | [
"## Baseline Model trained on irisg444_4c0 to apply classification on Species\n\nMetrics of the best model:\n\naccuracy 0.953333\n\nrecall_macro 0.953333\n\nprecision_macro 0.956229\n\nf1_macro 0.953216\n\nName: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64\... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us \n",
"## Baseline Model trained on irisg444_4c0 to apply classification on Species\n\nMetrics of the best model:\n\naccuracy 0.953333\n\nrecall_macro 0.953333\n\nprecision_macro 0.956229\n\nf1_macro ... |
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... | affahrizain/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T07:09:49+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: 0.1365
* F1: 0.8649
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 |
Binary causal sentence classification:
* LABEL_0 = Non-causal
* LABEL_1 = Causal
Trained on multiple datasets. | {"language": "en", "license": "unknown", "widget": [{"text": "She fell because he pushed her.", "example_title": "Causal Example 1"}, {"text": "He pushed her, causing her to fall.", "example_title": "Causal Example 2"}, {"text": "She fell onto him.", "example_title": "Non-causal Example 1"}, {"text": "He is Billy and h... | tanfiona/unicausal-seq-baseline | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T07:28:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us
|
Binary causal sentence classification:
* LABEL_0 = Non-causal
* LABEL_1 = Causal
Trained on multiple datasets. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Jim from The Office | {"tags": ["conversational"]} | Amir-UL/JimBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T07:30:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jim from The Office | [
"# Jim from The Office"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jim from The Office"
] |
text-classification | transformers | This is a fine-tuned version of [codeparrot-small-multi](https://huggingface.co/codeparrot/codeparrot-small-multi), a 110M multilingual model for code generation, on [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex), a dataset for complexity prediction of Java code. | {"license": "apache-2.0", "datasets": "codeparrot/codecomplex"} | codeparrot/codeparrot-small-complexity-prediction | null | [
"transformers",
"pytorch",
"gpt2",
"text-classification",
"dataset:codeparrot/codecomplex",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T08:57:34+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-classification #dataset-codeparrot/codecomplex #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a fine-tuned version of codeparrot-small-multi, a 110M multilingual model for code generation, on CodeComplex, a dataset for complexity prediction of Java code. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-classification #dataset-codeparrot/codecomplex #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# CodeParrot 🦜 small for text-t-code generation
This model is [CodeParrot-small](https://huggingface.co/codeparrot/codeparrot-small) (from `branch megatron`) Fine-tuned on [github-jupyter-text-to-code](https://huggingface.co/datasets/codeparrot/github-jupyter-text-to-code), a dataset where the samples are a successi... | {"language": ["code"], "license": "apache-2.0", "tags": ["code", "gpt2", "generation"], "datasets": ["codeparrot/codeparrot-clean", "codeparrot/github-jupyter-text-to-code"]} | codeparrot/codeparrot-small-text-to-code | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"code",
"generation",
"dataset:codeparrot/codeparrot-clean",
"dataset:codeparrot/github-jupyter-text-to-code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T08:58:00+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean #dataset-codeparrot/github-jupyter-text-to-code #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# CodeParrot small for text-t-code generation
This model is CodeParrot-small (from 'branch megatron') Fine-tuned on github-jupyter-text-to-code, a dataset where the samples are a succession of docstrings and their Python code, originally extracted from Jupyter notebooks parsed in this dataset.
| [
"# CodeParrot small for text-t-code generation\n\nThis model is CodeParrot-small (from 'branch megatron') Fine-tuned on github-jupyter-text-to-code, a dataset where the samples are a succession of docstrings and their Python code, originally extracted from Jupyter notebooks parsed in this dataset."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean #dataset-codeparrot/github-jupyter-text-to-code #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# CodeParrot small for text-t-code gen... |
null | null | # description of MFinBERT model
This is an MFinBERT model: A Multilingual Pretrained language model for Financial Domain.
Highly Recommend for Vietnamese, English and Litthuania.
| {"license": "mit"} | sonnv/MFinBERT | null | [
"license:mit",
"region:us"
] | null | 2022-07-15T08:59:05+00:00 | [] | [] | TAGS
#license-mit #region-us
| # description of MFinBERT model
This is an MFinBERT model: A Multilingual Pretrained language model for Financial Domain.
Highly Recommend for Vietnamese, English and Litthuania.
| [
"# description of MFinBERT model\n\nThis is an MFinBERT model: A Multilingual Pretrained language model for Financial Domain.\n Highly Recommend for Vietnamese, English and Litthuania."
] | [
"TAGS\n#license-mit #region-us \n",
"# description of MFinBERT model\n\nThis is an MFinBERT model: A Multilingual Pretrained language model for Financial Domain.\n Highly Recommend for Vietnamese, English and Litthuania."
] |
null | transformers | MC-BERT is a novel conceptualized representation learning approach for the medical domain. First, we use a different mask generation procedure to mask spans of tokens, rather than only random ones. We also introduce two kinds of masking strategies, namely whole entity masking and whole span masking. Finally, MC-BERT sp... | {} | freedomking/mc-bert | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T09:04:34+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| MC-BERT is a novel conceptualized representation learning approach for the medical domain. First, we use a different mask generation procedure to mask spans of tokens, rather than only random ones. We also introduce two kinds of masking strategies, namely whole entity masking and whole span masking. Finally, MC-BERT sp... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
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. -->
# xtremedistil-l6-h256-uncased-future-time-references-D1
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](... | {"license": "mit", "tags": ["generated_from_keras_callback"], "datasets": ["jonaskoenig/trump_administration_statement", "jonaskoenig/future-time-references-static-filter-D1"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-future-time-references-D1", "results": []}]} | jonaskoenig/xtremedistil-l6-h256-uncased-future-time-references-D1 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"dataset:jonaskoenig/trump_administration_statement",
"dataset:jonaskoenig/future-time-references-static-filter-D1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T09:48:03+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/trump_administration_statement #dataset-jonaskoenig/future-time-references-static-filter-D1 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xtremedistil-l6-h256-uncased-future-time-references-D1
======================================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the jonaskoenig/trump\_administration\_statement and jonaskoenig/future-time-refernces-static-filter datsets.
It achieves the ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results\n\n\n\nThe test ac... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #dataset-jonaskoenig/trump_administration_statement #dataset-jonaskoenig/future-time-references-static-filter-D1 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe follow... |
null | transformers |
# Model Overview
This repository contains the code for Swin UNETR [1,2]. Swin UNETR is the state-of-the-art on Medical Segmentation
Decathlon (MSD) and Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset. In [1], a novel methodology is devised for pre-training Swin UNETR backbone in a self-supervised
manne... | {"language": "en", "license": "apache-2.0", "tags": ["btcv", "medical", "swin"], "datasets": ["BTCV"]} | darragh/swinunetr-btcv-small | null | [
"transformers",
"pytorch",
"btcv",
"medical",
"swin",
"en",
"dataset:BTCV",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T10:30:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #btcv #medical #swin #en #dataset-BTCV #license-apache-2.0 #endpoints_compatible #region-us
| Model Overview
==============
This repository contains the code for Swin UNETR [1,2]. Swin UNETR is the state-of-the-art on Medical Segmentation
Decathlon (MSD) and Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset. In [1], a novel methodology is devised for pre-training Swin UNETR backbone in a self-sup... | [] | [
"TAGS\n#transformers #pytorch #btcv #medical #swin #en #dataset-BTCV #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
# Model Overview
This repository contains the code for Swin UNETR [1,2]. Swin UNETR is the state-of-the-art on Medical Segmentation
Decathlon (MSD) and Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset. In [1], a novel methodology is devised for pre-training Swin UNETR backbone in a self-supervised
manne... | {"language": "en", "license": "apache-2.0", "tags": ["btcv", "medical", "swin"], "datasets": ["BTCV"]} | darragh/swinunetr-btcv-base | null | [
"transformers",
"pytorch",
"btcv",
"medical",
"swin",
"en",
"dataset:BTCV",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T10:31:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #btcv #medical #swin #en #dataset-BTCV #license-apache-2.0 #endpoints_compatible #region-us
| Model Overview
==============
This repository contains the code for Swin UNETR [1,2]. Swin UNETR is the state-of-the-art on Medical Segmentation
Decathlon (MSD) and Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset. In [1], a novel methodology is devised for pre-training Swin UNETR backbone in a self-sup... | [] | [
"TAGS\n#transformers #pytorch #btcv #medical #swin #en #dataset-BTCV #license-apache-2.0 #endpoints_compatible #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/1416640600124788736/vuYW... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/juncassis/1657925041359/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/juncassis | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T10:35:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
elevator ghost
@juncassis
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 | 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="spacestar1705/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | spacestar1705/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-15T10:37:46+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
| {"library_name": "stable-baselines3", "tags": ["Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "ty... | epsil/ppo-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-15T10:50:47+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Walker2DBulletEnv-v0
This is a trained model of a PPO agent playing Walker2DBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
| [
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)"
] | [
"TAGS\n#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baseline... |
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-imdb-mlflow
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-case... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "base_model": "distilbert-base-cased", "model-index": [{"name": "distilbert-imdb-mlflow", "results": []}]} | juliensimon/distilbert-imdb-mlflow | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T10:58:40+00:00 | [] | [] | TAGS
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|
# distilbert-imdb-mlflow
This model is a fine-tuned version of distilbert-base-cased on the imdb dataset.
MLflow logs are included. To visualize them, just clone the repo and run :
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation d... | [
"# distilbert-imdb-mlflow\n\nThis model is a fine-tuned version of distilbert-base-cased on the imdb dataset.\n\nMLflow logs are included. To visualize them, just clone the repo and run :",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trai... | [
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"# distilbert-imdb-mlflow\n\nThis model is a fine-tuned version of distilbert-base-cas... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1136241676
- CO2 Emissions (in grams): 684.7105644305452
## Validation Metrics
- Loss: 0.2270897775888443
- Rouge1: 63.4452
- Rouge2: 60.0038
- RougeL: 63.3343
- RougeLsum: 63.321
- Gen Len: 19.1562
## Usage
You can use cURL to access this ... | {"language": "en", "tags": "autotrain", "datasets": ["aalbertini1990/autotrain-data-first-test-html"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 684.7105644305452} | aalbertini1990/autotrain-first-test-html-1136241676 | null | [
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"endpoints_compatible",
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|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1136241676
- CO2 Emissions (in grams): 684.7105644305452
## Validation Metrics
- Loss: 0.2270897775888443
- Rouge1: 63.4452
- Rouge2: 60.0038
- RougeL: 63.3343
- RougeLsum: 63.321
- Gen Len: 19.1562
## Usage
You can use cURL to access this ... | [
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"## Usage\n\nYou can... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1136241676\n- CO2 Emissions (... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1136241677
- CO2 Emissions (in grams): 19.49742293318862
## Validation Metrics
- Loss: 0.18860992789268494
- Rouge1: 84.2283
- Rouge2: 80.2825
- RougeL: 83.9066
- RougeLsum: 83.9129
- Gen Len: 58.3175
## Usage
You can use cURL to access thi... | {"language": "en", "tags": "autotrain", "datasets": ["aalbertini1990/autotrain-data-first-test-html"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 19.49742293318862} | aalbertini1990/autotrain-first-test-html-1136241677 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T11:46:14+00:00 | [] | [
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|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1136241677
- CO2 Emissions (in grams): 19.49742293318862
## Validation Metrics
- Loss: 0.18860992789268494
- Rouge1: 84.2283
- Rouge2: 80.2825
- RougeL: 83.9066
- RougeLsum: 83.9129
- Gen Len: 58.3175
## Usage
You can use cURL to access thi... | [
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"## Usage\n\nYou c... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1136241677\n- CO2 Emission... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-mrpc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glu... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-base-finetuned-mrpc", "results": []}]} | Jinchen/roberta-base-finetuned-mrpc | null | [
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"pytorch",
"optimum_graphcore",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T11:46:23+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-mrpc
===========================
This model is a fine-tuned version of roberta-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2891
* Accuracy: 0.8925
* F1: 0.9228
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: IPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_s... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\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/1544350525558525952/duMy... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/thes_standsfor/1657938820053/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/thes_standsfor | null | [
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"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T12:00:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
DIDN’T DISAPPOINT A PICTURE?
@thes\_standsfor
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
# Postagger Bio Portuguese
## Citation
```
coming soon
```
| {"language": "pt", "datasets": ["MacMorpho"], "widget": [{"text": "O paciente recebeu no hospital e falou com a m\u00e9dica"}]} | lisaterumi/postagger-bio-portuguese | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:MacMorpho",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T12:27:33+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-MacMorpho #autotrain_compatible #endpoints_compatible #region-us
|
# Postagger Bio Portuguese
| [
"# Postagger Bio Portuguese"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-MacMorpho #autotrain_compatible #endpoints_compatible #region-us \n",
"# Postagger Bio Portuguese"
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null | null | Probando texto a ver que tal | {"license": "apache-2.0"} | jcgrour/Prueba_jc | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-07-15T12:36:43+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Probando texto a ver que tal | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-base-dequad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gener... | {"language": "de", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_dequad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Empfangs- und Sendeantenne sollen in ihrer Polarisation \u00fcbereinstimmen, ande... | lmqg/mt5-base-dequad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"de",
"dataset:lmqg/qg_dequad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T12:47:56+00:00 | [
"2210.03992"
] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #de #dataset-lmqg/qg_dequad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-base-dequad-qg'
=======================================
This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg\_dequad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-base
* Language: de
* Training data: lmqg/qg\_dequa... | [
"### Overview\n\n\n* Language model: google/mt5-base\n* Language: de\n* Training data: lmqg/qg\\_dequad (default)\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\n* ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #de #dataset-lmqg/qg_dequad #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: google/mt5-base\n* Language: de\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_large-chunking_0715_v0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta_large-chunking_0715_v0", "results": []}]} | mariolinml/roberta_large-chunking_0715_v0 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T12:57:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta\_large-chunking\_0715\_v0
=================================
This model is a fine-tuned version of roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3602
* Precision: 0.3182
* Recall: 0.2213
* F1: 0.2610
* Accuracy: 0.8681
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_si... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | gazzehamine/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T13:10:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5707
* Wer: 0.3388
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
text-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. -->
# gpt2-large-bne-milunanoches
This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-large-bne](https://huggingface.co/PlanTL-G... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-large-bne-milunanoches", "results": []}]} | RobertoFont/gpt2-large-bne-milunanoches | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T13:48:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-large-bne-milunanoches
===========================
This model is a fine-tuned version of PlanTL-GOB-ES/gpt2-large-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9118
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: 1\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
automatic-speech-recognition | transformers | Finetuned from [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self).
# Installation
1. PyTorch installation: https://pytorch.org/
2. Install transformers: https://huggingface.co/docs/transformers/installation
e.g., installation by conda
```
>> conda create -n wav2vec... | {"language": "en", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["LIUM/tedlium"]} | yongjian/wav2vec2-large-a | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"en",
"dataset:LIUM/tedlium",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-15T14:03:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-LIUM/tedlium #endpoints_compatible #has_space #region-us
| Finetuned from facebook/wav2vec2-large-960h-lv60-self.
# Installation
1. PyTorch installation: URL
2. Install transformers: URL
e.g., installation by conda
# Usage
# Code
GitHub Repo:
URL | [
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text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1544626436899852289/QMNN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/amityexploder/1657898522848/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/amityexploder | null | [
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"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T14:04:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
SOPPY
@amityexploder
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"
] |
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. -->
# opus-mt-en-es-scielo
This model is a fine-tuned version of [domenicrosati/opus-mt-en-es-scielo](https://huggingface.co/domenicro... | {"tags": ["translation", "generated_from_trainer"], "datasets": ["scielo"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-es-scielo", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "scielo", "type": "scielo", "args": "en-es"}, "me... | domenicrosati/opus-mt-en-es-scielo | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:scielo",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T14:25:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-scielo #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-es-scielo
====================
This model is a fine-tuned version of domenicrosati/opus-mt-en-es-scielo on the scielo dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2189
* Bleu: 41.5373
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-scielo #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:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-50-finetuned-opus-en-pt-translation-finetuned-english-to-portuguese-handmade-dataset
This model is a fine-tuned vers... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mbart-large-50-finetuned-opus-en-pt-translation-finetuned-english-to-portuguese-handmade-dataset", "results": []}]} | VanessaSchenkel/mbart-large-50-finetuned-opus-en-pt-translation-finetuned-english-to-portuguese-handmade-dataset | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T14:40:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-50-finetuned-opus-en-pt-translation-finetuned-english-to-portuguese-handmade-dataset
================================================================================================
This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on an unknown dataset.
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-model
This model is a fine-tuned version of [facebook/opt-13b](https://huggingface.co/facebook/opt-13b) on an unknown datase... | {"license": "other", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "opt-model", "results": []}]} | big-kek/NeuroSkeptic | null | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T15:03:23+00:00 | [] | [] | TAGS
#transformers #pytorch #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| opt-model
=========
This model is a fine-tuned version of facebook/opt-13b on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3965
* Accuracy: 0.5020
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* total\\_train\\_batch\\_size: 72\n* total\\_eval\\_batch\\_size: 72\n* ... | [
"TAGS\n#transformers #pytorch #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-ner-hrl-finetuned-ner-full
This model is a fine-tuned version of [Davlan/xlm-roberta-large-ner-hrl](https://hu... | {"tags": ["generated_from_trainer"], "datasets": ["toydata"], "model-index": [{"name": "xlm-roberta-large-ner-hrl-finetuned-ner-full", "results": []}]} | kinanmartin/xlm-roberta-large-ner-hrl-finetuned-ner-full | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:toydata",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T15:40:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-toydata #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-large-ner-hrl-finetuned-ner-full
This model is a fine-tuned version of Davlan/xlm-roberta-large-ner-hrl on the toydata dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training... | [
"# xlm-roberta-large-ner-hrl-finetuned-ner-full\n\nThis model is a fine-tuned version of Davlan/xlm-roberta-large-ner-hrl on the toydata dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informati... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-toydata #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-large-ner-hrl-finetuned-ner-full\n\nThis model is a fine-tuned version of Davlan/xlm-roberta-large-ner-hrl on the toydat... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **3DBall**
This is a trained model of a **ppo** agent playing **3DBall** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete t... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-3DBall"]} | infinitejoy/MLAgents-3DBall | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-3DBall",
"region:us"
] | null | 2022-07-15T16:17:15+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-3DBall #region-us
|
# ppo Agent playing 3DBall
This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
... | [
"# ppo Agent playing 3DBall\n This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the train... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-3DBall #region-us \n",
"# ppo Agent playing 3DBall\n This is a trained model of a ppo agent playing 3DBall using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation... |
unconditional-image-generation | diffusers |
# Latent Diffusion Models (LDM)
**Paper**: [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)
**Abstract**:
*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis res... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | CompVis/ldm-celebahq-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2112.10752",
"license:apache-2.0",
"has_space",
"diffusers:LDMPipeline",
"region:us"
] | null | 2022-07-15T16:28:35+00:00 | [
"2112.10752"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2112.10752 #license-apache-2.0 #has_space #diffusers-LDMPipeline #region-us
|
# Latent Diffusion Models (LDM)
Paper: High-Resolution Image Synthesis with Latent Diffusion Models
Abstract:
*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally,... | [
"# Latent Diffusion Models (LDM)\n\nPaper: High-Resolution Image Synthesis with Latent Diffusion Models\n\nAbstract:\n\n*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Add... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2112.10752 #license-apache-2.0 #has_space #diffusers-LDMPipeline #region-us \n",
"# Latent Diffusion Models (LDM)\n\nPaper: High-Resolution Image Synthesis with Latent Diffusion Models\n\nAbstract:\n\n*By decomposing the image formation process int... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | dspg/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T16:31:56+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1596
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ev... |
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... | ab93/ppo-LunarLanderv2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-15T16:35:30+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-mnli-fer-finetuned
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-mnli-fer-finetuned", "results": []}]} | vish88/roberta-large-mnli-fer-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T16:41:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-mnli-fer-finetuned
================================
This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6940
* Accuracy: 0.5005
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-emo20q
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
#... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-base-finetuned-emo20q", "results": []}]} | abecode/t5-base-finetuned-emo20q | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-15T16:51:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-emo20q
========================
This model is a fine-tuned version of t5-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-to-speech | null | ## GroTTS Model
This model was trained with the [FastSpeech 2](https://arxiv.org/abs/2006.04558) architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from [here](https://huggingface.co/ahnafsamin/parallelwavegan-gronings) and then use the follow... | {"language": "gos", "license": "afl-3.0", "tags": ["text-to-speech", "gronings", "FastSpeech 2"], "datasets": ["gronings"]} | ahnafsamin/FastSpeech2-gronings | null | [
"text-to-speech",
"gronings",
"FastSpeech 2",
"gos",
"dataset:gronings",
"arxiv:2006.04558",
"doi:10.57967/hf/0165",
"license:afl-3.0",
"has_space",
"region:us"
] | null | 2022-07-15T17:21:02+00:00 | [
"2006.04558"
] | [
"gos"
] | TAGS
#text-to-speech #gronings #FastSpeech 2 #gos #dataset-gronings #arxiv-2006.04558 #doi-10.57967/hf/0165 #license-afl-3.0 #has_space #region-us
| ## GroTTS Model
This model was trained with the FastSpeech 2 architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from here and then use the following code:
The GroTTS model is deployed here.
## TTS config
<details><summary>expand</summary>
... | [
"## GroTTS Model \n\nThis model was trained with the FastSpeech 2 architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from here and then use the following code:\n\n\nThe GroTTS model is deployed here.",
"## TTS config\n\n<details><summary>e... | [
"TAGS\n#text-to-speech #gronings #FastSpeech 2 #gos #dataset-gronings #arxiv-2006.04558 #doi-10.57967/hf/0165 #license-afl-3.0 #has_space #region-us \n",
"## GroTTS Model \n\nThis model was trained with the FastSpeech 2 architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to ... |
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. -->
# finetuned-indian-food
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vi... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "widget": [{"src": "https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/003.jpg", "example_title": "fried_rice"}, {"src": "https://huggingface.co/rajistics/finetune... | rajistics/finetuned-indian-food | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-15T17:47:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| finetuned-indian-food
=====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the indian\_food\_images dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2632
* Accuracy: 0.9330
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n... |
automatic-speech-recognition | nemo |
# NVIDIA Citrinet CTC 256 Librispeech (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| ... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.hug... | nvidia/stt_en_citrinet_256_ls | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2104.01721",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-07-15T19:21:11+00:00 | [
"2104.01721"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Citrinet CTC 256 Librispeech (en-US)
===========================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lower c... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get ... |
automatic-speech-recognition | nemo |
# NVIDIA Citrinet CTC 384 Librispeech (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| ... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.hug... | nvidia/stt_en_citrinet_384_ls | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2104.01721",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-07-15T19:54:51+00:00 | [
"2104.01721"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Citrinet CTC 384 Librispeech (en-US)
===========================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lower c... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get ... |
token-classification | transformers |
# POS-Tagger Bio Portuguese
We fine-tuned the BioBERTpt(all) model with the MacMorpho corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.9818.
Metrics:
```
Precision Recall F1 Suport
accuracy 0.98 38320
macro avg 0.95 0.94 0.94... | {"language": "pt", "datasets": ["MacMorpho"], "widget": [{"text": "O paciente recebeu no hospital e falou com a m\u00e9dica"}, {"text": "COMO ESQUEMA DE MEDICA\u00c7\u00c3O PARA ICC PRESCRITO NO ALTA, RECEBE FUROSEMIDA 40 BID, ISOSSORBIDA 40 TID, DIGOXINA 0,25 /D, CAPTOPRIL 50 TID E ESPIRONOLACTONA 25 /D."}, {"text": "... | pucpr-br/postagger-bio-portuguese | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:MacMorpho",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-15T19:58:25+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-MacMorpho #autotrain_compatible #endpoints_compatible #region-us
|
# POS-Tagger Bio Portuguese
We fine-tuned the BioBERTpt(all) model with the MacMorpho corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.9818.
Metrics:
Parameters:
## Acknowledgements
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Supe... | [
"# POS-Tagger Bio Portuguese\n\nWe fine-tuned the BioBERTpt(all) model with the MacMorpho corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.9818.\n\nMetrics:\n\n\n\nParameters:",
"## Acknowledgements\n\nThis study was financed in part by the Coordenação de Aperfeiçoamento de Pesso... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-MacMorpho #autotrain_compatible #endpoints_compatible #region-us \n",
"# POS-Tagger Bio Portuguese\n\nWe fine-tuned the BioBERTpt(all) model with the MacMorpho corpus for the Post-Tagger task, with 10 epochs, achieving a general F1-Score of 0.... |
automatic-speech-recognition | nemo |
# NVIDIA Citrinet CTC 512 Librispeech (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| ... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.hug... | nvidia/stt_en_citrinet_512_ls | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2104.01721",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-07-15T20:04:57+00:00 | [
"2104.01721"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Citrinet CTC 512 Librispeech (en-US)
===========================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in the low... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get ... |
automatic-speech-recognition | nemo |
# NVIDIA Citrinet CTC 768 Librispeech (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| ... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.hug... | nvidia/stt_en_citrinet_768_ls | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2104.01721",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-07-15T20:15:08+00:00 | [
"2104.01721"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Citrinet CTC 768 Librispeech (en-US)
===========================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lower c... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get ... |
automatic-speech-recognition | nemo |
# NVIDIA Citrinet CTC 1924 Librispeech (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.hug... | nvidia/stt_en_citrinet_1024_ls | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2104.01721",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-07-15T20:31:04+00:00 | [
"2104.01721"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Citrinet CTC 1924 Librispeech (en-US)
============================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lower... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2104.01721 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get ... |
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