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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | AA1152/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T02:15:57+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-emotion
=========================================
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: 0.2130
* Accuracy: 0.9275
* F1: 0.9277
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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... |
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. -->
# 4-way-detection-prop-16-distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "4-way-detection-prop-16-distilbert", "results": []}]} | ultra-coder54732/4-way-detection-prop-16-distilbert | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T03:29:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 4-way-detection-prop-16-distilbert
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# 4-way-detection-prop-16-distilbert\n\nThis model is a fine-tuned version of distilbert-base-uncased 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",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# 4-way-detection-prop-16-distilbert\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## ... |
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. -->
# 4-way-detection-prop-16-deberta
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "4-way-detection-prop-16-deberta", "results": []}]} | ultra-coder54732/4-way-detection-prop-16-deberta | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T04:28:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# 4-way-detection-prop-16-deberta
This model is a fine-tuned version of microsoft/deberta-base 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... | [
"# 4-way-detection-prop-16-deberta\n\nThis model is a fine-tuned version of microsoft/deberta-base 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",
"## Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# 4-way-detection-prop-16-deberta\n\nThis model is a fine-tuned version of microsoft/deberta-base on an unknown dataset.",
"## Model descript... |
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-xls-r-bengali_v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-bengali_v1", "results": []}]} | aimanlameesa/wav2vec2-xls-r-bengali_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T08:34:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-bengali\_v1
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2973
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* tr... |
sentence-similarity | sentence-transformers |
# louis030195/multi-qa-MiniLM-L6-cos-v1-de-ecommerce
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | louis030195/multi-qa-MiniLM-L6-cos-v1-de-ecommerce | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T08:41:03+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# louis030195/multi-qa-MiniLM-L6-cos-v1-de-ecommerce
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-tra... | [
"# louis030195/multi-qa-MiniLM-L6-cos-v1-de-ecommerce\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have se... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# louis030195/multi-qa-MiniLM-L6-cos-v1-de-ecommerce\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can b... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **PushBlock**
This is a trained model of a **ppo** agent playing **PushBlock** 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 comp... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-PushBlock"]} | rebolforces/ppo-pushblock-9M | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-PushBlock",
"region:us"
] | null | 2022-08-21T09:09:38+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us
|
# ppo Agent playing PushBlock
This is a trained model of a ppo agent playing PushBlock 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 train... | [
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock 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... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us \n",
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Docu... |
text-generation | transformers | # Conversational QA
This framework is trained on the [CoQA dataset](https://stanfordnlp.github.io/coqa/).
# Install
pip install conversation-qa
# Example
```python
from conversation_qa import QA, Dialogue
qa = QA("fractalego/conversation-qa")
dialogue = Dialogue()
dialogue.add_dialogue_pair("Where was the cat?"... | {} | fractalego/conversation-qa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"doi:10.57967/hf/0010",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-21T09:26:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #doi-10.57967/hf/0010 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Conversational QA
This framework is trained on the CoQA dataset.
# Install
pip install conversation-qa
# Example
| [
"# Conversational QA\nThis framework is trained on the CoQA dataset.",
"# Install\npip install conversation-qa",
"# Example"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #doi-10.57967/hf/0010 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Conversational QA\nThis framework is trained on the CoQA dataset.",
"# Install\npip install conversation-qa",
"# Example"
] |
null | null | ## Inverse Cooking: Recipe Generation from Food Images
Code supporting the paper:
*Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero.
[Inverse Cooking: Recipe Generation from Food Images. ](https://arxiv.org/abs/1812.06164)
CVPR 2019*
If you find this code useful in your research, please consider... | {} | justahandsomeboy/recipedia | null | [
"arxiv:1812.06164",
"region:us"
] | null | 2022-08-21T09:44:36+00:00 | [
"1812.06164"
] | [] | TAGS
#arxiv-1812.06164 #region-us
| ## Inverse Cooking: Recipe Generation from Food Images
Code supporting the paper:
*Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero.
Inverse Cooking: Recipe Generation from Food Images.
CVPR 2019*
If you find this code useful in your research, please consider citing using the
following BibTeX e... | [
"## Inverse Cooking: Recipe Generation from Food Images\n\nCode supporting the paper:\n\n*Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero.\nInverse Cooking: Recipe Generation from Food Images. \nCVPR 2019*\n\n\nIf you find this code useful in your research, please consider citing using the\nfol... | [
"TAGS\n#arxiv-1812.06164 #region-us \n",
"## Inverse Cooking: Recipe Generation from Food Images\n\nCode supporting the paper:\n\n*Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero.\nInverse Cooking: Recipe Generation from Food Images. \nCVPR 2019*\n\n\nIf you find this code useful in your rese... |
automatic-speech-recognition | nemo | # NVIDIA Streaming Citrinet 512 (uk-UA)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Lang... | {"language": ["uk"], "license": "bsd-3-clause", "library_name": "nemo", "tags": ["automatic-speech-recognition"], "datasets": ["mozilla-foundation/common_voice_10_0", "Yehor/voa-uk-transcriptions"], "model-index": [{"name": "stt_uk_citrinet_512_gamma_0_25", "results": [{"task": {"type": "automatic-speech-recognition", ... | neongeckocom/stt_uk_citrinet_512_gamma_0_25 | null | [
"nemo",
"onnx",
"automatic-speech-recognition",
"uk",
"dataset:mozilla-foundation/common_voice_10_0",
"dataset:Yehor/voa-uk-transcriptions",
"license:bsd-3-clause",
"model-index",
"region:us"
] | null | 2022-08-21T10:19:36+00:00 | [] | [
"uk"
] | TAGS
#nemo #onnx #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #dataset-Yehor/voa-uk-transcriptions #license-bsd-3-clause #model-index #region-us
| # NVIDIA Streaming Citrinet 512 (uk-UA)
<style>
img {
display: inline;
}
</style>
| 
| 
|  |
## Attribution
As initial checkpoint used stt_en_citrinet_512_gamma_0_25 by NVIDIA licensed under CC-BY-4.0 | [
"# NVIDIA Streaming Citrinet 512 (uk-UA)\n\n<style>\nimg {\n display: inline;\n}\n</style>\n\n| \n| \n|  |",
"## Attribution\nAs initial checkpoint used stt_en_citrinet_512_gamma_0_25 by NVIDIA licensed under CC-BY-4... | [
"TAGS\n#nemo #onnx #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #dataset-Yehor/voa-uk-transcriptions #license-bsd-3-clause #model-index #region-us \n",
"# NVIDIA Streaming Citrinet 512 (uk-UA)\n\n<style>\nimg {\n display: inline;\n}\n</style>\n\n| ![Model architecture](#model-ar... |
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": []}]} | TakeHirako/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 | 2022-08-21T11:29:29+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*... |
null | null | git lfs install
git clone https://huggingface.co/FluxML/vgg16 | {} | Yahiya/model520.h5 | null | [
"region:us"
] | null | 2022-08-21T11:53:02+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_imtiaz
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cvbn"], "model-index": [{"name": "wav2vec2_imtiaz", "results": []}]} | MBMMurad/wav2vec2_imtiaz | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:cvbn",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T11:53:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2_imtiaz
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1956
- eval_wer: 0.2202
- eval_runtime: 574.912
- eval_samples_per_second: 8.697
- eval_steps_per_second: 0.544
- epoch: 9.41
- step: 2200... | [
"# wav2vec2_imtiaz\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.1956\n- eval_wer: 0.2202\n- eval_runtime: 574.912\n- eval_samples_per_second: 8.697\n- eval_steps_per_second: 0.544\n- epoch: 9.41\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2_imtiaz\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the followin... |
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": []}]} | TakeHirako/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 | 2022-08-21T12:03:19+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: 0.1745
* F1: 0.8505
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-bert-sst2-1_mobilebert-2_bert-distillation
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-1_mobilebert-2_bert-distillation", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "confi... | gokuls/tiny-bert-sst2-1_mobilebert-2_bert-distillation | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T12:26:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-sst2-1\_mobilebert-2\_bert-distillation
=================================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0697
* Accuracy: 0.8360
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | VanHoan/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T12:31:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0644
* Precision: 0.9326
* Recall: 0.9502
* F1: 0.9413
* Accuracy: 0.9856
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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. -->
# m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu1
This model is a fine-tuned version of [fac... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["un_multi"], "metrics": ["bleu"], "model-index": [{"name": "m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"},... | abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu1 | null | [
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T12:34:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #generated_from_trainer #dataset-un_multi #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| m2m100\_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu1
======================================================================================
This model is a fine-tuned version of facebook/m2m100\_418M on the un\_multi dataset.
It achieves the following results on the evaluation set:
... | [
"### 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: 11\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #generated_from_trainer #dataset-un_multi #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\\_ra... |
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. -->
# tiny-bert-sst2-1_mobilebert_and_bert-multi-teacher-distillation
This model is a fine-tuned version of [google/bert_uncased_L-2_H... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-1_mobilebert_and_bert-multi-teacher-distillation", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type"... | gokuls/tiny-bert-sst2-1_mobilebert_and_bert-multi-teacher-distillation | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T12:43:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-sst2-1\_mobilebert\_and\_bert-multi-teacher-distillation
==================================================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5545
* Accuracy: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# tiny-bert-sst2-1_mobilebert-only-distillation
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-1_mobilebert-only-distillation", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config"... | gokuls/tiny-bert-sst2-1_mobilebert-only-distillation | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T13:10:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-sst2-1\_mobilebert-only-distillation
==============================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2808
* Accuracy: 0.8291
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
null | diffusers |
# ddpm-EmojiAlignedFaces-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the [Norod78/EmojiFFHQAlignedFaces](https://huggingface.co/datasets/Norod78/EmojiFFHQAlignedFaces) dataset.
#### How to use
```python
from diffusers import ... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "Norod78/EmojiFFHQAlignedFaces", "metrics": []} | Norod78/ddpm-EmojiAlignedFaces-64 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:Norod78/EmojiFFHQAlignedFaces",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-21T13:27:14+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-Norod78/EmojiFFHQAlignedFaces #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# ddpm-EmojiAlignedFaces-64
## Model description
This diffusion model is trained with the Diffusers library
on the Norod78/EmojiFFHQAlignedFaces dataset.
#### How to use
### Training data
Norod78/EmojiFFHQAlignedFaces
### Training results
TensorBoard logs
| [
"# ddpm-EmojiAlignedFaces-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the Norod78/EmojiFFHQAlignedFaces dataset.",
"#### How to use",
"### Training data\n\nNorod78/EmojiFFHQAlignedFaces",
"### Training results\n\n TensorBoard logs"
] | [
"TAGS\n#diffusers #tensorboard #en #dataset-Norod78/EmojiFFHQAlignedFaces #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# ddpm-EmojiAlignedFaces-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the Norod78/EmojiFFHQAlignedFaces dataset.",
... |
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. -->
# tiny-bert-sst2-1_mobilebert_2_bert-only-distillation
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-1_mobilebert_2_bert-only-distillation", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "... | gokuls/tiny-bert-sst2-1_mobilebert_2_bert-only-distillation | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T13:45:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-sst2-1\_mobilebert\_2\_bert-only-distillation
=======================================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5399
* Accuracy: 0.8291
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | osanseviero/fastai_bears | null | [
"fastai",
"region:us"
] | null | 2022-08-21T13:54:33+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
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. -->
# tiny-bert-sst2-1_mobilebert_2_bert_3_gold_labels-distillation
This model is a fine-tuned version of [google/bert_uncased_L-2_H-1... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-1_mobilebert_2_bert_3_gold_labels-distillation", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": ... | gokuls/tiny-bert-sst2-1_mobilebert_2_bert_3_gold_labels-distillation | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T13:56:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-sst2-1\_mobilebert\_2\_bert\_3\_gold\_labels-distillation
===================================================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9350
* Accuracy:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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 [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "test_trainer", "results": []}]} | Teeto/test_trainer | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T14:22:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| test\_trainer
=============
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1667
* Accuracy: 0.9464
Model description
-----------------
More information needed
Intended uses & limitations
--------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
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. -->
# 4-way-detection-prop-16-xlnet
This model is a fine-tuned version of [ultra-coder54732/4-way-detection-prop-16-bert](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "4-way-detection-prop-16-xlnet", "results": []}]} | ultra-coder54732/4-way-detection-prop-16-xlnet | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T14:29:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 4-way-detection-prop-16-xlnet
This model is a fine-tuned version of ultra-coder54732/4-way-detection-prop-16-bert on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# 4-way-detection-prop-16-xlnet\n\nThis model is a fine-tuned version of ultra-coder54732/4-way-detection-prop-16-bert on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# 4-way-detection-prop-16-xlnet\n\nThis model is a fine-tuned version of ultra-coder54732/4-way-detection-prop-16-bert on an unknown datase... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-mnist
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | {"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["mnist"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-mnist", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "mnist", "type": "mnist", ... | farleyknight-org-username/vit-base-mnist | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:mnist",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-21T15:48:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-base-mnist
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mnist dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0236
* Accuracy: 0.9949
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: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln69Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln69Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln69Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-21T15:49:57+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikiSQL
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-wikiSQL", "results": []}]} | abaldaniya29/t5-small-finetuned-wikiSQL | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-21T16:20:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikiSQL
==========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1110
* Rouge2 Precision: 0.8308
* Rouge2 Recall: 0.7395
* Rouge2 Fmeasure: 0.7754
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln70Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln70Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln70Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-21T16:28:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
## DevBERT
DevBERT is a Devanagari BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi and Marathi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [<... | {"language": ["hi", "mr", "multilingual"], "license": "cc-by-4.0"} | l3cube-pune/hindi-marathi-dev-bert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"hi",
"mr",
"multilingual",
"arxiv:2211.11418",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T17:00:43+00:00 | [
"2211.11418"
] | [
"hi",
"mr",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #hi #mr #multilingual #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## DevBERT
DevBERT is a Devanagari BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi and Marathi monolingual datasets.
[project link] (URL
More details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .
Citing:
Ot... | [
"## DevBERT\nDevBERT is a Devanagari BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi and Marathi monolingual datasets. \n[project link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .\n\nCi... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #hi #mr #multilingual #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## DevBERT\nDevBERT is a Devanagari BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly avail... |
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
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emot... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "train", "... | jplum87/test | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T17:53:47+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
| test
====
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: 0.2778
* Accuracy: 0.9335
* F1:: 0.9337
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: 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... |
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... | theicfire/atari1 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-21T18:55:31+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **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... | Mhd/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-21T19:00:33+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... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
re... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | theicfire/lunar-dqn2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-21T20:21:52+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing LunarLander-v2
This is a trained model of a DQN agent playing LunarLander-v2
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with... | [
"# DQN Agent playing LunarLander-v2\nThis is a trained model of a DQN agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
... | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing LunarLander-v2\nThis is a trained model of a DQN agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework... |
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. -->
# t5-small-text-summary-generation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the e... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-small-text-summary-generation", "results": []}]} | Yihui/t5-small-text-summary-generation | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-21T20:28:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-text-summary-generation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# t5-small-text-summary-generation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-text-summary-generation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on ... |
text-classification | transformers |
# distilroberta-base-finetuned-fake-news-detection
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on [this](https://huggingface.co/datasets/GonzaloA/fake_news) Fake News Detection Dataset, which has been constructed by combining multiple Fake News datasets from K... | {"license": "apache-2.0", "tags": ["Fake News Detection", "Text Classification"], "model-index": [{"name": "distilroberta-base-finetuned-fake-news-detection", "results": []}]} | vikram71198/distilroberta-base-finetuned-fake-news-detection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"Fake News Detection",
"Text Classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T20:30:14+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #Fake News Detection #Text Classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-fake-news-detection
================================================
This model is a fine-tuned version of distilroberta-base on this Fake News Detection Dataset, which has been constructed by combining multiple Fake News datasets from Kaggle.
This is the classification report after tra... | [
"### 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* optimizer: default AdamW Optimizer\n* num\\_epochs: 3\n* warmup\\_steps: 500\n* weight\\_decay: 0.01\n* random seed: 42\n\n\nI also trai... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #Fake News Detection #Text Classification #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\\... |
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. -->
# paraphrase_detector
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "paraphrase_detector", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "mrpc", "split": "... | abdulmatinomotoso/paraphrase_detector | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-21T20:45:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| paraphrase\_detector
====================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6599
* Accuracy: 0.8554
* F1: 0.8985
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-generation | transformers | # Architext GPT-J 162M
# Model Description
Architext GPT-J-162M is a transformer model trained using Ben Wang's Mesh Transformer JAX on the Pile and finetuned specifically on a synthetically generated dataset of architectural layouts of apartments. It is capable of generating a large diversity of designs, in a conveni... | {"language": ["en"], "license": "apache-2.0", "tags": ["architecture", "design"], "datasets": ["THEODOROS/Architext_v1"], "pipeline_tag": "text-generation"} | architext/gptj-162M | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"architecture",
"design",
"en",
"dataset:THEODOROS/Architext_v1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-21T23:04:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gptj #text-generation #architecture #design #en #dataset-THEODOROS/Architext_v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Architext GPT-J 162M
# Model Description
Architext GPT-J-162M is a transformer model trained using Ben Wang's Mesh Transformer JAX on the Pile and finetuned specifically on a synthetically generated dataset of architectural layouts of apartments. It is capable of generating a large diversity of designs, in a conveni... | [
"# Architext GPT-J 162M",
"# Model Description\nArchitext GPT-J-162M is a transformer model trained using Ben Wang's Mesh Transformer JAX on the Pile and finetuned specifically on a synthetically generated dataset of architectural layouts of apartments. It is capable of generating a large diversity of designs, in... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #architecture #design #en #dataset-THEODOROS/Architext_v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Architext GPT-J 162M",
"# Model Description\nArchitext GPT-J-162M is a transformer model trained using Ben ... |
null | null | This model is used for experimental purposes for EN/AR data.
`text-classification`
`Text Classification`
widget:
- text: "ما حكم الزكاة؟" | {"license": "apache-2.0"} | MohamedElkamhawy/IslamQA | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-21T23:28:02+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| This model is used for experimental purposes for EN/AR data.
'text-classification'
'Text Classification'
widget:
- text: "ما حكم الزكاة؟" | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
image-segmentation | 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. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-sidewalk-2", "results": []}]} | nishita/segformer-b0-finetuned-segments-sidewalk-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T23:56:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-sidewalk-2
==========================================
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6306
* Mean Iou: 0.1027
* Mean Accuracy: 0.1574
* Overall Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 16\n* ev... |
text-to-speech | null | ## GroTTS Model
This model is trained with the [Tacotron 2](https://arxiv.org/abs/1712.05884) architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from [here](https://huggingface.co/ahnafsamin/parallelwavegan-gronings) and then use the following... | {"language": "gos", "tags": ["text-to-speech", "gronings", "Tacotron 2"], "datasets": ["gronings"]} | ahnafsamin/Tacotron2-gronings | null | [
"text-to-speech",
"gronings",
"Tacotron 2",
"gos",
"dataset:gronings",
"arxiv:1712.05884",
"region:us"
] | null | 2022-08-22T00:02:31+00:00 | [
"1712.05884"
] | [
"gos"
] | TAGS
#text-to-speech #gronings #Tacotron 2 #gos #dataset-gronings #arxiv-1712.05884 #region-us
| ## GroTTS Model
This model is trained with the Tacotron 2 architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from here and then use the following code:
## TTS config
<details><summary>expand</summary>
</details> | [
"## GroTTS Model \n\nThis model is trained with the Tacotron 2 architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from here and then use the following code:",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n</details>"
] | [
"TAGS\n#text-to-speech #gronings #Tacotron 2 #gos #dataset-gronings #arxiv-1712.05884 #region-us \n",
"## GroTTS Model \n\nThis model is trained with the Tacotron 2 architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from here and then use ... |
null | null |
## 라이브러리 버전
- transformers: 4.21.1
- datasets: 2.4.0
- tokenizers: 0.12.1
[Bingsu/ko_BBPE_tokenizer_roberta](https://huggingface.co/Bingsu/ko_BBPE_tokenizer_roberta)에서 post-processor를 BertProcessing로 변경하고 토크나이저 클래스를 `BertTokenizerFast`로 변경한 것입니다.
두 문장을 토크나이저에 입력했을 때, sep 토큰이 다르게 처리된다는 것을 제외하고 결과는 동일합니다. | {"language": ["ko"], "license": ["mit"], "tags": ["bert", "tokenizer only"]} | Bingsu/ko_BBPE_tokenizer_bert | null | [
"bert",
"tokenizer only",
"ko",
"license:mit",
"region:us"
] | null | 2022-08-22T00:11:40+00:00 | [] | [
"ko"
] | TAGS
#bert #tokenizer only #ko #license-mit #region-us
|
## 라이브러리 버전
- transformers: 4.21.1
- datasets: 2.4.0
- tokenizers: 0.12.1
Bingsu/ko_BBPE_tokenizer_roberta에서 post-processor를 BertProcessing로 변경하고 토크나이저 클래스를 'BertTokenizerFast'로 변경한 것입니다.
두 문장을 토크나이저에 입력했을 때, sep 토큰이 다르게 처리된다는 것을 제외하고 결과는 동일합니다. | [
"## 라이브러리 버전\n\n- transformers: 4.21.1\n- datasets: 2.4.0\n- tokenizers: 0.12.1\n\nBingsu/ko_BBPE_tokenizer_roberta에서 post-processor를 BertProcessing로 변경하고 토크나이저 클래스를 'BertTokenizerFast'로 변경한 것입니다.\n\n두 문장을 토크나이저에 입력했을 때, sep 토큰이 다르게 처리된다는 것을 제외하고 결과는 동일합니다."
] | [
"TAGS\n#bert #tokenizer only #ko #license-mit #region-us \n",
"## 라이브러리 버전\n\n- transformers: 4.21.1\n- datasets: 2.4.0\n- tokenizers: 0.12.1\n\nBingsu/ko_BBPE_tokenizer_roberta에서 post-processor를 BertProcessing로 변경하고 토크나이저 클래스를 'BertTokenizerFast'로 변경한 것입니다.\n\n두 문장을 토크나이저에 입력했을 때, sep 토큰이 다르게 처리된다는 것을 제외하고 결과는 동... |
null | transformers |
# mDeBERTa-v3-base-kor-further
> 💡 아래 프로젝트는 KPMG Lighthouse Korea에서 진행하였습니다.
> KPMG Lighthouse Korea에서는, Financial area의 다양한 문제들을 해결하기 위해 Edge Technology의 NLP/Vision AI를 모델링하고 있습니다.
> https://kpmgkr.notion.site/
## What is DeBERTa?
- [DeBERTa](https://arxiv.org/abs/2006.03654)는 `Disentangled Attention` + `Enhanc... | {"language": ["multilingual", "en", "ko", "ar", "bg", "de", "el", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": "mit", "tags": ["deberta", "deberta-v3", "mdeberta", "korean", "pretraining"]} | lighthouse/mdeberta-v3-base-kor-further | null | [
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"licens... | null | 2022-08-22T01:12:13+00:00 | [
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"es",
"fr",
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"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh"
] | TAGS
#transformers #pytorch #deberta-v2 #deberta #deberta-v3 #mdeberta #korean #pretraining #multilingual #en #ko #ar #bg #de #el #es #fr #hi #ru #sw #th #tr #ur #vi #zh #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #endpoints_compatible #region-us
| mDeBERTa-v3-base-kor-further
============================
>
> 아래 프로젝트는 KPMG Lighthouse Korea에서 진행하였습니다.
>
> KPMG Lighthouse Korea에서는, Financial area의 다양한 문제들을 해결하기 위해 Edge Technology의 NLP/Vision AI를 모델링하고 있습니다.
> URL
>
>
>
What is DeBERTa?
----------------
* DeBERTa는 'Disentangled Attention' + 'Enhanced M... | [] | [
"TAGS\n#transformers #pytorch #deberta-v2 #deberta #deberta-v3 #mdeberta #korean #pretraining #multilingual #en #ko #ar #bg #de #el #es #fr #hi #ru #sw #th #tr #ur #vi #zh #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #endpoints_compatible #region-us \n"
] |
text-generation | transformers | onnx model for codegen-350-multi
made this to be used with my example code completion extensions repo:
https://github.com/SirWaffle/local-ai-code-completion | {} | SirWaffle/codegen-350M-multi-onnx | null | [
"transformers",
"onnx",
"codegen",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T01:20:25+00:00 | [] | [] | TAGS
#transformers #onnx #codegen #text-generation #autotrain_compatible #endpoints_compatible #region-us
| onnx model for codegen-350-multi
made this to be used with my example code completion extensions repo:
URL | [] | [
"TAGS\n#transformers #onnx #codegen #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | VanHoan/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T02:17:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3229
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln71Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln71Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln71Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T02:24:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
Backwards
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-WholeWordMasking-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-WholeWordMasking-finetuned-imdb", "results": []}]} | VanHoan/distilbert-base-uncased-WholeWordMasking-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T02:51:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-WholeWordMasking-finetuned-imdb
=======================================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6211
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: 3.0\n* mixed\\_pr... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-generation | transformers |
This model is based on [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m).
We pruned its vocabulary from 250880 to 42437 with Chinese corpus to reduce GPU memory usage. So the total parameter is 389m now.
# How to use
```python
from transformers import BloomTokenizerFast, BloomForCausalLM
tokeni... | {"language": ["zh"], "license": "bigscience-bloom-rail-1.0", "pipeline_tag": "text-generation", "widget": [{"text": "\u4e2d\u56fd\u7684\u9996\u90fd\u662f"}]} | Langboat/bloom-389m-zh | null | [
"transformers",
"pytorch",
"bloom",
"text-generation",
"zh",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T05:39:40+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bloom #text-generation #zh #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This model is based on bigscience/bloom-560m.
We pruned its vocabulary from 250880 to 42437 with Chinese corpus to reduce GPU memory usage. So the total parameter is 389m now.
# How to use
| [
"# How to use"
] | [
"TAGS\n#transformers #pytorch #bloom #text-generation #zh #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# How to use"
] |
fill-mask | transformers | ## MahaTweetBERT
A MahaBERT (l3cube-pune/marathi-bert-v2) model finetuned on Marathi Tweets.
More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)
Released under project: https://github.com/l3cube-pune/MarathiNLP
```
@article{gokh... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaCorpus"]} | l3cube-pune/marathi-tweets-bert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"mr",
"dataset:L3Cube-MahaCorpus",
"arxiv:2210.04267",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T05:52:27+00:00 | [
"2210.04267"
] | [
"mr"
] | TAGS
#transformers #pytorch #bert #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2210.04267 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ## MahaTweetBERT
A MahaBERT (l3cube-pune/marathi-bert-v2) model finetuned on Marathi Tweets.
More details on the dataset, models, and baseline results can be found in our [paper] (<a href='URL link </a>)
Released under project: URL
| [
"## MahaTweetBERT\nA MahaBERT (l3cube-pune/marathi-bert-v2) model finetuned on Marathi Tweets.\nMore details on the dataset, models, and baseline results can be found in our [paper] (<a href='URL link </a>)\n\nReleased under project: URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #mr #dataset-L3Cube-MahaCorpus #arxiv-2210.04267 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MahaTweetBERT\nA MahaBERT (l3cube-pune/marathi-bert-v2) model finetuned on Marathi Tweets.\nMore details on the dataset, models, and ba... |
fill-mask | transformers |
# ERNIE-3.0-base-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of e... | {"language": "zh"} | nghuyong/ernie-3.0-base-zh | null | [
"transformers",
"pytorch",
"ernie",
"fill-mask",
"zh",
"arxiv:2107.02137",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-22T06:54:44+00:00 | [
"2107.02137"
] | [
"zh"
] | TAGS
#transformers #pytorch #ernie #fill-mask #zh #arxiv-2107.02137 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ERNIE-3.0-base-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: URL
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducte... | [
"# ERNIE-3.0-base-zh",
"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
"## Released Model Info\n\nThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and \na series of experiments ... | [
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"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
"## Relea... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-paraphrasing-mlm-med-mask-filling-cm0
This model is a fine-tuned version of [gayanin/t5-small-paraphrase-pubmed](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-paraphrasing-mlm-med-mask-filling-cm0", "results": []}]} | gayanin/t5-small-paraphrasing-mlm-med-mask-filling-cm0 | null | [
"transformers",
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"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T07:33:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-paraphrasing-mlm-med-mask-filling-cm0
==============================================
This model is a fine-tuned version of gayanin/t5-small-paraphrase-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6697
* Rouge2 Precision: 0.6929
* Rouge2 Recall: 0.4742
* R... | [
"### 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: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-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. -->
# ft_clinical_bert_diabetes
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyal... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "ft_clinical_bert_diabetes", "results": []}]} | ericntay/ft_clinical_bert_diabetes | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T07:42:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ft\_clinical\_bert\_diabetes
============================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1020
* Accuracy: 0.9632
* F1: 0.9578
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 3e-05\n* train\\_batch\\_size: ... |
text-to-speech | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech
This repository provides all the necessary tools ... | {"language": "sw", "license": "apache-2.0", "tags": ["text-to-speech", "TTS", "speech-synthesis", "Tacotron2", "speechbrain"], "datasets": ["ALFFA_Public"], "metrics": ["mos"]} | aioxlabs/tacotron-swahili | null | [
"speechbrain",
"text-to-speech",
"TTS",
"speech-synthesis",
"Tacotron2",
"sw",
"dataset:ALFFA_Public",
"arxiv:1712.05884",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-08-22T07:48:23+00:00 | [
"1712.05884",
"2106.04624"
] | [
"sw"
] | TAGS
#speechbrain #text-to-speech #TTS #speech-synthesis #Tacotron2 #sw #dataset-ALFFA_Public #arxiv-1712.05884 #arxiv-2106.04624 #license-apache-2.0 #region-us
|
<iframe src="URL frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech
This repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a Tacotron2 pretrained on ALLFA Public.
The pre-trai... | [
"# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech\n\nThis repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a Tacotron2 pretrained on ALLFA Public.\n\nThe pre-trained model takes in input a short text and produces a spectrogram in output. One can get the final wav... | [
"TAGS\n#speechbrain #text-to-speech #TTS #speech-synthesis #Tacotron2 #sw #dataset-ALFFA_Public #arxiv-1712.05884 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"# Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech\n\nThis repository provides all the necessary tools for Text-to-Speech (TTS) with Spee... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-ja-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_10_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ja-colab", "results": []}]} | pinot/wav2vec2-large-xls-r-300m-ja-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T07:52:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-ja-colab
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_10\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1407
* Wer: 0.2456
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003... |
null | null | just for test | {} | mivenis/distilbert-base-uncased-finetuned-imdb | null | [
"region:us"
] | null | 2022-08-22T07:53:23+00:00 | [] | [] | TAGS
#region-us
| just for test | [] | [
"TAGS\n#region-us \n"
] |
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"]} | kws/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-22T08:24:10+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... |
fill-mask | transformers |
# ERNIE-3.0-medium-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of... | {"language": "zh"} | nghuyong/ernie-3.0-medium-zh | null | [
"transformers",
"pytorch",
"ernie",
"fill-mask",
"zh",
"arxiv:2107.02137",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-22T08:29:01+00:00 | [
"2107.02137"
] | [
"zh"
] | TAGS
#transformers #pytorch #ernie #fill-mask #zh #arxiv-2107.02137 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ERNIE-3.0-medium-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: URL
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conduc... | [
"# ERNIE-3.0-medium-zh",
"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
"## Released Model Info\n\nThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and \na series of experiment... | [
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"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
"## Rel... |
feature-extraction | transformers |
# ERNIE-3.0-mini-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of e... | {"language": "zh"} | nghuyong/ernie-3.0-mini-zh | null | [
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"zh",
"arxiv:2107.02137",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T08:33:09+00:00 | [
"2107.02137"
] | [
"zh"
] | TAGS
#transformers #pytorch #ernie #feature-extraction #zh #arxiv-2107.02137 #endpoints_compatible #region-us
|
# ERNIE-3.0-mini-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: URL
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducte... | [
"# ERNIE-3.0-mini-zh",
"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
"## Released Model Info\n\nThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and \na series of experiments ... | [
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feature-extraction | transformers |
# ERNIE-3.0-micro-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of ... | {"language": "zh"} | nghuyong/ernie-3.0-micro-zh | null | [
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"zh",
"arxiv:2107.02137",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T08:36:10+00:00 | [
"2107.02137"
] | [
"zh"
] | TAGS
#transformers #pytorch #ernie #feature-extraction #zh #arxiv-2107.02137 #endpoints_compatible #region-us
|
# ERNIE-3.0-micro-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: URL
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conduct... | [
"# ERNIE-3.0-micro-zh",
"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
"## Released Model Info\n\nThis released pytorch model is converted from the officially released PaddlePaddle ERNIE model and \na series of experiments... | [
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feature-extraction | transformers |
# ERNIE-3.0-nano-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of e... | {"language": "zh"} | nghuyong/ernie-3.0-nano-zh | null | [
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"zh",
"arxiv:2107.02137",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-22T08:39:34+00:00 | [
"2107.02137"
] | [
"zh"
] | TAGS
#transformers #pytorch #ernie #feature-extraction #zh #arxiv-2107.02137 #endpoints_compatible #has_space #region-us
|
# ERNIE-3.0-nano-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: URL
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducte... | [
"# ERNIE-3.0-nano-zh",
"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
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"## Introduction\n\nERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation\nMore detail: URL",
"## Released Model Inf... |
text-classification | transformers |
This Repository includes the files required to run the `Templates Recommendation` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service.
| {"license": "mit"} | orkg/orkgnlp-templates-recommendation | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T09:04:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
This Repository includes the files required to run the 'Templates Recommendation' ORKG-NLP service.
Please check this article for more details about the service.
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# Model Card for stable-diffusion-safety-checker
# Model Details
## Model Description
More information needed
- **Developed by:** More information needed
- **Shared by [Optional]:** CompVis
- **Model type:** Image Identification
- **Language(s) (NLP):** More information needed
- **License:** More information ... | {"tags": ["clip"]} | CompVis/stable-diffusion-safety-checker | null | [
"transformers",
"pytorch",
"clip",
"arxiv:2103.00020",
"arxiv:1910.09700",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-22T09:22:34+00:00 | [
"2103.00020",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #clip #arxiv-2103.00020 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
|
# Model Card for stable-diffusion-safety-checker
# Model Details
## Model Description
More information needed
- Developed by: More information needed
- Shared by [Optional]: CompVis
- Model type: Image Identification
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Mode... | [
"# Model Card for stable-diffusion-safety-checker",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: More information needed\n- Shared by [Optional]: CompVis\n- Model type: Image Identification \n- Language(s) (NLP): More information needed\n- License: More information ne... | [
"TAGS\n#transformers #pytorch #clip #arxiv-2103.00020 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n",
"# Model Card for stable-diffusion-safety-checker",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: More information needed\n- Shared by [Optional]:... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bengali-v7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bengali-v7", "results": []}]} | rashedsafa/wav2vec2-large-xls-r-300m-bengali-v7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T09:27:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-bengali-v7
====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2999
* Wer: 1.0
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* tr... |
text-generation | transformers |
# Graphcore/gptj-mnli
This model is the fine-tuned version of [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) on the [GLUE MNLI dataset](https://huggingface.co/datasets/glue#mnli).
MNLI dataset consists of pairs of sentences, a *premise* and a *hypothesis*.
The task is to predict the relation between... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm", "text-classification", "text-generation"], "datasets": ["glue"], "pipeline_tag": "text-generation", "widget": [{"text": "mnli hypothesis: Your contributions were of no help with our students' education. premise: Your contribution helped make... | Graphcore/gptj-mnli | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"text-classification",
"en",
"dataset:glue",
"arxiv:1910.10683",
"arxiv:2104.09864",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T09:35:28+00:00 | [
"1910.10683",
"2104.09864"
] | [
"en"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #text-classification #en #dataset-glue #arxiv-1910.10683 #arxiv-2104.09864 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Graphcore/gptj-mnli
===================
This model is the fine-tuned version of EleutherAI/gpt-j-6B on the GLUE MNLI dataset.
MNLI dataset consists of pairs of sentences, a *premise* and a *hypothesis*.
The task is to predict the relation between the premise and the hypothesis, which can be:
* 'entailment': hypothe... | [
"### Hyperparameters:\n\n\n* optimiser: AdamW (beta1: 0.9, beta2: 0.999, eps: 1e-6, weight decay: 0.0, learning rate: 5e-6)\n* learning rate schedule: warmup schedule (min: 1e-7, max: 5e-6, warmup proportion: 0.005995)\n* batch size: 128\n* training steps: 300. Each epoch consists of ceil(17,762/128) steps, hence 3... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #text-classification #en #dataset-glue #arxiv-1910.10683 #arxiv-2104.09864 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Hyperparameters:\n\n\n* optimiser: AdamW (beta1: 0.9, beta2: 0.999, eps: 1e-6... |
text-classification | transformers | --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 0.0002 --mlm \
3 layers | {} | alishudi/distil_mse_3 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T09:59:03+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 0.0002 --mlm \
3 layers | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-vi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-vi", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | VanHoan/marian-finetuned-kde4-en-to-vi | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T11:45:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-vi
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2217
- Bleu: 51.1008
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-vi\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2217\n- Bleu: 51.1008",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
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"# marian-finetuned-kde4-en-to-vi\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
null | stable-diffusion |
Test | {"library_name": "stable-diffusion"} | osanseviero/test_stability | null | [
"stable-diffusion",
"clip",
"region:us"
] | null | 2022-08-22T11:46:26+00:00 | [] | [] | TAGS
#stable-diffusion #clip #region-us
|
Test | [] | [
"TAGS\n#stable-diffusion #clip #region-us \n"
] |
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"]} | danieladejumo/MLAgents-Worm | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-08-22T12:13:42+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\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1297049687
- CO2 Emissions (in grams): 0.0705
## Validation Metrics
- Loss: 0.603
- Accuracy: 0.738
- Macro F1: 0.725
- Micro F1: 0.738
- Weighted F1: 0.737
- Macro Precision: 0.730
- Micro Precision: 0.738
- Weighted Precision: ... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["mclion/autotrain-data-test1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.07046485785777015}} | rstanic/autotrain-test1-1297049687 | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain",
"unk",
"dataset:mclion/autotrain-data-test1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T12:22:01+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-mclion/autotrain-data-test1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1297049687
- CO2 Emissions (in grams): 0.0705
## Validation Metrics
- Loss: 0.603
- Accuracy: 0.738
- Macro F1: 0.725
- Micro F1: 0.738
- Weighted F1: 0.737
- Macro Precision: 0.730
- Micro Precision: 0.738
- Weighted Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1297049687\n- CO2 Emissions (in grams): 0.0705",
"## Validation Metrics\n\n- Loss: 0.603\n- Accuracy: 0.738\n- Macro F1: 0.725\n- Micro F1: 0.738\n- Weighted F1: 0.737\n- Macro Precision: 0.730\n- Micro Precision: 0.738\n-... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-mclion/autotrain-data-test1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1297049687\n- CO2 Emissions (... |
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. -->
# bart-paraphrasing-mlm-med-mask-filling
This model is a fine-tuned version of [gayanin/bart-paraphrase-pubmed-1.1](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrasing-mlm-med-mask-filling", "results": []}]} | gayanin/bart-paraphrasing-mlm-med-mask-filling | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T12:28:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrasing-mlm-med-mask-filling
======================================
This model is a fine-tuned version of gayanin/bart-paraphrase-pubmed-1.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2528
* Rouge2 Precision: 0.8317
* Rouge2 Recall: 0.5986
* Rouge2 Fmeasure: ... | [
"### 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: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
sentence-similarity | sentence-transformers |
# bertin-sts-cc-news-es
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes e... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["LeoCordoba/CC-NEWS-ES-titles"], "pipeline_tag": "sentence-similarity"} | edumunozsala/bertin-sts-cc-news-es | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:LeoCordoba/CC-NEWS-ES-titles",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T12:51:47+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-LeoCordoba/CC-NEWS-ES-titles #endpoints_compatible #region-us
|
# bertin-sts-cc-news-es
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then ... | [
"# bertin-sts-cc-news-es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-LeoCordoba/CC-NEWS-ES-titles #endpoints_compatible #region-us \n",
"# bertin-sts-cc-news-es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector spa... |
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. -->
# detect-femicide-news-bert-nl-None
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/Gr... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "detect-femicide-news-bert-nl-None", "results": []}]} | gossminn/detect-femicide-news-bert-nl-None | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T12:56:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| detect-femicide-news-bert-nl-None
=================================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8162
* Accuracy: 0.75
* Precision Neg: 0.8235
* Precision Pos: 0.6364
* Recall Neg: 0.7778
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100",
"### Tra... | [
"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: 1e-05\n* train\\_batch\\_size: 24\n* eval\\_... |
text-generation | transformers | ## LuxGPT-2
GPT-2 model for Text Generation in luxembourgish language, trained on 667 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles.
The training took place on a 32 GB Nvidia Tesla V100
- with an initial learning rate ... | {"language": ["lb"], "license": "mit", "tags": ["luxembourgish", "l\u00ebtzebuergesch", "text generation"], "model-index": [{"name": "LuxGPT2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Luxembourgish Test Dataset", "type": "LuxembourgishTestDataset"}, "metrics": [... | laurabernardy/LuxGPT2 | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"luxembourgish",
"lëtzebuergesch",
"text generation",
"lb",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T13:06:05+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #luxembourgish #lëtzebuergesch #text generation #lb #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## LuxGPT-2
GPT-2 model for Text Generation in luxembourgish language, trained on 667 MB of text data, consisting of URL news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles.
The training took place on a 32 GB Nvidia Tesla V100
- with an initial learning rate of ... | [
"## LuxGPT-2 \n\nGPT-2 model for Text Generation in luxembourgish language, trained on 667 MB of text data, consisting of URL news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles.\nThe training took place on a 32 GB Nvidia Tesla V100\n- with an initial learning... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #luxembourgish #lëtzebuergesch #text generation #lb #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## LuxGPT-2 \n\nGPT-2 model for Text Generation in luxembourgish language, trained... |
question-answering | transformers |
# ESG Question Answering
A NLP service to identify Emission Reduction Targets and Mechanisms of various companies from their ESG disclosure or Annual Reports.
This Roberta-Base model has been finetuned on a very small sample (manually annotated). A lot of companies have clear mentions of targets & goals than methodo... | {} | Ayushb/roberta-base-ft-esg | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T13:18:12+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
|
# ESG Question Answering
A NLP service to identify Emission Reduction Targets and Mechanisms of various companies from their ESG disclosure or Annual Reports.
This Roberta-Base model has been finetuned on a very small sample (manually annotated). A lot of companies have clear mentions of targets & goals than methodo... | [
"# ESG Question Answering\n\nA NLP service to identify Emission Reduction Targets and Mechanisms of various companies from their ESG disclosure or Annual Reports.\n\nThis Roberta-Base model has been finetuned on a very small sample (manually annotated). A lot of companies have clear mentions of targets & goals than... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us \n",
"# ESG Question Answering\n\nA NLP service to identify Emission Reduction Targets and Mechanisms of various companies from their ESG disclosure or Annual Reports.\n\nThis Roberta-Base model has been finetuned on a ver... |
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. -->
# biobert-finetuned-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/b... | {"tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "The widespread circular form of DNA molecules inside cells creates very serious topological problems during replication. Due to the helical structure of the double helix the parental s... | siddharthtumre/biobert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:jnlpba",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T13:25:08+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us
| biobert-finetuned-ner
=====================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the jnlpba dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5113
* Precision: 0.6551
* Recall: 0.7646
* F1: 0.7056
* Accuracy: 0.9108
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
text-generation | transformers | ## LuxGPT-2 based GER
GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an German base model, feature space mappi... | {"language": ["lb"], "license": "mit", "tags": ["luxembourgish", "l\u00ebtzebuergesch", "text generation", "transfer learning"], "model-index": [{"name": "LuxGPT2-basedGER", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Luxembourgish Test Dataset", "type": "Luxembourg... | laurabernardy/LuxGPT2-basedGER | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"luxembourgish",
"lëtzebuergesch",
"text generation",
"transfer learning",
"lb",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T13:41:23+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #luxembourgish #lëtzebuergesch #text generation #transfer learning #lb #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## LuxGPT-2 based GER
GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of URL news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an German base model, feature space mapping ... | [
"## LuxGPT-2 based GER\n\nGPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of URL news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an German base model, feature space m... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #luxembourgish #lëtzebuergesch #text generation #transfer learning #lb #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## LuxGPT-2 based GER\n\nGPT-2 model for Text Generation in luxembourgish la... |
null | null | # House-price-prediction
in this project i want to predict price of a houise with 8 inputs
# Inputs
there is 2 types of 4 image inputs and 4 numerical inputs as image inputs model resive 4 vison of home that contains:
1) bath room
2) bedroom
3) frontal
4) kitchen
and as numerical inputs model recive 4 inputs such n... | {} | alifthi/HousePricePrediction | null | [
"region:us"
] | null | 2022-08-22T14:12:28+00:00 | [] | [] | TAGS
#region-us
| # House-price-prediction
in this project i want to predict price of a houise with 8 inputs
# Inputs
there is 2 types of 4 image inputs and 4 numerical inputs as image inputs model resive 4 vison of home that contains:
1) bath room
2) bedroom
3) frontal
4) kitchen
and as numerical inputs model recive 4 inputs such n... | [
"# House-price-prediction\nin this project i want to predict price of a houise with 8 inputs",
"# Inputs\n\nthere is 2 types of 4 image inputs and 4 numerical inputs as image inputs model resive 4 vison of home that contains:\n\n1) bath room\n2) bedroom\n3) frontal\n4) kitchen\n\nand as numerical inputs model rec... | [
"TAGS\n#region-us \n",
"# House-price-prediction\nin this project i want to predict price of a houise with 8 inputs",
"# Inputs\n\nthere is 2 types of 4 image inputs and 4 numerical inputs as image inputs model resive 4 vison of home that contains:\n\n1) bath room\n2) bedroom\n3) frontal\n4) kitchen\n\nand as n... |
text-generation | transformers |
---
## LuxGPT-2 based GER
GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an English base model, feature space ... | {"language": ["lb"], "license": "mit", "tags": ["luxembourgish", "l\u00ebtzebuergesch", "text generation", "transfer learning"], "model-index": [{"name": "LuxGPT2-basedEN", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Luxembourgish Test Dataset", "type": "Luxembourgi... | laurabernardy/LuxGPT-basedEN | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"luxembourgish",
"lëtzebuergesch",
"text generation",
"transfer learning",
"lb",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T14:22:54+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #luxembourgish #lëtzebuergesch #text generation #transfer learning #lb #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
---
## LuxGPT-2 based GER
GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of URL news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an English base model, feature space map... | [
"## LuxGPT-2 based GER\nGPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of URL news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an English base model, feature space ma... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #luxembourgish #lëtzebuergesch #text generation #transfer learning #lb #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## LuxGPT-2 based GER\nGPT-2 model for Text Generation in luxembourgish lang... |
token-classification | transformers |
## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract model finetuned using the ncbi_disease dataset from the datasets library.
## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will predict lable... | {"language": "en", "license": "cc", "tags": ["named-entity-recognition", "token-classification", "entity_extraction", "multi_class_classification"], "datasets": "ncbi_disease", "metrics": ["precision", "recall", "f1", "accuracy"], "task": ["multi_class_classification", "entity_extraction", "named-entity-recognition", "... | sarahmiller137/BiomedNLP-PubMedBERT-base-uncased-abstract-ft-ncbi-disease | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"named-entity-recognition",
"entity_extraction",
"multi_class_classification",
"en",
"dataset:ncbi_disease",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T14:28:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #named-entity-recognition #entity_extraction #multi_class_classification #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract model finetuned using the ncbi_disease dataset from the datasets library.
## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will predict lable... | [
"## Model information:\nmicrosoft/BiomedNLP-PubMedBERT-base-uncased-abstract model finetuned using the ncbi_disease dataset from the datasets library.",
"## Intended uses:\nThis model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will pred... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #named-entity-recognition #entity_extraction #multi_class_classification #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\nmicrosoft/BiomedNLP-PubMedBERT-base-uncased-abst... |
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. -->
# glue-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "glue-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": [{"type": ... | autoevaluate/glue-mrpc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T14:30:46+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue-mrpc
=========
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3654
* Accuracy: 0.8554
* F1: 0.8998
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
null | espnet |
## ESPnet2 ASR model
### `espnet/brianyan918_aesrc2020_asr_conformer`
This model was trained by Brian Yan using the aesrc2020 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/aesrc2020/asr1
./run.sh --skip_data_prep false --skip_train... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-recognition"], "datasets": ["aesrc2020"]} | espnet/brianyan918_aesrc2020_asr_conformer | null | [
"espnet",
"audio",
"speech-recognition",
"en",
"dataset:aesrc2020",
"license:cc-by-4.0",
"region:us"
] | null | 2022-08-22T14:37:27+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #speech-recognition #en #dataset-aesrc2020 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/brianyan918\_aesrc2020\_asr\_conformer'
This model was trained by Brian Yan using the aesrc2020 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat Aug 20 06:55:57 EDT 2022'
* python version: '3.8.13 (defaul... | [
"### 'espnet/brianyan918\\_aesrc2020\\_asr\\_conformer'\n\n\nThis model was trained by Brian Yan using the aesrc2020 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat Aug 20 06:55:57 EDT 2022'\n* python version: '3.8.13 (default, Mar 28 20... | [
"TAGS\n#espnet #audio #speech-recognition #en #dataset-aesrc2020 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_aesrc2020\\_asr\\_conformer'\n\n\nThis model was trained by Brian Yan using the aesrc2020 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\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. -->
# glue-qqp
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "glue-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "qqp"}, "metrics": [{"type": "a... | autoevaluate/glue-qqp | null | [
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"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T14:40:56+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| glue-qqp
========
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4798
* Accuracy: 0.9033
* F1: 0.8703
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
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-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": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.fr", "s... | hhffxx/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T14:58:44+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-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: 0.6466
* F1: 0.8025
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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library.
## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will pred... | {"language": "en", "license": "cc", "tags": ["named-entity-recognition", "token-classification"], "datasets": "ncbi_disease", "metrics": ["precision", "recall", "f1", "accuracy"], "task": ["named-entity-recognition", "token-classification"], "widget": [{"text": " The risk of cancer, especially lymphoid neoplasias, is s... | sarahmiller137/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ft-ncbi-disease | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"named-entity-recognition",
"en",
"dataset:ncbi_disease",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T15:06:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #named-entity-recognition #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library.
## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will pred... | [
"## Model information:\nmicrosoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library.",
"## Intended uses:\nThis model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #named-entity-recognition #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\nmicrosoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_di... |
question-answering | transformers |
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
```python
from transformers.pipelines import pipeline
model_name = "JAlexis/PruebaBert"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'question': ... | {"language": "en", "widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model... | JAlexis/bert_v2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T15:16:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #endpoints_compatible #region-us
|
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
| [
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #endpoints_compatible #region-us \n",
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] |
zero-shot-classification | transformers | # Model card for mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
## Model description
This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying mDeBERTa-v3-base model was pre-trained by Microsoft on the [... | {"language": ["multilingual", "zh", "ja", "ar", "ko", "de", "fr", "es", "pt", "hi", "id", "it", "tr", "ru", "bn", "ur", "mr", "ta", "vi", "fa", "pl", "uk", "nl", "sv", "he", "sw", "ps"], "license": "mit", "tags": ["zero-shot-classification", "text-classification", "nli", "pytorch"], "datasets": ["MoritzLaurer/multiling... | MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 | null | [
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"deberta-v2",
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"ko",
"de",
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"it",
"tr",
"ru",
"bn",
"ur",
"mr",
"ta",
"vi",
"fa",
"pl",
... | null | 2022-08-22T15:59:35+00:00 | [
"2111.09543",
"2104.07179",
"1809.05053",
"1911.02116"
] | [
"multilingual",
"zh",
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"ar",
"ko",
"de",
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"ur",
"mr",
"ta",
"vi",
"fa",
"pl",
"uk",
"nl",
"sv",
"he",
"sw",
"ps"
] | TAGS
#transformers #pytorch #onnx #safetensors #deberta-v2 #text-classification #zero-shot-classification #nli #multilingual #zh #ja #ar #ko #de #fr #es #pt #hi #id #it #tr #ru #bn #ur #mr #ta #vi #fa #pl #uk #nl #sv #he #sw #ps #dataset-MoritzLaurer/multilingual-NLI-26lang-2mil7 #dataset-xnli #dataset-multi_nli #datas... | Model card for mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
===========================================================
Model description
-----------------
This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. ... | [
"### How to use the model",
"#### Simple zero-shot classification pipeline",
"#### NLI use-case",
"### Training data\n\n\nThis model was trained on the multilingual-nli-26lang-2mil7 dataset and the XNLI validation dataset.\n\n\nThe multilingual-nli-26lang-2mil7 dataset contains 2 730 000 NLI hypothesis-premis... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #deberta-v2 #text-classification #zero-shot-classification #nli #multilingual #zh #ja #ar #ko #de #fr #es #pt #hi #id #it #tr #ru #bn #ur #mr #ta #vi #fa #pl #uk #nl #sv #he #sw #ps #dataset-MoritzLaurer/multilingual-NLI-26lang-2mil7 #dataset-xnli #dataset-multi_nli ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esci-all-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "esci-all-bert-base-uncased", "results": []}]} | spacemanidol/esci-mlm-alllang-bert-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T17:20:57+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# esci-all-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0435
- Accuracy: 0.7740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training an... | [
"# esci-all-bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0435\n- Accuracy: 0.7740",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# esci-all-bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esci-us-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "esci-us-bert-base-uncased", "results": []}]} | spacemanidol/esci-mlm-us-bert-base-uncased | null | [
"transformers",
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"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T17:21:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# esci-us-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1785
- Accuracy: 0.7499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and... | [
"# esci-us-bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1785\n- Accuracy: 0.7499",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# esci-us-bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation s... |
null | diffusers |
This is an unconditioned diffusion model trained on around 500
miscellaneous tiki images from around the web.
The resolution is 64x64.
It was trained for around 4k epochs with a loss around 1.5%.
| {"license": "cc-by-2.0"} | verkaDerkaDerk/tiki-64 | null | [
"diffusers",
"license:cc-by-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-22T17:54:15+00:00 | [] | [] | TAGS
#diffusers #license-cc-by-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
This is an unconditioned diffusion model trained on around 500
miscellaneous tiki images from around the web.
The resolution is 64x64.
It was trained for around 4k epochs with a loss around 1.5%.
!sample tiki images
| [] | [
"TAGS\n#diffusers #license-cc-by-2.0 #has_space #diffusers-DDPMPipeline #region-us \n"
] |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1298049754
- CO2 Emissions (in grams): 428.1193
## Validation Metrics
- Loss: 1.226
- Rouge1: 64.560
- Rouge2: 51.048
- RougeL: 56.712
- RougeLsum: 60.255
- Gen Len: 156.964
## Usage
You can use cURL to access this model:
```
$ curl -X POS... | {"language": ["en"], "tags": ["autotrain", "summarization"], "datasets": ["ckirby/autotrain-data-pat-abst"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 428.11928889922655}} | ckirby/autotrain-pat-abst-1298049754 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
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"en",
"dataset:ckirby/autotrain-data-pat-abst",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T18:37:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #summarization #en #dataset-ckirby/autotrain-data-pat-abst #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1298049754
- CO2 Emissions (in grams): 428.1193
## Validation Metrics
- Loss: 1.226
- Rouge1: 64.560
- Rouge2: 51.048
- RougeL: 56.712
- RougeLsum: 60.255
- Gen Len: 156.964
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1298049754\n- CO2 Emissions (in grams): 428.1193",
"## Validation Metrics\n\n- Loss: 1.226\n- Rouge1: 64.560\n- Rouge2: 51.048\n- RougeL: 56.712\n- RougeLsum: 60.255\n- Gen Len: 156.964",
"## Usage\n\nYou can use cURL to access this ... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #summarization #en #dataset-ckirby/autotrain-data-pat-abst #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1298049754\n- CO2 Emission... |
null | null | # 512x512 Diffusion (Architecture fine-tuned)
## Detailed description
A 512x512 unconditional ImageNet diffusion model, fine-tuned for 900.000 samples from the 512x512 unconditional ImageNet diffusion model. It was fine-tuned using 60.000 images of architecture of the AIDA dataset from Harvard x ArchDaily.
## Config... | {} | jerostephan/Architecture_Diffusion_1.5M | null | [
"region:us"
] | null | 2022-08-22T18:57:12+00:00 | [] | [] | TAGS
#region-us
| # 512x512 Diffusion (Architecture fine-tuned)
## Detailed description
A 512x512 unconditional ImageNet diffusion model, fine-tuned for 900.000 samples from the 512x512 unconditional ImageNet diffusion model. It was fine-tuned using 60.000 images of architecture of the AIDA dataset from Harvard x ArchDaily.
## Config... | [
"# 512x512 Diffusion (Architecture fine-tuned)",
"## Detailed description\n\nA 512x512 unconditional ImageNet diffusion model, fine-tuned for 900.000 samples from the 512x512 unconditional ImageNet diffusion model. It was fine-tuned using 60.000 images of architecture of the AIDA dataset from Harvard x ArchDaily.... | [
"TAGS\n#region-us \n",
"# 512x512 Diffusion (Architecture fine-tuned)",
"## Detailed description\n\nA 512x512 unconditional ImageNet diffusion model, fine-tuned for 900.000 samples from the 512x512 unconditional ImageNet diffusion model. It was fine-tuned using 60.000 images of architecture of the AIDA dataset ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bengali-v8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bengali-v8", "results": []}]} | rashedsafa/wav2vec2-large-xls-r-300m-bengali-v8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T19:26:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-bengali-v8
====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7874
* Wer: 0.6777
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-05\n* tr... |
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. -->
# reviews-classification
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unk... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "reviews-classification", "results": []}]} | Teeto/reviews-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T19:35:15+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reviews-classification
======================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5442
* Accuracy: 0.875
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
text-generation | transformers |
# Review Training Bot
This model was trained for the purpose of generating scores and reviews for any given movie. It is fine-tuned on distilgpt2 as a baseline and trained on a custom dataset created by scraping around 120k letterboxd reviews. The current state of the model can get the correct formatting reliably but... | {"license": "cc", "widget": [{"text": "Movie: Parasite Score:", "example_title": "Parasite"}, {"text": "Movie: Come and See Score:", "example_title": "Come and See"}, {"text": "Movie: Harakiri Score:", "example_title": "Harakiri"}]} | uripper/AVA | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T19:54:37+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #license-cc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Review Training Bot
This model was trained for the purpose of generating scores and reviews for any given movie. It is fine-tuned on distilgpt2 as a baseline and trained on a custom dataset created by scraping around 120k letterboxd reviews. The current state of the model can get the correct formatting reliably but... | [
"# Review Training Bot\n\nThis model was trained for the purpose of generating scores and reviews for any given movie. It is fine-tuned on distilgpt2 as a baseline and trained on a custom dataset created by scraping around 120k letterboxd reviews. The current state of the model can get the correct formatting reliab... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #license-cc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Review Training Bot\n\nThis model was trained for the purpose of generating scores and reviews for any given movie. It is fine-tuned on... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-100h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Pa... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | saahith/wav2vec2_base_100h_ngram | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T20:42:48+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Base-100h
==================
Facebook's Wav2Vec2
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Au... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-ja
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-ja", "results": []}]} | VanHoan/mt5-small-finetuned-amazon-en-ja | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-22T22:44:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-ja
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2749
* Rouge1: 16.6603
* Rouge2: 8.1096
* Rougel: 16.0117
* Rougelsum: 16.1001
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #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*... |
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. -->
# tokens
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "tokens", "results": []}]} | kalmuraee/tokens | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T23:35:06+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| tokens
======
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9811
* Wer: 0.4608
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
image-classification | transformers |
# brand-detector
Brand detector is a particular case of image classification, since these may contain only text, images, or a combination of both.
In this work, we trained a system for the brand classification of shoes available at https://www.shooos.com/.
The method allows obtaining the most similar brands on the... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | rONIVALDO/brand-detector | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-22T23:57:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# brand-detector
Brand detector is a particular case of image classification, since these may contain only text, images, or a combination of both.
In this work, we trained a system for the brand classification of shoes available at URL
The method allows obtaining the most similar brands on the basis of their shape... | [
"# brand-detector\n\nBrand detector is a particular case of image classification, since these may contain only text, images, or a combination of both. \n\nIn this work, we trained a system for the brand classification of shoes available at URL\n\nThe method allows obtaining the most similar brands on the basis of t... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# brand-detector\n\nBrand detector is a particular case of image classification, since these may contain only text, images, or a combination of both. \n\nIn ... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | sureshchinta/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T00:13:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
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: 0.2562
* Accuracy: 0.9869
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 3e-05\n* train\\_batch\\_size: 32\n* eval... |
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-vee-demo-colab
This model is a fine-tuned version of [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/aire... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model_index": {"name": "wav2vec2-vee-demo-colab"}} | KISSz/wav2vec2-vee-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T01:38:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# wav2vec2-vee-demo-colab
This model is a fine-tuned version of airesearch/wav2vec2-large-xlsr-53-th on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# wav2vec2-vee-demo-colab\n\nThis model is a fine-tuned version of airesearch/wav2vec2-large-xlsr-53-th on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# wav2vec2-vee-demo-colab\n\nThis model is a fine-tuned version of airesearch/wav2vec2-large-xlsr-53-th on an unkown dataset.",
"## Model descri... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-50-many-to-many-mmt-finetuned-ar-to-en
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-m... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mbart-large-50-many-to-many-mmt-finetuned-acw-to-en", "results": []}]} | Shamus/mbart-large-50-many-to-many-mmt-finetuned-acw-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-23T01:45:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-50-many-to-many-mmt-finetuned-ar-to-en
==================================================
This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5204
* Bleu: 34.8213
* Gen Len: 33.544
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\... |
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