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text2text-generation | transformers |
# 🚀 Text Punctuator Based on Transformers model T5.
T5 model fine-tuned for punctuation restoration.
Model currently supports only French Language. More language supports will be added later using mT5.
Train Datasets :
Model trained using 2 french datasets (around 500k records):
- [orange_sum](https://huggingface.... | {"language": ["fr"], "license": "apache-2.0", "tags": ["t5", "french", "punctuation"], "datasets": ["orange_sum", "mlsum"]} | ZakaryaRouzki/t5-punctuation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"french",
"punctuation",
"fr",
"dataset:orange_sum",
"dataset:mlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-02T10:22:47+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #french #punctuation #fr #dataset-orange_sum #dataset-mlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Text Punctuator Based on Transformers model T5.
T5 model fine-tuned for punctuation restoration.
Model currently supports only French Language. More language supports will be added later using mT5.
Train Datasets :
Model trained using 2 french datasets (around 500k records):
- orange_sum
- mlsum (only french te... | [
"# Text Punctuator Based on Transformers model T5.\nT5 model fine-tuned for punctuation restoration.\nModel currently supports only French Language. More language supports will be added later using mT5.\n\nTrain Datasets : \nModel trained using 2 french datasets (around 500k records): \n- orange_sum \n- mlsum (onl... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #french #punctuation #fr #dataset-orange_sum #dataset-mlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Text Punctuator Based on Transformers model T5.\nT5 model fine-tuned for punctuation re... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
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-fr", "results": []}]} | Neha2608/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T10:40:50+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-xtreme #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-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: 0.1699
* F1: 0.8725
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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-xtreme #license-mit #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* eval\\_batch\\_size: 24\n* ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-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... | SelamatPagi/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 | 2022-07-02T10:43:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
null | 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-it
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-it", "results": []}]} | Neha2608/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T10:59:49+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-xtreme #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
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: 0.2740
* F1: 0.7919
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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-xtreme #license-mit #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* eval\\_batch\\_size: 24\n* ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-sol
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-sol", "results": []}]} | solve/wav2vec2-base-timit-demo-sol | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T11:12:28+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-sol
============================
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: 0.3922
* Wer: 0.2862
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 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 #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: 64\n* eval\\_b... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
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-en", "results": []}]} | Neha2608/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T11:17:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #generated_from_trainer #dataset-xtreme #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
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: 0.4329
* F1: 0.6431
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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #generated_from_trainer #dataset-xtreme #license-mit #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* eval\\_batch\\... |
image-classification | transformers |
# opencampus_age-detection
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/nat... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | chradden/opencampus_age-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T11:27:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# opencampus_age-detection
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
#### child portrait face
!child portrait face
#### generation x portrait face
!generation x portra... | [
"# opencampus_age-detection\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",
"#### child portrait face\n\n!child portrait face",
"#### generation x portrait fac... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# opencampus_age-detection\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport... |
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": []}]} | Neha2608/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 | 2022-07-02T11:35:28+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: 0.1752
* F1: 0.8557
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*... |
image-classification | transformers |
# rare-bottle
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/huggingp... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | tmoodley/rare-bottle | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T12:21:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-bottle
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
#### Don Julio
!Don Julio
#### Jack Daniels
!Jack Daniels
#### Southern Comfort
!Southern Comfort
#### bacar... | [
"# rare-bottle\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",
"#### Don Julio\n\n!Don Julio",
"#### Jack Daniels\n\n!Jack Daniels",
"#### Southern Comfort\n... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-bottle\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues w... |
null | fastai |
# DeBERTa V3 fine-tuned on TweetEval (sentiment)
A pre-trained DeBERTa V3 fine-tuned on TweetEval (sentiment) achieving 85.56% in accuracy on the validation set.
| {"language": ["en"], "tags": ["fastai"], "datasets": ["tweet_eval"]} | matteopilotto/deberta-v3-base-tweet_eval-emotion | null | [
"fastai",
"en",
"dataset:tweet_eval",
"has_space",
"region:us"
] | null | 2022-07-02T12:37:43+00:00 | [] | [
"en"
] | TAGS
#fastai #en #dataset-tweet_eval #has_space #region-us
|
# DeBERTa V3 fine-tuned on TweetEval (sentiment)
A pre-trained DeBERTa V3 fine-tuned on TweetEval (sentiment) achieving 85.56% in accuracy on the validation set.
| [
"# DeBERTa V3 fine-tuned on TweetEval (sentiment)\nA pre-trained DeBERTa V3 fine-tuned on TweetEval (sentiment) achieving 85.56% in accuracy on the validation set."
] | [
"TAGS\n#fastai #en #dataset-tweet_eval #has_space #region-us \n",
"# DeBERTa V3 fine-tuned on TweetEval (sentiment)\nA pre-trained DeBERTa V3 fine-tuned on TweetEval (sentiment) achieving 85.56% in accuracy on the validation set."
] |
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-350m-economy-data
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an ... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "opt-350m-economy-data", "results": []}]} | Abdelmageed95/opt-350m-economy-data | null | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-02T12:55:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# opt-350m-economy-data
This model is a fine-tuned version of facebook/opt-350m on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2910
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mor... | [
"# opt-350m-economy-data\n\nThis model is a fine-tuned version of facebook/opt-350m on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.2910",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training an... | [
"TAGS\n#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-350m-economy-data\n\nThis model is a fine-tuned version of facebook/opt-350m on an unknown dataset.\nIt achieves the... |
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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | kidzy/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T13:07:34+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2653
* Accuracy: 0.9471
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1076038122
- CO2 Emissions (in grams): 6.1987408118248375
## Validation Metrics
- Loss: 0.5054866671562195
- Rouge1: 76.4469
- Rouge2: 72.6874
- RougeL: 76.3128
- RougeLsum: 76.2952
- Gen Len: 19.3856
## Usage
You can use cURL to access thi... | {"language": "en", "tags": "autotrain", "datasets": ["scaccomatto/autotrain-data-dataset-en-5-mini-1-50-truncate"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 6.1987408118248375} | scaccomatto/autotrain-dataset-en-5-mini-1-50-truncate-1076038122 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"en",
"dataset:scaccomatto/autotrain-data-dataset-en-5-mini-1-50-truncate",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T13:55:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-dataset-en-5-mini-1-50-truncate #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1076038122
- CO2 Emissions (in grams): 6.1987408118248375
## Validation Metrics
- Loss: 0.5054866671562195
- Rouge1: 76.4469
- Rouge2: 72.6874
- RougeL: 76.3128
- RougeLsum: 76.2952
- Gen Len: 19.3856
## Usage
You can use cURL to access thi... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1076038122\n- CO2 Emissions (in grams): 6.1987408118248375",
"## Validation Metrics\n\n- Loss: 0.5054866671562195\n- Rouge1: 76.4469\n- Rouge2: 72.6874\n- RougeL: 76.3128\n- RougeLsum: 76.2952\n- Gen Len: 19.3856",
"## Usage\n\nYou c... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1076038122\n- CO... |
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... | Jimchoo91/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 | 2022-07-02T14:03:05+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: 0.2251
* Accuracy: 0.923
* F1: 0.9232
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 #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... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1076338146
- CO2 Emissions (in grams): 5.239170170576799
## Validation Metrics
- Loss: 0.6177766919136047
- Rouge1: 76.4034
- Rouge2: 72.6118
- RougeL: 76.233
- RougeLsum: 76.2601
- Gen Len: 18.6275
## Usage
You can use cURL to access this ... | {"language": "en", "tags": "autotrain", "datasets": ["scaccomatto/autotrain-data-dataset-en-5-mini-1-50-num"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.239170170576799} | scaccomatto/autotrain-dataset-en-5-mini-1-50-num-1076338146 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"en",
"dataset:scaccomatto/autotrain-data-dataset-en-5-mini-1-50-num",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T14:10:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-dataset-en-5-mini-1-50-num #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1076338146
- CO2 Emissions (in grams): 5.239170170576799
## Validation Metrics
- Loss: 0.6177766919136047
- Rouge1: 76.4034
- Rouge2: 72.6118
- RougeL: 76.233
- RougeLsum: 76.2601
- Gen Len: 18.6275
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1076338146\n- CO2 Emissions (in grams): 5.239170170576799",
"## Validation Metrics\n\n- Loss: 0.6177766919136047\n- Rouge1: 76.4034\n- Rouge2: 72.6118\n- RougeL: 76.233\n- RougeLsum: 76.2601\n- Gen Len: 18.6275",
"## Usage\n\nYou can... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-dataset-en-5-mini-1-50-num #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1076338146\n- CO2 Emi... |
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... | jdang/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 | 2022-07-02T14:27:33+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: 0.1358
* F1: 0.8638
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\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | jdang/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T15:10:21+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1608
* F1: 0.8593
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
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-news
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-news", "results": []}]} | Eleven/distilbert-base-uncased-finetuned-news | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T15:19:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-news
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1667
* Accuracy: 0.9447
* F1: 0.9448
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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... | sofiaoliveira/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-02T16:22:59+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-persian-50p
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-persian-50p", "results": []}]} | zoha/wav2vec2-xlsr-persian-50p | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T16:36:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-persian-50p
=========================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6846
* Wer: 0.4339
Model description
-----------------
More information needed
Intended uses & limit... | [
"### 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* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"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... |
null | null | # Finetuned on 4 seasons of funfic rugpt3 large model
| {} | AlexWortega/vsratiy_hogwarts | null | [
"region:us"
] | null | 2022-07-02T16:50:03+00:00 | [] | [] | TAGS
#region-us
| # Finetuned on 4 seasons of funfic rugpt3 large model
| [
"# Finetuned on 4 seasons of funfic rugpt3 large model"
] | [
"TAGS\n#region-us \n",
"# Finetuned on 4 seasons of funfic rugpt3 large model"
] |
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="ryanblak/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | ryanblak/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-02T17:16:01+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ryanblak/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | ryanblak/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-02T17:18:37+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | transformers | # ru-gpt-dy
**This is the first model I fine-tuned.**
It is GPT-NEO fine-tuned on around 36,000 of my tweets. It’s a generation model. Input -> output. It’s just okay, but it’s mine. :-)
*Compute for fine-tune by RunPod.io*
***Made with love in Brownsville, Texas*** | {"language": ["en"], "license": "gpl", "tags": ["text", "nlp", "generation", "beginner"], "thumbnail": "url to a thumbnail used in social sharing"} | southmost/ru-gpt-dy | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text",
"nlp",
"generation",
"beginner",
"en",
"license:gpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T17:43:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #text #nlp #generation #beginner #en #license-gpl #autotrain_compatible #endpoints_compatible #region-us
| # ru-gpt-dy
This is the first model I fine-tuned.
It is GPT-NEO fine-tuned on around 36,000 of my tweets. It’s a generation model. Input -> output. It’s just okay, but it’s mine. :-)
*Compute for fine-tune by URL*
*Made with love in Brownsville, Texas* | [
"# ru-gpt-dy\n\nThis is the first model I fine-tuned. \nIt is GPT-NEO fine-tuned on around 36,000 of my tweets. It’s a generation model. Input -> output. It’s just okay, but it’s mine. :-)\n\n*Compute for fine-tune by URL*\n\n*Made with love in Brownsville, Texas*"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #text #nlp #generation #beginner #en #license-gpl #autotrain_compatible #endpoints_compatible #region-us \n",
"# ru-gpt-dy\n\nThis is the first model I fine-tuned. \nIt is GPT-NEO fine-tuned on around 36,000 of my tweets. It’s a generation model. Input -> ou... |
token-classification | transformers | # tner/bert-base-tweetner7-2020
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-paramete... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/bert-base-tweetner7-2020 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T17:56:46+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/bert-base-tweetner7-2020
This model is a fine-tuned version of bert-base-cased on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.600898... | [
"# tner/bert-base-tweetner7-2020\n\nThis model is a fine-tuned version of bert-base-cased on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (micro... | [
"TAGS\n#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/bert-base-tweetner7-2020\n\nThis model is a fine-tuned version of bert-base-cased on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tun... |
token-classification | transformers | # tner/roberta-large-tweetner7-2021
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-paramete... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-2021 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T17:57:38+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-2021
This model is a fine-tuned version of roberta-large on the
tner/tweetner7 dataset ('train_2021' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.6404... | [
"# tner/roberta-large-tweetner7-2021\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (mic... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-2021\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fin... |
token-classification | transformers | # tner/bert-large-tweetner7-2020
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-param... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/bert-large-tweetner7-2020 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T17:58:57+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/bert-large-tweetner7-2020
This model is a fine-tuned version of bert-large-cased on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.6142... | [
"# tner/bert-large-tweetner7-2020\n\nThis model is a fine-tuned version of bert-large-cased on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (mic... | [
"TAGS\n#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/bert-large-tweetner7-2020\n\nThis model is a fine-tuned version of bert-large-cased on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-t... |
token-classification | transformers | # tner/bertweet-base-tweetner7-2020
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hy... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/bertweet-base-tweetner7-2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:02:29+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/bertweet-base-tweetner7-2020
This model is a fine-tuned version of vinai/bertweet-base on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): ... | [
"# tner/bertweet-base-tweetner7-2020\n\nThis model is a fine-tuned version of vinai/bertweet-base on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/bertweet-base-tweetner7-2020\n\nThis model is a fine-tuned version of vinai/bertweet-base on the \ntner/tweetner7 dataset ('train_2020' split).\nMod... |
token-classification | transformers | # tner/bertweet-large-tweetner7-2020
This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/bertweet-large-tweetner7-2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:04:55+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/bertweet-large-tweetner7-2020
This model is a fine-tuned version of vinai/bertweet-large on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro)... | [
"# tner/bertweet-large-tweetner7-2020\n\nThis model is a fine-tuned version of vinai/bertweet-large on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n-... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/bertweet-large-tweetner7-2020\n\nThis model is a fine-tuned version of vinai/bertweet-large on the \ntner/tweetner7 dataset ('train_2020' split).\nM... |
token-classification | transformers | # tner/roberta-large-tweetner7-all
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-all | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:08:51+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-all
This model is a fine-tuned version of roberta-large on the
tner/tweetner7 dataset ('train_all' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.657455... | [
"# tner/roberta-large-tweetner7-all\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train_all' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (micro... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-all\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train_all' split).\nModel fine-... |
token-classification | transformers | # tner/roberta-base-tweetner7-2020
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter s... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-base-tweetner7-2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:09:10+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-base-tweetner7-2020
This model is a fine-tuned version of roberta-base on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.642118... | [
"# tner/roberta-base-tweetner7-2020\n\nThis model is a fine-tuned version of roberta-base on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (micro... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-base-tweetner7-2020\n\nThis model is a fine-tuned version of roberta-base on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-... |
token-classification | transformers | # tner/roberta-large-tweetner7-selflabel2020
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2020` split of ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-selflabel2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:11:21+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-selflabel2020
This model is a fine-tuned version of roberta-large on the
tner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2020' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for more detail of r... | [
"# tner/roberta-large-tweetner7-selflabel2020\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2020' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for more det... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-selflabel2020\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split). This m... |
token-classification | transformers | # tner/roberta-large-tweetner7-2020
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-paramete... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:11:45+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-2020
This model is a fine-tuned version of roberta-large on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.6476... | [
"# tner/roberta-large-tweetner7-2020\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (mic... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-2020\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fin... |
token-classification | transformers | # tner/roberta-large-tweetner7-selflabel2021
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2021` split of ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-selflabel2021 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:12:11+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-selflabel2021
This model is a fine-tuned version of roberta-large on the
tner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2021' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for more detail of r... | [
"# tner/roberta-large-tweetner7-selflabel2021\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2021' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for more det... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-selflabel2021\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split). This m... |
token-classification | transformers | # tner/roberta-large-tweetner7-continuous
This model is a fine-tuned version of [tner/roberta-large-tweetner-2020](https://huggingface.co/tner/roberta-large-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). The model is first fine-tuned on `train_2020... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-continuous | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:12:30+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-continuous
This model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the
tner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'.
Model fine-tuning is done via T-NER's hyper-parameter s... | [
"# tner/roberta-large-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the \ntner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'. \nModel fine-tuning is done via T-NER's hyper-pa... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the \ntner/tweetner7 dataset ('train... |
token-classification | transformers | # tner/roberta-large-tweetner7-2020-selflabel2020-all
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2020` ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-2020-selflabel2020-all | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:16:44+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-2020-selflabel2020-all
This model is a fine-tuned version of roberta-large on the
tner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2020' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for more de... | [
"# tner/roberta-large-tweetner7-2020-selflabel2020-all\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2020' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-2020-selflabel2020-all\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split... |
token-classification | transformers | # tner/roberta-large-tweetner7-2020-selflabel2021-all
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2021` ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-2020-selflabel2021-all | null | [
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"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:17:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-2020-selflabel2021-all
This model is a fine-tuned version of roberta-large on the
tner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2021' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for more de... | [
"# tner/roberta-large-tweetner7-2020-selflabel2021-all\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2021' split of the tner/tweetner7 annotated by tner/roberta-large). Please check URL for... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-2020-selflabel2021-all\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweetner7 dataset ('train' split... |
token-classification | transformers | # tner/roberta-large-tweetner7-selflabel2020-continuous
This model is a fine-tuned version of [tner/roberta-large-tweetner-2020](https://huggingface.co/tner/roberta-large-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-la... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-selflabel2020-continuous | null | [
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"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:21:08+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-selflabel2020-continuous
This model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the
tner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2020' split of the tner/tweetner7 annotated by tner/roberta-large). Please ... | [
"# tner/roberta-large-tweetner7-selflabel2020-continuous\n\nThis model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the \ntner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2020' split of the tner/tweetner7 annotated by tner/roberta-large).... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-selflabel2020-continuous\n\nThis model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the \ntner/tweetner7 d... |
token-classification | transformers | # tner/roberta-large-tweetner7-selflabel2021-continuous
This model is a fine-tuned version of [tner/roberta-large-tweetner-2020](https://huggingface.co/tner/roberta-large-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-la... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-large-tweetner7-selflabel2021-continuous | null | [
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"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T18:21:30+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweetner7-selflabel2021-continuous
This model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the
tner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2021' split of the tner/tweetner7 annotated by tner/roberta-large). Please ... | [
"# tner/roberta-large-tweetner7-selflabel2021-continuous\n\nThis model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the \ntner/tweetner7 dataset ('train' split). This model is fine-tuned on self-labeled dataset which is the 'extra_2021' split of the tner/tweetner7 annotated by tner/roberta-large).... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweetner7-selflabel2021-continuous\n\nThis model is a fine-tuned version of tner/roberta-large-tweetner-2020 on the \ntner/tweetner7 d... |
text-generation | null |
# Test chatbot | {"tags": ["conversational"]} | JamesonSpiff/chatBot_test_model | null | [
"conversational",
"region:us"
] | null | 2022-07-02T19:48:24+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Test chatbot | [
"# Test chatbot"
] | [
"TAGS\n#conversational #region-us \n",
"# Test chatbot"
] |
fill-mask | transformers |
**Paper:** For more details, please refer to our paper: [BERTabaporu: Assessing a Genre-Specific Language Model for Portuguese NLP](https://aclanthology.org/2023.ranlp-1.24/)
## Introduction
BERTabaporu is a Brazilian Portuguese BERT model in the Twitter domain. The model has been built from a collection of 238 mi... | {"language": "pt", "license": "mit", "tags": ["bert", "pytorch"], "datasets": ["Twitter"]} | pablocosta/bertabaporu-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"pt",
"dataset:Twitter",
"doi:10.57967/hf/0019",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T20:59:20+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #fill-mask #pt #dataset-Twitter #doi-10.57967/hf/0019 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Paper: For more details, please refer to our paper: BERTabaporu: Assessing a Genre-Specific Language Model for Portuguese NLP
Introduction
------------
BERTabaporu is a Brazilian Portuguese BERT model in the Twitter domain. The model has been built from a collection of 238 million tweets written by over 100 thousan... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #pt #dataset-Twitter #doi-10.57967/hf/0019 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
## Introduction
BERTabaporu is a Brazilian Portuguese BERT model in the Twitter domain. The model has been built from a collection of 238 million tweets written by over 100 thousand unique Twitter users, and conveying over 2.9 billion tokens in total.
## Available models
| Model ... | {"language": "pt", "license": "mit", "tags": ["bert", "pytorch"], "datasets": ["Twitter"]} | pablocosta/bertabaporu-large-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"pt",
"dataset:Twitter",
"doi:10.57967/hf/0020",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T22:21:21+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #fill-mask #pt #dataset-Twitter #doi-10.57967/hf/0020 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Introduction
------------
BERTabaporu is a Brazilian Portuguese BERT model in the Twitter domain. The model has been built from a collection of 238 million tweets written by over 100 thousand unique Twitter users, and conveying over 2.9 billion tokens in total.
Available models
----------------
Usage
-----
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #pt #dataset-Twitter #doi-10.57967/hf/0020 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
This is a cross-encoder model trained to predict semantic equivalence of two Russian sentences.
It classifies text pairs as paraphrases (class 1) or non-paraphrases (class 0). Its scores can be used as a metric of content preservation for paraphrasing or text style transfer.
It is a [sberbank-ai/ruRoberta-large](h... | {"language": ["ru"], "tags": ["sentence-similarity", "text-classification"], "datasets": ["merionum/ru_paraphraser", "RuPAWS"]} | s-nlp/ruRoberta-large-paraphrase-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"sentence-similarity",
"ru",
"dataset:merionum/ru_paraphraser",
"dataset:RuPAWS",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-02T22:23:03+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #sentence-similarity #ru #dataset-merionum/ru_paraphraser #dataset-RuPAWS #autotrain_compatible #endpoints_compatible #region-us
| This is a cross-encoder model trained to predict semantic equivalence of two Russian sentences.
It classifies text pairs as paraphrases (class 1) or non-paraphrases (class 0). Its scores can be used as a metric of content preservation for paraphrasing or text style transfer.
It is a sberbank-ai/ruRoberta-large mode... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #sentence-similarity #ru #dataset-merionum/ru_paraphraser #dataset-RuPAWS #autotrain_compatible #endpoints_compatible #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="choonlee/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | choonlee/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-03T01:26:09+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **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": "reinforcement", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": ... | workRL/reinforcement | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-03T02:07:50+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... |
image-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. -->
# YKXBCi/resnet-50-ucSat
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "YKXBCi/resnet-50-ucSat", "results": []}]} | YKXBCi/resnet-50-ucSat | null | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T02:23:43+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| YKXBCi/resnet-50-ucSat
======================
This model is a fine-tuned version of microsoft/resnet-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9091
* Train Accuracy: 0.7125
* Train Top-3-accuracy: 0.9227
* Validation Loss: 1.0869
* Validation Accuracy: 0.6562... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'clas... |
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/InformalToFormalLincoln54 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T02:33:19+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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": [{... | devetle/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-03T02:55:09+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... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | workRL/Reinforce-Pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-03T03:28:38+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
image-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. -->
# YKXBCi/resnet-50-euroSat
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "YKXBCi/resnet-50-euroSat", "results": []}]} | YKXBCi/resnet-50-euroSat | null | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T04:19:47+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| YKXBCi/resnet-50-euroSat
========================
This model is a fine-tuned version of microsoft/resnet-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1408
* Train Accuracy: 0.9540
* Train Top-3-accuracy: 0.9973
* Validation Loss: 0.2008
* Validation Accuracy: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'clas... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | devetle/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-03T04:30:15+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
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 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | WasuratS/ppo-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-03T05:03:24+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
token-classification | transformers |
# (NER) distilbert-base-uncased : conll2012_ontonotesv5-english-v4
This **distilbert-base-uncased** NER model was finetuned on **conll2012_ontonotesv5-english-v4** dataset. <br>
Check out [NER-System Repository](https://github.com/djagatiya/NER-System) for more information.
## Evaluation
- Precision: 84.60
- Recall:... | {"tags": ["token-classification"], "datasets": ["djagatiya/ner-ontonotes-v5-eng-v4"]} | djagatiya/ner-distilbert-base-uncased-ontonotesv5-englishv4 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"dataset:djagatiya/ner-ontonotes-v5-eng-v4",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T06:17:50+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us
|
# (NER) distilbert-base-uncased : conll2012_ontonotesv5-english-v4
This distilbert-base-uncased NER model was finetuned on conll2012_ontonotesv5-english-v4 dataset. <br>
Check out NER-System Repository for more information.
## Evaluation
- Precision: 84.60
- Recall: 86.47
- F1-Score: 85.53
> check out this URL file... | [
"# (NER) distilbert-base-uncased : conll2012_ontonotesv5-english-v4\n\nThis distilbert-base-uncased NER model was finetuned on conll2012_ontonotesv5-english-v4 dataset. <br>\nCheck out NER-System Repository for more information.",
"## Evaluation\n- Precision: 84.60\n- Recall: 86.47\n- F1-Score: 85.53\n\n> check o... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us \n",
"# (NER) distilbert-base-uncased : conll2012_ontonotesv5-english-v4\n\nThis distilbert-base-uncased NER model was finetuned on conll2012_ontonotesv... |
token-classification | transformers |
# (NER) ALBERT-base-v2 : conll2012_ontonotesv5-english-v4
This `ALBERT-base-v2` NER model was finetuned on `conll2012_ontonotesv5` version `english-v4` dataset. <br>
Check out [NER-System Repository](https://github.com/djagatiya/NER-System) for more information.
## Evaluation
- Precision: 86.20
- Recall: 86.18
- F1-... | {"tags": ["token-classification"], "datasets": ["djagatiya/ner-ontonotes-v5-eng-v4"], "widget": [{"text": "On September 1st George won 1 dollar while watching Game of Thrones."}]} | djagatiya/ner-albert-base-v2-ontonotesv5-englishv4 | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"dataset:djagatiya/ner-ontonotes-v5-eng-v4",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T06:25:25+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us
|
# (NER) ALBERT-base-v2 : conll2012_ontonotesv5-english-v4
This 'ALBERT-base-v2' NER model was finetuned on 'conll2012_ontonotesv5' version 'english-v4' dataset. <br>
Check out NER-System Repository for more information.
## Evaluation
- Precision: 86.20
- Recall: 86.18
- F1-Score: 86.19
> check out this URL file for... | [
"# (NER) ALBERT-base-v2 : conll2012_ontonotesv5-english-v4\n\nThis 'ALBERT-base-v2' NER model was finetuned on 'conll2012_ontonotesv5' version 'english-v4' dataset. <br>\nCheck out NER-System Repository for more information.",
"## Evaluation\n- Precision: 86.20\n- Recall: 86.18\n- F1-Score: 86.19\n\n> check out t... | [
"TAGS\n#transformers #pytorch #albert #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us \n",
"# (NER) ALBERT-base-v2 : conll2012_ontonotesv5-english-v4\n\nThis 'ALBERT-base-v2' NER model was finetuned on 'conll2012_ontonotesv5' version 'english... |
token-classification | transformers |
# (NER) bert-base-cased : conll2012_ontonotesv5-english-v4
This `bert-base-cased` NER model was finetuned on `conll2012_ontonotesv5` version `english-v4` dataset. <br>
Check out [NER-System Repository](https://github.com/djagatiya/NER-System) for more information.
## Evaluation
- Precision: 87.85
- Recall: 89.63
- F... | {"tags": ["token-classification"], "datasets": ["djagatiya/ner-ontonotes-v5-eng-v4"], "task_ids": ["named-entity-recognition"], "widget": [{"text": "On September 1st George won 1 dollar while watching Game of Thrones."}]} | djagatiya/ner-bert-base-cased-ontonotesv5-englishv4 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:djagatiya/ner-ontonotes-v5-eng-v4",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T06:26:18+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us
|
# (NER) bert-base-cased : conll2012_ontonotesv5-english-v4
This 'bert-base-cased' NER model was finetuned on 'conll2012_ontonotesv5' version 'english-v4' dataset. <br>
Check out NER-System Repository for more information.
## Evaluation
- Precision: 87.85
- Recall: 89.63
- F1-Score: 88.73
> check out this URL file f... | [
"# (NER) bert-base-cased : conll2012_ontonotesv5-english-v4\n\nThis 'bert-base-cased' NER model was finetuned on 'conll2012_ontonotesv5' version 'english-v4' dataset. <br>\nCheck out NER-System Repository for more information.",
"## Evaluation\n- Precision: 87.85\n- Recall: 89.63\n- F1-Score: 88.73\n\n> check out... | [
"TAGS\n#transformers #pytorch #bert #token-classification #dataset-djagatiya/ner-ontonotes-v5-eng-v4 #autotrain_compatible #endpoints_compatible #region-us \n",
"# (NER) bert-base-cased : conll2012_ontonotesv5-english-v4\n\nThis 'bert-base-cased' NER model was finetuned on 'conll2012_ontonotesv5' version 'english... |
text-classification | transformers |
# beto-emoji
Fine-tunning [BETO](https://github.com/dccuchile/beto) for emoji-prediction.
## Repository
Details with training and a use example are shown in [github.com/camilocarvajalreyes/beto-emoji](https://github.com/camilocarvajalreyes/beto-emoji). A deeper analysis of this and other models on the full dataset ca... | {"language": ["es"]} | ccarvajal/beto-emoji | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T06:26:55+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #autotrain_compatible #endpoints_compatible #region-us
|
# beto-emoji
Fine-tunning BETO for emoji-prediction.
## Repository
Details with training and a use example are shown in URL A deeper analysis of this and other models on the full dataset can be found in URL We have used this model for a project for CC5205 Data Mining course.
## Example
Inspired by model card from ca... | [
"# beto-emoji\nFine-tunning BETO for emoji-prediction.",
"## Repository\nDetails with training and a use example are shown in URL A deeper analysis of this and other models on the full dataset can be found in URL We have used this model for a project for CC5205 Data Mining course.",
"## Example\nInspired by mod... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #autotrain_compatible #endpoints_compatible #region-us \n",
"# beto-emoji\nFine-tunning BETO for emoji-prediction.",
"## Repository\nDetails with training and a use example are shown in URL A deeper analysis of this and other models on the full datase... |
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-austen
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "t5-austen", "results": []}]} | Gorilla115/t5-austen | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T06:30:20+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-austen
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters... | [
"# t5-austen\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-austen\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended us... |
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)
```python
import gym
from huggingface_sb3 import load_from_hub
from stable_baselines... | {"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... | coledie/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-03T06:39:12+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)
| [
"# 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)"
] | [
"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)"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | minsoo9574/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-03T06:51:45+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.4,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Auth... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | chali12/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-07-03T06:55:19+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (1 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] |
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. -->
# Hubert-base-superb
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "model-index": [{"name": "Hubert-base-superb", "results": []}]} | Elliotte/Hubert-base-superb | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T07:32:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| Hubert-base-superb
==================
This model is a fine-tuned version of ntu-spml/distilhubert on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6712
* Wer: 0.4781
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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 #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-superb #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: 0.001\n* train\\_ba... |
token-classification | transformers | # tner/twitter-roberta-base-dec2021-tweetner7-2020
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
Model fine-tuning is ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2021-tweetner7-2020 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T08:07:32+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2021-tweetner7-2020
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the
tner/tweetner7 dataset ('train_2020' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on t... | [
"# tner/twitter-roberta-base-dec2021-tweetner7-2020\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the \ntner/tweetner7 dataset ('train_2020' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following re... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2021-tweetner7-2020\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the \ntner/tweetner7... |
token-classification | transformers | # tner/twitter-roberta-base-dec2021-tweetner7-2021
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).
Model fine-tuning is ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2021-tweetner7-2021 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T08:22:26+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2021-tweetner7-2021
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the
tner/tweetner7 dataset ('train_2021' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on t... | [
"# tner/twitter-roberta-base-dec2021-tweetner7-2021\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following re... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2021-tweetner7-2021\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the \ntner/tweetner7... |
token-classification | transformers | # tner/twitter-roberta-base-dec2021-tweetner7-all
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is do... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2021-tweetner7-all | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T08:24:32+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2021-tweetner7-all
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the
tner/tweetner7 dataset ('train_all' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the... | [
"# tner/twitter-roberta-base-dec2021-tweetner7-all\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the \ntner/tweetner7 dataset ('train_all' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following resu... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2021-tweetner7-all\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the \ntner/tweetner7 ... |
token-classification | transformers | # tner/twitter-roberta-base-dec2021-tweetner7-continuous
This model is a fine-tuned version of [tner/twitter-roberta-base-dec2021-tweetner-2020](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2021-tweetner7-continuous | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T08:26:30+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2021-tweetner7-continuous
This model is a fine-tuned version of tner/twitter-roberta-base-dec2021-tweetner-2020 on the
tner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'.
Model fine-tuning is done... | [
"# tner/twitter-roberta-base-dec2021-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/twitter-roberta-base-dec2021-tweetner-2020 on the \ntner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'. \nModel fine-tunin... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2021-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/twitter-roberta-base-dec2021-tweetner-2020 on the \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="angelinux/q-FrozenLake-v1-4x4-Slippery-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "... | angelinux/q-FrozenLake-v1-4x4-Slippery-v1 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-03T08:44:13+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | transformers | # tner/roberta-base-tweetner7-2021
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter s... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-base-tweetner7-2021 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T09:10:43+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-base-tweetner7-2021
This model is a fine-tuned version of roberta-base on the
tner/tweetner7 dataset ('train_2021' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.617555... | [
"# tner/roberta-base-tweetner7-2021\n\nThis model is a fine-tuned version of roberta-base on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (micro... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-base-tweetner7-2021\n\nThis model is a fine-tuned version of roberta-base on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-... |
token-classification | transformers | # tner/roberta-base-tweetner7-continuous
This model is a fine-tuned version of [tner/roberta-base-tweetner-2020](https://huggingface.co/tner/roberta-base-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). The model is first fine-tuned on `train_2020`, ... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/roberta-base-tweetner7-continuous | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T09:14:00+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-base-tweetner7-continuous
This model is a fine-tuned version of tner/roberta-base-tweetner-2020 on the
tner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'.
Model fine-tuning is done via T-NER's hyper-parameter sea... | [
"# tner/roberta-base-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/roberta-base-tweetner-2020 on the \ntner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'. \nModel fine-tuning is done via T-NER's hyper-para... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-base-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/roberta-base-tweetner-2020 on the \ntner/tweetner7 dataset ('train_2... |
automatic-speech-recognition | transformers |
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model has apostrophes and hyphens.
The language model is 3-gram.
Attribution to the dataset of the language model:
- Chaplynskyi, D. et al. (2... | {"language": ["uk"], "license": "cc-by-nc-sa-4.0", "datasets": ["mozilla-foundation/common_voice_10_0"]} | Yehor/wav2vec2-xls-r-300m-uk-with-news-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"uk",
"dataset:mozilla-foundation/common_voice_10_0",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T09:20:24+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
This model has apostrophes and hyphens.
The language model is 3-gram.
Attribution to the dataset of the language model:
* Chaplynskyi, D. et al. (2021) lang-uk Ukrainian Ubercorpus [Data ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# C3PO DialoGPT Small | {"tags": ["conversational"]} | Akito1961/DialoGPT-small-C3PO | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T09:21:59+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# C3PO DialoGPT Small | [
"# C3PO DialoGPT Small"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# C3PO DialoGPT Small"
] |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | Neha2608/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T09:25:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4859
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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\\... |
text-classification | transformers |
This is a [ruBERT-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on the mixture of 3 paraphrase detection datasets:
- [ru_paraphraser](https://huggingface.co/merionum/ru_paraphraser) (with classes -1 and 0 merged)
- [RuPAWS](https://github.com/ivkrotova/rupaws_dataset... | {"language": ["ru"], "tags": ["sentence-similarity", "text-classification", "paraphrase-detection"], "datasets": ["merionum/ru_paraphraser"]} | s-nlp/rubert-base-cased-conversational-paraphrase-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sentence-similarity",
"paraphrase-detection",
"ru",
"dataset:merionum/ru_paraphraser",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T09:49:18+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #paraphrase-detection #ru #dataset-merionum/ru_paraphraser #autotrain_compatible #endpoints_compatible #region-us
|
This is a ruBERT-conversational model trained on the mixture of 3 paraphrase detection datasets:
- ru_paraphraser (with classes -1 and 0 merged)
- RuPAWS
- A dataset containing crowdsourced evaluation of content preservation in Russian text detoxification by Dementieva et al, 2022.
The model can be used to assess sem... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #paraphrase-detection #ru #dataset-merionum/ru_paraphraser #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | haddadalwi/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T10:32:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 5.5273
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** 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 tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | osanseviero/worms | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-07-03T10:49:31+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm 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 Worm\n This is a trained model of a ppo agent playing Worm 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 training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
question-answering | transformers | # GELECTRA-base-LegalQuAD
## Overview
**Language model:** GELECTRA-base
**Language:** German
**Downstream-task:** Extractive QA
**Training data:** German-legal-SQuAD
**Eval data:** German-legal-SQuAD testset
## Hyperparameters
```
batch_size = 10
n_epochs = 2
max_seq_len=256,
learning_rate=1e-5,
## Eval results
E... | {"language": ["de"], "tags": ["qa"], "widget": [{"text": "", "context": "", "example_title": "Extractive QA"}]} | Christoph911/GELECTRA-base-LegalQuAD | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"qa",
"de",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T11:08:32+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #electra #question-answering #qa #de #endpoints_compatible #region-us
| # GELECTRA-base-LegalQuAD
## Overview
Language model: GELECTRA-base
Language: German
Downstream-task: Extractive QA
Training data: German-legal-SQuAD
Eval data: German-legal-SQuAD testset
## Hyperparameters
'''
batch_size = 10
n_epochs = 2
max_seq_len=256,
learning_rate=1e-5,
## Eval results
Evaluated on German-l... | [
"# GELECTRA-base-LegalQuAD",
"## Overview\nLanguage model: GELECTRA-base\nLanguage: German\nDownstream-task: Extractive QA \nTraining data: German-legal-SQuAD \nEval data: German-legal-SQuAD testset",
"## Hyperparameters\n'''\nbatch_size = 10\nn_epochs = 2\nmax_seq_len=256,\nlearning_rate=1e-5,",
"## Eval ... | [
"TAGS\n#transformers #pytorch #electra #question-answering #qa #de #endpoints_compatible #region-us \n",
"# GELECTRA-base-LegalQuAD",
"## Overview\nLanguage model: GELECTRA-base\nLanguage: German\nDownstream-task: Extractive QA \nTraining data: German-legal-SQuAD \nEval data: German-legal-SQuAD testset",
"... |
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. -->
# tmp
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_Gen... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "tmp", "results": []}]} | shubhamitra/tmp | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T11:44:07+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tmp
===
This model is a fine-tuned version of huawei-noah/TinyBERT\_General\_4L\_312D on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 123\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size:... |
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. -->
# TinyBERT_General_4L_312D-finetuned-toxic-classification
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_3... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "TinyBERT_General_4L_312D-finetuned-toxic-classification", "results": []}]} | shubhamitra/TinyBERT_General_4L_312D-finetuned-toxic-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T12:23:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| TinyBERT\_General\_4L\_312D-finetuned-toxic-classification
==========================================================
This model is a fine-tuned version of huawei-noah/TinyBERT\_General\_4L\_312D on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 123\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_... |
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. -->
# TestZee/t5-small-finetuned-xum-test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TestZee/t5-small-finetuned-xum-test", "results": []}]} | TestZee/t5-small-finetuned-xum-test | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T12:35:45+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TestZee/t5-small-finetuned-xum-test
===================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.9733
* Validation Loss: 2.6463
* Epoch: 0
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-cartpole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | epsil/Reinforce-cartpole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-03T14:07:48+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 .
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 ."
] | [
"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 ."
] |
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="Kinahem/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Kinahem/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-03T14:13:15+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Kinahem/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/... | Kinahem/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-03T14:24:13+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | epsil/Reinforce-pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-03T14:29:20+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 ."
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 ."
] |
null | null | ---git lfs install
git clone https://huggingface.co/Rickster/Fish
license: other
---
| {} | Rickster/Fish | null | [
"region:us"
] | null | 2022-07-03T14:48:59+00:00 | [] | [] | TAGS
#region-us
| ---git lfs install
git clone URL
license: other
---
| [] | [
"TAGS\n#region-us \n"
] |
summarization | transformers |
#### Pre-trained BART Model fine-tune on WikiLingua dataset
The repository for the fine-tuned BART model (by sshleifer) using the **wiki_lingua** dataset (English)
**Purpose:** Examine the performance of a fine-tuned model research purposes
**Observation:**
- Pre-trained model was trained on the XSum dataset, which ... | {"language": ["en"], "license": "mit", "tags": ["summarization"], "datasets": ["wiki_lingua"], "metrics": ["rouge"]} | datien228/distilbart-ftn-wiki_lingua | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:wiki_lingua",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T15:21:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
#### Pre-trained BART Model fine-tune on WikiLingua dataset
The repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua dataset (English)
Purpose: Examine the performance of a fine-tuned model research purposes
Observation:
- Pre-trained model was trained on the XSum dataset, which summarize a ... | [
"#### Pre-trained BART Model fine-tune on WikiLingua dataset\nThe repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua dataset (English)\n\nPurpose: Examine the performance of a fine-tuned model research purposes\n\nObservation:\n- Pre-trained model was trained on the XSum dataset, which su... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Pre-trained BART Model fine-tune on WikiLingua dataset\nThe repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
### Currently trained for lesser iterations, will be updated soon!
| {"tags": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pong", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{"type":... | epsil/Reinforce-Pong | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-03T15:28:26+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
### Currently trained for lesser iterations, will be updated soon!
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n \n ### Currently trained for lesser iterations, will be updated soon!"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n \n ### Currently trained for lesser iterations, will be updated soon!"
] |
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="coledie/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | coledie/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-03T16:14:23+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | coledie/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-03T16:15:37+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) o... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "test_trainer", "results": []}]} | Pro0100Hy6/test_trainer | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T16:33:02+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| test\_trainer
=============
This model is a fine-tuned version of cointegrated/rubert-tiny on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7773
* Accuracy: 0.6375
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_b... |
text-generation | transformers |
# Technoblade DialoGPT Model | {"tags": ["conversational"]} | Naturealbe/DialoGPT-small-Technoblade | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T16:53:55+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Technoblade DialoGPT Model | [
"# Technoblade DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Technoblade DialoGPT Model"
] |
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-complaints-wandb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilbert-complaints-wandb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | Kayvane/distilbert-complaints-wandb | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T17:06:15+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-complaints-wandb
===========================
This model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4448
* Accuracy: 0.8689
* F1: 0.8631
* Recall: 0.8689
* Precision: 0.8616
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #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... |
null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
| {"license": "apache-2.0", "title": "trader", "emoji": "\u26a1", "colorFrom": "purple", "colorTo": "yellow", "sdk": "streamlit", "sdk_version": "1.2.0", "app_file": "app.py", "pinned": false} | tonne/trader | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-07-03T17:07:16+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
Check out the configuration reference at URL
| [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | xzhang/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T17:16:34+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
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6421
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #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... |
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"]} | kingabzpro/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-03T17:30:52+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... |
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. -->
# distilgpt2-finetuned-spam
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-spam", "results": []}]} | xzhang/distilgpt2-finetuned-spam | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T18:03:45+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
| distilgpt2-finetuned-spam
=========================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.1656
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #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... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** 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 tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | kingabzpro/MLAgents-Worm | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-07-03T18:09:56+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm 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 Worm\n This is a trained model of a ppo agent playing Worm 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 training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
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... | xliu128/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 | 2022-07-03T18:24:30+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: 0.1363
* F1: 0.8627
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | postgrammar/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T18:26:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #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: 0.2204
* Accuracy: 0.9245
* F1: 0.9244
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
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... | ramonzaca/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-03T18:30:25+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
# CodeParrot-Multi 🦜 (small)
CodeParrot-Multi 🦜 is a GPT-2 model (110M parameters) trained to generate code in 9 programming languages: "Java", "JavaScript", "PHP", "Python", "C#", "C++", "GO", "Ruby" and "TypeScript".
## Usage
You can load the CodeParrot-Multi model and tokenizer directly in `transformers`:
```... | {"language": ["code"], "license": "apache-2.0", "tags": ["code", "gpt2", "generation"], "datasets": ["codeparrot/github-code-clean", "openai_humaneval"], "metrics": ["evaluate-metric/code_eval"]} | codeparrot/codeparrot-small-multi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"code",
"generation",
"dataset:codeparrot/github-code-clean",
"dataset:openai_humaneval",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-03T18:34:10+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #code #generation #dataset-codeparrot/github-code-clean #dataset-openai_humaneval #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| CodeParrot-Multi (small)
========================
CodeParrot-Multi is a GPT-2 model (110M parameters) trained to generate code in 9 programming languages: "Java", "JavaScript", "PHP", "Python", "C#", "C++", "GO", "Ruby" and "TypeScript".
Usage
-----
You can load the CodeParrot-Multi model and tokenizer directly i... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #code #generation #dataset-codeparrot/github-code-clean #dataset-openai_humaneval #license-apache-2.0 #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. -->
# NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2
This model is a fine-tuned version of [ajtamayoh/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Despert\u00e9 del coma con una inquietud espiritual, que me llev\u00f3 a mirar al cielo y a encontrar la paz, entrevista a Piki\u00a0Pfaff https://t.co/JgXnDrXjLN https://t.co/95eVVQO... | ajtamayoh/NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-03T18:37:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| NLP-CIC-WFU\_SocialDisNER\_fine\_tuned\_NER\_EHR\_Spanish\_model\_Mulitlingual\_BERT\_v2
========================================================================================
This model is a fine-tuned version of ajtamayoh/NER\_EHR\_Spanish\_model\_Mulitlingual\_BERT on the dataset provided by SocialDisNER shared ... | [
"### 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: 7",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\... |
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