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text-classification | transformers | # bcms-bertic-parlasent-bcs-ter
Ternary text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the BCS Political Sentiment dataset (sentence-level data).
This classifier classifies text into only three categories: Negative, Neutral, and Positive. For t... | {"language": "hr", "tags": ["text-classification", "sentiment-analysis"], "widget": [{"text": "Po\u0161tovani potpredsjedni\u010dke Vlade i ministre hrvatskih branitelja, mislite li da ste zapravo iznevjerili svoje suborce s kojima ste 555 dana prosvjedovali u \u0161atoru protiv tada\u0161njih du\u017enosnika jer ste z... | classla/bcms-bertic-parlasent-bcs-ter | null | [
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"electra",
"text-classification",
"sentiment-analysis",
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"autotrain_compatible",
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"region:us"
] | null | 2022-06-01T08:00:25+00:00 | [
"2206.00929"
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"hr"
] | TAGS
#transformers #pytorch #safetensors #electra #text-classification #sentiment-analysis #hr #arxiv-2206.00929 #autotrain_compatible #endpoints_compatible #region-us
| bcms-bertic-parlasent-bcs-ter
=============================
Ternary text classification model based on 'classla/bcms-bertic' and fine-tuned on the BCS Political Sentiment dataset (sentence-level data).
This classifier classifies text into only three categories: Negative, Neutral, and Positive. For the binary classi... | [] | [
"TAGS\n#transformers #pytorch #safetensors #electra #text-classification #sentiment-analysis #hr #arxiv-2206.00929 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # SSCI-BERT: A pretrained language model for social scientific text
## Introduction
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a ... | {"license": "apache-2.0"} | KM4STfulltext/SSCI-BERT-e4 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T08:01:58+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SSCI-BERT: A pretrained language model for social scientific text
=================================================================
Introduction
------------
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy ... | [
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-SciBERT",
"### Download Models\n\n\n* The version of the model we provide is 'PyTorch'.",
"### From Huggingface\n\n\n* Downlo... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-... |
fill-mask | transformers | # SSCI-BERT: A pretrained language model for social scientific text
## Introduction
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a ... | {"license": "apache-2.0"} | KM4STfulltext/SSCI-SciBERT-e2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T08:03:32+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SSCI-BERT: A pretrained language model for social scientific text
=================================================================
Introduction
------------
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy ... | [
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-SciBERT",
"### Download Models\n\n\n* The version of the model we provide is 'PyTorch'.",
"### From Huggingface\n\n\n* Downlo... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-... |
fill-mask | transformers | # SSCI-BERT: A pretrained language model for social scientific text
## Introduction
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a ... | {"license": "apache-2.0"} | KM4STfulltext/SSCI-SciBERT-e4 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T08:05:25+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SSCI-BERT: A pretrained language model for social scientific text
=================================================================
Introduction
------------
The research for social science texts needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy ... | [
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-SciBERT",
"### Download Models\n\n\n* The version of the model we provide is 'PyTorch'.",
"### From Huggingface\n\n\n* Downlo... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain SSCI-BERT and SSCI-SciBERT models online.\n\n\n* SSCI-BERT\n* SSCI-... |
text-classification | transformers | # bcms-bertic-parlasent-bcs-bi
Binary text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the BCS Political Sentiment dataset (sentence-level data).
This classifier classifies text into only two categories: Negative vs. Other. For the ternary class... | {"language": "hr", "tags": ["text-classification", "sentiment-analysis"], "widget": [{"text": "Po\u0161tovani potpredsjedni\u010dke Vlade i ministre hrvatskih branitelja, mislite li da ste zapravo iznevjerili svoje suborce s kojima ste 555 dana prosvjedovali u \u0161atoru protiv tada\u0161njih du\u017enosnika jer ste z... | classla/bcms-bertic-parlasent-bcs-bi | null | [
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"electra",
"text-classification",
"sentiment-analysis",
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"arxiv:2206.00929",
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"region:us"
] | null | 2022-06-01T08:10:17+00:00 | [
"2206.00929"
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"hr"
] | TAGS
#transformers #pytorch #electra #text-classification #sentiment-analysis #hr #arxiv-2206.00929 #autotrain_compatible #endpoints_compatible #region-us
| bcms-bertic-parlasent-bcs-bi
============================
Binary text classification model based on 'classla/bcms-bertic' and fine-tuned on the BCS Political Sentiment dataset (sentence-level data).
This classifier classifies text into only two categories: Negative vs. Other. For the ternary classifier (Negative, N... | [] | [
"TAGS\n#transformers #pytorch #electra #text-classification #sentiment-analysis #hr #arxiv-2206.00929 #autotrain_compatible #endpoints_compatible #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-large-xlsr-53-tr-fine-tuning-02
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-tr-fine-tuning-deprecated", "results": []}]} | bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning-deprecated | null | [
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"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T08:50:57+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-53-tr-fine-tuning-02
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
| [
"# wav2vec2-large-xlsr-53-tr-fine-tuning-02\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset."
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"# wav2vec2-large-xlsr-53-tr-fine-tuning-02\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dat... |
null | null | This is Hindi ASR model finetuned on facebook wav2vec2-large-xls-r-300m model. | {} | pravesh/wav2vec2-large-xls-r-300m-hindi-v2 | null | [
"region:us"
] | null | 2022-06-01T09:11:49+00:00 | [] | [] | TAGS
#region-us
| This is Hindi ASR model finetuned on facebook wav2vec2-large-xls-r-300m model. | [] | [
"TAGS\n#region-us \n"
] |
null | keras | # BERT-Tiny fine-tuned on on sms_spam dataset for spam detection
Validation accuracy: **0.98** | {"language": "en", "tags": ["spam"], "widget": [{"text": "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days."}]} | duddaladeepak/test | null | [
"keras",
"spam",
"en",
"region:us"
] | null | 2022-06-01T09:15:07+00:00 | [] | [
"en"
] | TAGS
#keras #spam #en #region-us
| # BERT-Tiny fine-tuned on on sms_spam dataset for spam detection
Validation accuracy: 0.98 | [
"# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection\n\nValidation accuracy: 0.98"
] | [
"TAGS\n#keras #spam #en #region-us \n",
"# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection\n\nValidation accuracy: 0.98"
] |
fill-mask | transformers |
# Catalan BERTa-v2 (roberta-base-ca-v2) base model
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Trai... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "masked-lm", "RoBERTa-base-ca-v2", "CaText", "Catalan Textual Corpus"], "widget": [{"text": "El Catal\u00e0 \u00e9s una llengua molt <mask>."}, {"text": "Salvador Dal\u00ed va viure a <mask>."}, {"text": "La Costa Brava t\u00e9 les millors <mask> d'Espan... | projecte-aina/roberta-base-ca-v2 | null | [
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"ca",
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"region:us"
] | null | 2022-06-01T09:15:34+00:00 | [] | [
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] | TAGS
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| Catalan BERTa-v2 (roberta-base-ca-v2) base model
================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ Training procedure
* Evaluation
+ CLUB benchm... | [
"### Training data\n\n\nThe training corpus consists of several corpora gathered from web crawling and public corpora.",
"### Training procedure\n\n\nThe training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE)\nused in the original RoBERTA model with a vocabulary size of 50,262 tokens.... | [
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"### Training data\n\n\nThe training corpus consists of several corpora gathered from web crawlin... |
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. -->
# wav2vec2-19
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-19", "results": []}]} | chrisvinsen/wav2vec2-19 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T09:35:47+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-19
===========
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: 0.6305
* Wer: 0.4499
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: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_b... |
image-classification | transformers |
# LeViT
LeViT-384 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/levit-384 | null | [
"transformers",
"pytorch",
"levit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.01136",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T10:27:30+00:00 | [
"2104.01136"
] | [] | TAGS
#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# LeViT
LeViT-384 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
by Graham et al. and first released in this repository.
Disclaimer: The team releasing LeViT did not write a model card for this model so t... | [
"# LeViT\n\nLeViT-384 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference\n by Graham et al. and first released in this repository. \n\nDisclaimer: The team releasing LeViT did not write a model card for this m... | [
"TAGS\n#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# LeViT\n\nLeViT-384 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Tra... |
image-classification | transformers |
# LeViT
LeViT-256 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/levit-256 | null | [
"transformers",
"pytorch",
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"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.01136",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T10:27:41+00:00 | [
"2104.01136"
] | [] | TAGS
#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# LeViT
LeViT-256 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
by Graham et al. and first released in this repository.
Disclaimer: The team releasing LeViT did not write a model card for this model so t... | [
"# LeViT\n\nLeViT-256 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference\n by Graham et al. and first released in this repository. \n\nDisclaimer: The team releasing LeViT did not write a model card for this m... | [
"TAGS\n#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# LeViT\n\nLeViT-256 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Tra... |
image-classification | transformers |
# LeViT
LeViT-192 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/levit-192 | null | [
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"levit",
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"vision",
"dataset:imagenet-1k",
"arxiv:2104.01136",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T10:27:51+00:00 | [
"2104.01136"
] | [] | TAGS
#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# LeViT
LeViT-192 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
by Graham et al. and first released in this repository.
Disclaimer: The team releasing LeViT did not write a model card for this model so t... | [
"# LeViT\n\nLeViT-192 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference\n by Graham et al. and first released in this repository. \n\nDisclaimer: The team releasing LeViT did not write a model card for this m... | [
"TAGS\n#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# LeViT\n\nLeViT-192 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Tra... |
image-classification | transformers |
# LeViT
LeViT-128 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/levit-128 | null | [
"transformers",
"pytorch",
"levit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.01136",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T10:27:59+00:00 | [
"2104.01136"
] | [] | TAGS
#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# LeViT
LeViT-128 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
by Graham et al. and first released in this repository.
Disclaimer: The team releasing LeViT did not write a model card for this model so t... | [
"# LeViT\n\nLeViT-128 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference\n by Graham et al. and first released in this repository. \n\nDisclaimer: The team releasing LeViT did not write a model card for this m... | [
"TAGS\n#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# LeViT\n\nLeViT-128 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Tra... |
image-classification | transformers |
# LeViT
LeViT-128S model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
D... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/levit-128S | null | [
"transformers",
"pytorch",
"levit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.01136",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-01T10:28:11+00:00 | [
"2104.01136"
] | [] | TAGS
#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LeViT
LeViT-128S model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
by Graham et al. and first released in this repository.
Disclaimer: The team releasing LeViT did not write a model card for this model so ... | [
"# LeViT\n\nLeViT-128S model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference\n by Graham et al. and first released in this repository. \n\nDisclaimer: The team releasing LeViT did not write a model card for this ... | [
"TAGS\n#transformers #pytorch #levit #image-classification #vision #dataset-imagenet-1k #arxiv-2104.01136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LeViT\n\nLeViT-128S model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper LeViT: ... |
text-generation | transformers |
# BERTIN-GPT-J-6B with 8-bit weights (Quantized)
### Go [here](https://huggingface.co/mrm8488/bertin-gpt-j-6B-ES-v1-8bit) to use the latest checkpoint.
This model (and model card) is an adaptation of [hivemind/gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit), so all credits to him/her.
This is a versi... | {"language": "es", "license": "wtfpl", "tags": ["gpt-j", "spanish", "LLM", "gpt-j-6b"]} | mrm8488/bertin-gpt-j-6B-ES-8bit | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"gpt-j",
"spanish",
"LLM",
"gpt-j-6b",
"es",
"arxiv:2106.09685",
"arxiv:2110.02861",
"license:wtfpl",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-01T10:30:01+00:00 | [
"2106.09685",
"2110.02861"
] | [
"es"
] | TAGS
#transformers #pytorch #gptj #text-generation #gpt-j #spanish #LLM #gpt-j-6b #es #arxiv-2106.09685 #arxiv-2110.02861 #license-wtfpl #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERTIN-GPT-J-6B with 8-bit weights (Quantized)
### Go here to use the latest checkpoint.
This model (and model card) is an adaptation of hivemind/gpt-j-6B-8bit, so all credits to him/her.
This is a version of bertin-project/bertin-gpt-j-6B that is modified so you can generate and fine-tune the model in colab or ... | [
"# BERTIN-GPT-J-6B with 8-bit weights (Quantized)",
"### Go here to use the latest checkpoint. \n\nThis model (and model card) is an adaptation of hivemind/gpt-j-6B-8bit, so all credits to him/her.\n\nThis is a version of bertin-project/bertin-gpt-j-6B that is modified so you can generate and fine-tune the model ... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #gpt-j #spanish #LLM #gpt-j-6b #es #arxiv-2106.09685 #arxiv-2110.02861 #license-wtfpl #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERTIN-GPT-J-6B with 8-bit weights (Quantized)",
"### Go here to use the latest checkpoint. \n\nThi... |
text2text-generation | transformers |
This model is a fine-tune checkpoint of [T5-base](https://huggingface.co/t5-base), fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral poi... | {"language": ["en"], "license": "apache-2.0", "datasets": ["WNC"], "metrics": ["accuracy"]} | erickfm/t5-base-finetuned-bias | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WNC",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T10:30:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-WNC #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model is a fine-tune checkpoint of T5-base, fine-tuned on the Wiki Neutrality Corpus (WNC), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral point of view”. This model reaches an accuracy of 0.39 on a dev split of the WNC.
For... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-WNC #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# En-Tn
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-tn](https://huggingface.co/Helsinki-NLP/opus-mt-en-tn) on t... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Tn", "results": []}]} | kabelomalapane/En-Tn | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T10:35:03+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# En-Tn
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-tn on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6174
- Bleu: 32.2889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation da... | [
"# En-Tn\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-tn on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6174\n- Bleu: 32.2889",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# En-Tn\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-tn on the None dataset.\nIt achieves the following results on t... |
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-Hindi-colab-v4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-Hindi-colab-v4", "results": []}]} | pravesh/wav2vec2-large-xls-r-300m-Hindi-colab-v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T10:39:09+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-Hindi-colab-v4
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# wav2vec2-large-xls-r-300m-Hindi-colab-v4\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-Hindi-colab-v4\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jayeshgar/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | jayeshgar/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-01T10:40:28+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jayeshgar/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/... | jayeshgar/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-01T10:51:00+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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"]} | Sundhar/bart_customized | null | [
"fastai",
"region:us"
] | null | 2022-06-01T11:18:33+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. -->
# bert-base-uncased-finetuned-filtered-0601
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-filtered-0601", "results": []}]} | YeRyeongLee/bert-base-uncased-finetuned-filtered-0601 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T11:22:30+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-filtered-0601
=========================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1152
* Accuracy: 0.9814
* F1: 0.9815
Model description
-----------------
More info... | [
"### 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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
question-answering | transformers |
# squad_it_xxl_cased
This is a model, based on **BERT** trained on cased Italian, that can be used for [Extractive Q&A](https://huggingface.co/tasks/question-answering) on Italian texts.
## Model description
This model has been trained on **squad_it** dataset starting from the pre-trained model [dbmdz/bert-base-itali... | {"language": ["it"], "tags": ["Q&A"], "datasets": ["squad_it"], "metrics": ["type squad"], "widget": [{"text": "Come si chiama il primo re di Roma?", "context": "Roma \u00e8 una delle pi\u00f9 belle ed antiche citt\u00e0 del mondo. Il pi\u00f9 famoso monumento di Roma \u00e8 il Colosseo. Un altro monumento molto bello ... | luigisaetta/squad_it_xxl_cased_hub1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"Q&A",
"it",
"dataset:squad_it",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-01T11:50:01+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #bert #question-answering #Q&A #it #dataset-squad_it #endpoints_compatible #has_space #region-us
|
# squad_it_xxl_cased
This is a model, based on BERT trained on cased Italian, that can be used for Extractive Q&A on Italian texts.
## Model description
This model has been trained on squad_it dataset starting from the pre-trained model dbmdz/bert-base-italian-xxl-cased.
These are the metrics computed on evaluation ... | [
"# squad_it_xxl_cased\nThis is a model, based on BERT trained on cased Italian, that can be used for Extractive Q&A on Italian texts.",
"## Model description\nThis model has been trained on squad_it dataset starting from the pre-trained model dbmdz/bert-base-italian-xxl-cased.\n\nThese are the metrics computed on... | [
"TAGS\n#transformers #pytorch #bert #question-answering #Q&A #it #dataset-squad_it #endpoints_compatible #has_space #region-us \n",
"# squad_it_xxl_cased\nThis is a model, based on BERT trained on cased Italian, that can be used for Extractive Q&A on Italian texts.",
"## Model description\nThis model has been t... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474
## Validation Metrics
- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377... | {"language": "unk", "tags": "autotrain", "datasets": ["cjbarrie/autotrain-data-masress-medcrit-binary-5"], "widget": [{"text": "\u0627\u0644\u0643\u0644 \u064a\u0646\u062a\u0642\u062f \u0627\u0644\u0631\u0626\u064a\u0633 \u0639\u0644\u0649 \u0625\u062e\u0641\u0627\u0642\u0627\u062a\u0647"}], "co2_eq_emissions": 0.01017... | cjbarrie/masress-medcrit-camel | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:cjbarrie/autotrain-data-masress-medcrit-binary-5",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T11:56:34+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-cjbarrie/autotrain-data-masress-medcrit-binary-5 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474
## Validation Metrics
- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 937130980\n- CO2 Emissions (in grams): 0.01017487638098474",
"## Validation Metrics\n\n- Loss: 0.757265031337738\n- Accuracy: 0.7551020408163265\n- Macro F1: 0.7202470830473576\n- Micro F1: 0.7551020408163265\n- Weighted F... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-cjbarrie/autotrain-data-masress-medcrit-binary-5 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 937130980\n- ... |
null | null | my name is Emiliano
| {} | emivalenti/1 | null | [
"region:us"
] | null | 2022-06-01T12:14:22+00:00 | [] | [] | TAGS
#region-us
| my name is Emiliano
| [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | lorenzkuhn/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T12:15:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3206
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bishmoy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | bishmoy/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-01T12:42:50+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **CartPoleNoVel-v1**
This is a trained model of a **RecurrentPPO** agent playing **CartPoleNoVel-v1**
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 fo... | {"library_name": "stable-baselines3", "tags": ["CartPoleNoVel-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPoleNoVel-v1", "t... | sb3/ppo_lstm-CartPoleNoVel-v1 | null | [
"stable-baselines3",
"CartPoleNoVel-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-01T12:45:08+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPoleNoVel-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing CartPoleNoVel-v1
This is a trained model of a RecurrentPPO agent playing CartPoleNoVel-v1
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 inc... | [
"# RecurrentPPO Agent playing CartPoleNoVel-v1\nThis is a trained model of a RecurrentPPO agent playing CartPoleNoVel-v1\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... | [
"TAGS\n#stable-baselines3 #CartPoleNoVel-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing CartPoleNoVel-v1\nThis is a trained model of a RecurrentPPO agent playing CartPoleNoVel-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="bishmoy/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | bishmoy/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-01T12:45:38+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **PendulumNoVel-v1**
This is a trained model of a **RecurrentPPO** agent playing **PendulumNoVel-v1**
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 fo... | {"library_name": "stable-baselines3", "tags": ["PendulumNoVel-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PendulumNoVel-v1", "t... | sb3/ppo_lstm-PendulumNoVel-v1 | null | [
"stable-baselines3",
"PendulumNoVel-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-01T12:49:13+00:00 | [] | [] | TAGS
#stable-baselines3 #PendulumNoVel-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing PendulumNoVel-v1
This is a trained model of a RecurrentPPO agent playing PendulumNoVel-v1
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 inc... | [
"# RecurrentPPO Agent playing PendulumNoVel-v1\nThis is a trained model of a RecurrentPPO agent playing PendulumNoVel-v1\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... | [
"TAGS\n#stable-baselines3 #PendulumNoVel-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing PendulumNoVel-v1\nThis is a trained model of a RecurrentPPO agent playing PendulumNoVel-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-filtered-0602
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-filtered-0602", "results": []}]} | YeRyeongLee/bert-base-uncased-finetuned-filtered-0602 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T13:09:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-filtered-0602
=========================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1959
* Accuracy: 0.9783
* F1: 0.9783
Model description
-----------------
More info... | [
"### 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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **MountainCarContinuousNoVel-v0**
This is a trained model of a **RecurrentPPO** agent playing **MountainCarContinuousNoVel-v0**
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 ... | {"library_name": "stable-baselines3", "tags": ["MountainCarContinuousNoVel-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Mountain... | sb3/ppo_lstm-MountainCarContinuousNoVel-v0 | null | [
"stable-baselines3",
"MountainCarContinuousNoVel-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-01T13:29:56+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCarContinuousNoVel-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing MountainCarContinuousNoVel-v0
This is a trained model of a RecurrentPPO agent playing MountainCarContinuousNoVel-v0
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 ... | [
"# RecurrentPPO Agent playing MountainCarContinuousNoVel-v0\nThis is a trained model of a RecurrentPPO agent playing MountainCarContinuousNoVel-v0\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 op... | [
"TAGS\n#stable-baselines3 #MountainCarContinuousNoVel-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing MountainCarContinuousNoVel-v0\nThis is a trained model of a RecurrentPPO agent playing MountainCarContinuousNoVel-v0\nusing the stable-baselines3... |
image-segmentation | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras", "tags": ["image-segmentation"]} | lmazzon70/deeplab-v3 | null | [
"keras",
"tensorboard",
"image-segmentation",
"region:us"
] | null | 2022-06-01T13:45:13+00:00 | [] | [] | TAGS
#keras #tensorboard #image-segmentation #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #tensorboard #image-segmentation #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\... |
feature-extraction | transformers | ERROR: type should be string, got "\nhttps://github.com/BM-K/Sentence-Embedding-is-all-you-need\n\n# Korean-Sentence-Embedding\n🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.\n\n## Quick tour\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\ndef cal_score(a, b):\n if len(a.shape) == 1: a = a.unsqueeze(0)\n if len(b.shape) == 1: b = b.unsqueeze(0)\n\n a_norm = a / a.norm(dim=1)[:, None]\n b_norm = b / b.norm(dim=1)[:, None]\n return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100\n\nmodel = AutoModel.from_pretrained('BM-K/KoSimCSE-bert-multitask') \nAutoTokenizer.from_pretrained('BM-K/KoSimCSE-bert-multitask')\n\nsentences = ['치타가 들판을 가로 질러 먹이를 쫓는다.',\n '치타 한 마리가 먹이 뒤에서 달리고 있다.',\n '원숭이 한 마리가 드럼을 연주한다.']\n\ninputs = tokenizer(sentences, padding=True, truncation=True, return_tensors=\"pt\")\nembeddings, _ = model(**inputs, return_dict=False)\n\nscore01 = cal_score(embeddings[0][0], embeddings[1][0])\nscore02 = cal_score(embeddings[0][0], embeddings[2][0])\n```\n\n## Performance\n- Semantic Textual Similarity test set results <br>\n\n| Model | AVG | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |\n|------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n| KoSBERT<sup>†</sup><sub>SKT</sub> | 77.40 | 78.81 | 78.47 | 77.68 | 77.78 | 77.71 | 77.83 | 75.75 | 75.22 |\n| KoSBERT | 80.39 | 82.13 | 82.25 | 80.67 | 80.75 | 80.69 | 80.78 | 77.96 | 77.90 |\n| KoSRoBERTa | 81.64 | 81.20 | 82.20 | 81.79 | 82.34 | 81.59 | 82.20 | 80.62 | 81.25 |\n| | | | | | | | | |\n| KoSentenceBART | 77.14 | 79.71 | 78.74 | 78.42 | 78.02 | 78.40 | 78.00 | 74.24 | 72.15 |\n| KoSentenceT5 | 77.83 | 80.87 | 79.74 | 80.24 | 79.36 | 80.19 | 79.27 | 72.81 | 70.17 |\n| | | | | | | | | |\n| KoSimCSE-BERT<sup>†</sup><sub>SKT</sub> | 81.32 | 82.12 | 82.56 | 81.84 | 81.63 | 81.99 | 81.74 | 79.55 | 79.19 |\n| KoSimCSE-BERT | 83.37 | 83.22 | 83.58 | 83.24 | 83.60 | 83.15 | 83.54 | 83.13 | 83.49 |\n| KoSimCSE-RoBERTa | 83.65 | 83.60 | 83.77 | 83.54 | 83.76 | 83.55 | 83.77 | 83.55 | 83.64 |\n| | | | | | | | | | |\n| KoSimCSE-BERT-multitask | 85.71 | 85.29 | 86.02 | 85.63 | 86.01 | 85.57 | 85.97 | 85.26 | 85.93 |\n| KoSimCSE-RoBERTa-multitask | 85.77 | 85.08 | 86.12 | 85.84 | 86.12 | 85.83 | 86.12 | 85.03 | 85.99 |" | {"language": "ko", "tags": ["korean"]} | BM-K/KoSimCSE-bert-multitask | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"korean",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T13:51:47+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #korean #ko #endpoints_compatible #region-us
| URL
Korean-Sentence-Embedding
=========================
Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
Quick tour
----------
Performance
-----------
* Semantic Textual Similarity test set... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #korean #ko #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ERROR: type should be string, got "\nhttps://github.com/BM-K/Sentence-Embedding-is-all-you-need\n\n# Korean-Sentence-Embedding\n🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.\n\n## Quick tour\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\ndef cal_score(a, b):\n if len(a.shape) == 1: a = a.unsqueeze(0)\n if len(b.shape) == 1: b = b.unsqueeze(0)\n\n a_norm = a / a.norm(dim=1)[:, None]\n b_norm = b / b.norm(dim=1)[:, None]\n return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100\n\nmodel = AutoModel.from_pretrained('BM-K/KoSimCSE-roberta-multitask') \nAutoTokenizer.from_pretrained('BM-K/KoSimCSE-roberta-multitask')\n\nsentences = ['치타가 들판을 가로 질러 먹이를 쫓는다.',\n '치타 한 마리가 먹이 뒤에서 달리고 있다.',\n '원숭이 한 마리가 드럼을 연주한다.']\n\ninputs = tokenizer(sentences, padding=True, truncation=True, return_tensors=\"pt\")\nembeddings, _ = model(**inputs, return_dict=False)\n\nscore01 = cal_score(embeddings[0][0], embeddings[1][0])\nscore02 = cal_score(embeddings[0][0], embeddings[2][0])\n```\n\n## Performance\n- Semantic Textual Similarity test set results <br>\n\n| Model | AVG | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |\n|------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n| KoSBERT<sup>†</sup><sub>SKT</sub> | 77.40 | 78.81 | 78.47 | 77.68 | 77.78 | 77.71 | 77.83 | 75.75 | 75.22 |\n| KoSBERT | 80.39 | 82.13 | 82.25 | 80.67 | 80.75 | 80.69 | 80.78 | 77.96 | 77.90 |\n| KoSRoBERTa | 81.64 | 81.20 | 82.20 | 81.79 | 82.34 | 81.59 | 82.20 | 80.62 | 81.25 |\n| | | | | | | | | |\n| KoSentenceBART | 77.14 | 79.71 | 78.74 | 78.42 | 78.02 | 78.40 | 78.00 | 74.24 | 72.15 |\n| KoSentenceT5 | 77.83 | 80.87 | 79.74 | 80.24 | 79.36 | 80.19 | 79.27 | 72.81 | 70.17 |\n| | | | | | | | | |\n| KoSimCSE-BERT<sup>†</sup><sub>SKT</sub> | 81.32 | 82.12 | 82.56 | 81.84 | 81.63 | 81.99 | 81.74 | 79.55 | 79.19 |\n| KoSimCSE-BERT | 83.37 | 83.22 | 83.58 | 83.24 | 83.60 | 83.15 | 83.54 | 83.13 | 83.49 |\n| KoSimCSE-RoBERTa | 83.65 | 83.60 | 83.77 | 83.54 | 83.76 | 83.55 | 83.77 | 83.55 | 83.64 |\n| | | | | | | | | | |\n| KoSimCSE-BERT-multitask | 85.71 | 85.29 | 86.02 | 85.63 | 86.01 | 85.57 | 85.97 | 85.26 | 85.93 |\n| KoSimCSE-RoBERTa-multitask | 85.77 | 85.08 | 86.12 | 85.84 | 86.12 | 85.83 | 86.12 | 85.03 | 85.99 |" | {"language": "ko", "tags": ["korean"]} | BM-K/KoSimCSE-roberta-multitask | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"korean",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T14:02:22+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #roberta #feature-extraction #korean #ko #endpoints_compatible #region-us
| URL
Korean-Sentence-Embedding
=========================
Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
Quick tour
----------
Performance
-----------
* Semantic Textual Similarity test set... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #feature-extraction #korean #ko #endpoints_compatible #region-us \n"
] |
tabular-regression | keras |
## Timeseries anomaly detection using an Autoencoder
This repo contains the model and the notebook to this [Keras example on Timeseries anomaly detection using an Autoencoder.](https://keras.io/examples/timeseries/timeseries_anomaly_detection/)
Full credits to: [Pavithra Vijay](https://github.com/pavithrasv)
## Ba... | {"library_name": "keras", "tags": ["tabular-regression", "time-series", "anomaly-detection"]} | keras-io/timeseries-anomaly-detection | null | [
"keras",
"tensorboard",
"tabular-regression",
"time-series",
"anomaly-detection",
"has_space",
"region:us"
] | null | 2022-06-01T14:19:26+00:00 | [] | [] | TAGS
#keras #tensorboard #tabular-regression #time-series #anomaly-detection #has_space #region-us
| Timeseries anomaly detection using an Autoencoder
-------------------------------------------------
This repo contains the model and the notebook to this Keras example on Timeseries anomaly detection using an Autoencoder.
Full credits to: Pavithra Vijay
Background and Datasets
-----------------------
This scrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #tensorboard #tabular-regression #time-series #anomaly-detection #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsi... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_... | {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | Alian3785/TEST2ppo-BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-01T14:32:38+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: A... |
text2text-generation | transformers |
# REBEL-ru
Based on russian part of wikipedia (scrapped with CROCODILE).
Model trained for 3 epochs on russian ruT5-base
# How to use
Same code as REBEL-large (https://huggingface.co/Babelscape/rebel-large)
```
text = '''За последние 9 месяцев инвесторы в азиатские долларовые долговые обязательства потеряли 155 ми... | {"language": ["ru"], "license": "apache-2.0", "tags": ["seq2seq", "relation-extraction", "t5"], "datasets": ["memyprokotow/rebel-dataset-rus"], "widget": [{"text": "\u0417\u0430 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 9 \u043c\u0435\u0441\u044f\u0446\u0435\u0432 \u0438\u043d\u0432\u0435\u0441\u0442\u043e... | memyprokotow/rut5-REBEL-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"relation-extraction",
"ru",
"dataset:memyprokotow/rebel-dataset-rus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T14:52:53+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #relation-extraction #ru #dataset-memyprokotow/rebel-dataset-rus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# REBEL-ru
Based on russian part of wikipedia (scrapped with CROCODILE).
Model trained for 3 epochs on russian ruT5-base
# How to use
Same code as REBEL-large (URL
| [
"# REBEL-ru\nBased on russian part of wikipedia (scrapped with CROCODILE). \nModel trained for 3 epochs on russian ruT5-base",
"# How to use\nSame code as REBEL-large (URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #relation-extraction #ru #dataset-memyprokotow/rebel-dataset-rus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# REBEL-ru\nBased on russian part of wikipedia (scrapped with CROCODILE). \n... |
translation | transformers |
### zho-eng
* source group: Chinese
* target group: English
* OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md)
* model: transformer
* source language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
* target l... | {"language": ["zh", "en"], "license": "apache-2.0", "tags": ["translation"]} | osanseviero/my-helsinki-duplicate | null | [
"transformers",
"pytorch",
"rust",
"marian",
"text2text-generation",
"translation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T14:56:44+00:00 | [] | [
"zh",
"en"
] | TAGS
#transformers #pytorch #rust #marian #text2text-generation #translation #zh #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### zho-eng
* source group: Chinese
* target group: English
* OPUS readme: zho-eng
* model: transformer
* source language(s): cjy\_Hans cjy\_Hant cmn cmn\_Hans cmn\_Hant gan lzh lzh\_Hans nan wuu yue yue\_Hans yue\_Hant
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (sp... | [
"### zho-eng\n\n\n* source group: Chinese\n* target group: English\n* OPUS readme: zho-eng\n* model: transformer\n* source language(s): cjy\\_Hans cjy\\_Hant cmn cmn\\_Hans cmn\\_Hant gan lzh lzh\\_Hans nan wuu yue yue\\_Hans yue\\_Hant\n* target language(s): eng\n* model: transformer\n* pre-processing: normalizati... | [
"TAGS\n#transformers #pytorch #rust #marian #text2text-generation #translation #zh #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### zho-eng\n\n\n* source group: Chinese\n* target group: English\n* OPUS readme: zho-eng\n* model: transformer\n* source language(s): cjy\\_Hans ... |
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. -->
# MiniLMv2-L12-H384-distilled-finetuned-spam-detection
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled... | {"tags": ["generated_from_trainer"], "datasets": ["sms_spam"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-finetuned-spam-detection", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sms_spam", "type": "sms_spam", "args": "plain... | Rhuax/MiniLMv2-L12-H384-distilled-finetuned-spam-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:sms_spam",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T15:05:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-sms_spam #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L12-H384-distilled-finetuned-spam-detection
====================================================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the sms\_spam dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0938
* Accuracy: 0.9928... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-sms_spam #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* ... |
feature-extraction | transformers | PMLM is the language model described in [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://arxiv.org/abs/2004.11579), which is trained with probabilistic masking. This is the "PMLM-R" variant, adapted from [the authors' original implementation](https://github.... | {} | jxm/u-PMLM-R | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2004.11579",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T15:08:29+00:00 | [
"2004.11579"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2004.11579 #endpoints_compatible #region-us
| PMLM is the language model described in Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order, which is trained with probabilistic masking. This is the "PMLM-R" variant, adapted from the authors' original implementation. | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2004.11579 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-finetuned-filtered-0602
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/ber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-large-uncased-finetuned-filtered-0602", "results": []}]} | YeRyeongLee/bert-large-uncased-finetuned-filtered-0602 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T15:28:40+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-large-uncased-finetuned-filtered-0602
==========================================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8409
* Accuracy: 0.1667
* F1: 0.0476
Model description
-----------------
More i... | [
"### 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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TEdetection_distiBERT_mLM_V2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_mLM_V2", "results": []}]} | FritzOS/TEdetection_distiBERT_mLM_V2 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T16:10:29+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TEdetection_distiBERT_mLM_V2
This model is a fine-tuned version of distilbert-base-uncased 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... | [
"# TEdetection_distiBERT_mLM_V2\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and e... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TEdetection_distiBERT_mLM_V2\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results ... |
automatic-speech-recognition | transformers | hello
| {} | creynier/wav2vec2-base-swbd-turn-eos-long_short1-8s_utt_removed_4percent | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T16:11:28+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #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-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | bilalahmed15/Urdu_repo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T16:21:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
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: 0.7532
* Wer: 0.4020
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **CarRacing-v0**
This is a trained model of a **RecurrentPPO** agent playing **CarRacing-v0**
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... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "C... | sb3/ppo_lstm-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-01T16:26:30+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing CarRacing-v0
This is a trained model of a RecurrentPPO agent playing CarRacing-v0
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.
... | [
"# RecurrentPPO Agent playing CarRacing-v0\nThis is a trained model of a RecurrentPPO agent playing CarRacing-v0\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 ... | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing CarRacing-v0\nThis is a trained model of a RecurrentPPO agent playing CarRacing-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a traini... |
feature-extraction | transformers | PMLM is the language model described in [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://arxiv.org/abs/2004.11579), which is trained with probabilistic masking. This is the "PMLM-A" variant, adapted from [the authors' original implementation](https://github.... | {} | jxm/u-PMLM-A | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2004.11579",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T16:37:45+00:00 | [
"2004.11579"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2004.11579 #endpoints_compatible #region-us
| PMLM is the language model described in Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order, which is trained with probabilistic masking. This is the "PMLM-A" variant, adapted from the authors' original implementation. | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2004.11579 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-Location
This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/db... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "bert-finetuned-Location", "results": []}]} | Abderrahim2/bert-finetuned-Location | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T16:38:50+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-Location
=======================
This model is a fine-tuned version of dbmdz/bert-base-french-europeana-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5462
* F1: 0.8167
* Roc Auc: 0.8624
* Accuracy: 0.8133
Model description
-----------------
More i... | [
"### 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: 8",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_b... |
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. -->
# unicamp-finetuned-en-to-pt-dataset-ted
This model is a fine-tuned version of [unicamp-dl/translation-pt-en-t5](https://huggingfa... | {"tags": ["translation", "generated_from_trainer"], "datasets": ["ted_iwlst2013"], "metrics": ["bleu"], "model-index": [{"name": "unicamp-finetuned-en-to-pt-dataset-ted", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "ted_iwlst2013", "type":... | VanessaSchenkel/unicamp-finetuned-en-to-pt-dataset-ted | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:ted_iwlst2013",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T16:57:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-ted_iwlst2013 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# unicamp-finetuned-en-to-pt-dataset-ted
This model is a fine-tuned version of unicamp-dl/translation-pt-en-t5 on the ted_iwlst2013 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8861
- Bleu: 25.6503
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# unicamp-finetuned-en-to-pt-dataset-ted\n\nThis model is a fine-tuned version of unicamp-dl/translation-pt-en-t5 on the ted_iwlst2013 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8861\n- Bleu: 25.6503",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #dataset-ted_iwlst2013 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# unicamp-finetuned-en-to-pt-dataset-ted\n\nThis model is a fine-tuned version of un... |
null | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pubmed_en` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `spancat` |
| **Components** | `tok2vec`, `spancat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |... | {"language": ["en"], "tags": ["spacy"], "model-index": [{"name": "en_pubmed_en", "results": []}]} | thet-system/en_pubmed_en | null | [
"spacy",
"en",
"region:us"
] | null | 2022-06-01T16:58:44+00:00 | [] | [
"en"
] | TAGS
#spacy #en #region-us
|
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #en #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
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"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | AlexanderPeter/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T17:06:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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:
- eval_loss: 0.0593
- eval_precision: 0.9293
- eval_recall: 0.9485
- eval_f1: 0.9388
- eval_accuracy: 0.9858
- eval_runtime: 120.5431
- eval_samples_per_secon... | [
"# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased on the conll2003 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0593\n- eval_precision: 0.9293\n- eval_recall: 0.9485\n- eval_f1: 0.9388\n- eval_accuracy: 0.9858\n- eval_runtime: 120.5431\n- eval_sampl... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased on the conll2003 dataset.\nIt achieves th... |
sentence-similarity | sentence-transformers |
# mmarco-sentence-BERTino
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. It was trained on [mmarco](https://huggingface.co/datasets/unicamp-dl/mmarco/viewer/italian/tra... | {"language": ["it"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["unicamp-dl/mmarco"], "pipeline_tag": "sentence-similarity"} | efederici/mmarco-sentence-BERTino | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"dataset:unicamp-dl/mmarco",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T17:20:17+00:00 | [] | [
"it"
] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #it #dataset-unicamp-dl/mmarco #license-apache-2.0 #endpoints_compatible #region-us
|
# mmarco-sentence-BERTino
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. It was trained on mmarco.
<p align="center">
<img src="URL width="600"> </br>
Mohan Samant, Midnight Fishing P... | [
"# mmarco-sentence-BERTino\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. It was trained on mmarco. \n\n<p align=\"center\">\n <img src=\"URL width=\"600\"> </br>\n Mohan Samant, Mid... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #it #dataset-unicamp-dl/mmarco #license-apache-2.0 #endpoints_compatible #region-us \n",
"# mmarco-sentence-BERTino\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensiona... |
null | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pubmed_rct` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `spancat` |
| **Components** | `tok2vec`, `spancat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a ... | {"language": ["en"], "tags": ["spacy"], "model-index": [{"name": "en_pubmed_rct", "results": []}]} | thet-system/en_pubmed_rct | null | [
"spacy",
"en",
"region:us"
] | null | 2022-06-01T18:05:17+00:00 | [] | [
"en"
] | TAGS
#spacy #en #region-us
|
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #en #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ukrainian-qa
This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-robert... | {"language": "uk", "license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "\u0429\u043e \u0432\u0456\u0434\u043f\u0440\u0430\u0432\u043b\u044f\u0442\u044c \u0434\u043b\u044f \u0417\u0421\u0423?", "context": "\u041f\u0440\u043e \u0446\u0435 \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u0438\u0432 \u043c... | robinhad/ukrainian-qa | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"uk",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T18:28:07+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #uk #license-mit #endpoints_compatible #region-us
| ukrainian-qa
============
This model is a fine-tuned version of ukr-models/xlm-roberta-base-uk on the UA-SQuAD dataset.
Link to training scripts - URL
It achieves the following results on the evaluation set:
* Loss: 1.4778
Model description
-----------------
More information needed
How to use
----------
... | [
"### 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: 6",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #uk #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1258515252163022848/_O1b... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/disgustingact84-kickswish | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T18:44:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Justin Moran & ToxicAct 🇺🇸 ️
@disgustingact84-kickswish
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1530279378332041220/1ysZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/disgustingact84-kickswish-managertactical/1654115021712/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/disgustingact84-kickswish-managertactical | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:06:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
ToxicAct 🇺🇸 ️ & Justin Moran & Tactical Manager
@disgustingact84-kickswish-managertactical
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-subjqa-restaurants-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/as... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-restaurants-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:39:28+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-subjqa-restaurants-qg'
===================================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TEdetection_distiBERT_NER_V2
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_V2](https://huggingface.co/Fritz... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_NER_V2", "results": []}]} | FritzOS/TEdetection_distiBERT_NER_V2 | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T19:40:03+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TEdetection\_distiBERT\_NER\_V2
===============================
This model is a fine-tuned version of FritzOS/TEdetection\_distiBERT\_mLM\_V2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0032
* Validation Loss: 0.0032
* Epoch: 0
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learn... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-subjqa-books-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-qu... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-books-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:41:56+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-subjqa-books-qg'
=============================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language: en
* Training da... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en... |
null | transformers |
# Enformer
Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/e... | {"license": "cc-by-4.0", "inference": false} | EleutherAI/enformer-official-rough | null | [
"transformers",
"pytorch",
"enformer",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-01T19:42:11+00:00 | [] | [] | TAGS
#transformers #pytorch #enformer #license-cc-by-4.0 #region-us
|
# Enformer
Enformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository.
This repo contains the official weights released by Deepmind, ported over to Pytorch.
## Model description
Enf... | [
"# Enformer\n\nEnformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository. \n\nThis repo contains the official weights released by Deepmind, ported over to Pytorch.",
"## Model desc... | [
"TAGS\n#transformers #pytorch #enformer #license-cc-by-4.0 #region-us \n",
"# Enformer\n\nEnformer model. It was introduced in the paper Effective gene expression prediction from sequence by integrating long-range interactions. by Avsec et al. and first released in this repository. \n\nThis repo contains the offi... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1142613360854388738/C49X... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mls_buzz-mlstransfers-transfersmls/1654117028998/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mls_buzz-mlstransfers-transfersmls | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:43:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
MLS Buzz & MLS Transfers & Will Forbes
@mls\_buzz-mlstransfers-transfersmls
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was develop... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-subjqa-tripadvisor-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/as... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-tripadvisor-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:44:49+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-subjqa-tripadvisor-qg'
===================================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-subjqa-grocery-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi417/l... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-grocery-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:47:41+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-subjqa-grocery-qg'
===============================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language: en
* Train... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-subjqa-movies-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-movies-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:50:28+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-subjqa-movies-qg'
==============================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language: en
* Training... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-subjqa-electronics-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/as... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-electronics-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T19:56:36+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-large-subjqa-electronics-qg'
===================================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | q2-jlbar/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T20:36:01+00:00 | [] | [] | TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1199
* Accuracy: 0.9619
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-image_folder #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: 5... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-subjqa-restaurants-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-large-subjqa-restaurants-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:13:24+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-subjqa-restaurants-qg'
=====================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* ... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-subjqa-electronics-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-large-subjqa-electronics-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:15:16+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-subjqa-electronics-qg'
=====================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* ... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-subjqa-books-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-quest... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-base-subjqa-books-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:16:43+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-subjqa-books-qg'
============================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en
* Training data: ... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nT... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-subjqa-electronics-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-base-subjqa-electronics-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:19:25+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-subjqa-electronics-qg'
====================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Lang... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-subjqa-restaurants-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/asahi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-base-subjqa-restaurants-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:20:32+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-subjqa-restaurants-qg'
==================================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-portuguese-cased-finetuned-acordao_v2
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](ht... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-portuguese-cased-finetuned-acordao_v2", "results": []}]} | ederkamphorst/bert-base-portuguese-cased-finetuned-acordao_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:20:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-portuguese-cased-finetuned-acordao\_v2
================================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9156
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: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-subjqa-grocery-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asah... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-large-subjqa-grocery-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:21:56+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-subjqa-grocery-qg'
=================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* Language: en... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-subjqa-restaurants-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/as... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-small-subjqa-restaurants-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:23:38+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-subjqa-restaurants-qg'
===================================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-subjqa-grocery-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi41... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-base-subjqa-grocery-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:24:30+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-subjqa-grocery-qg'
================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Language: en
* T... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-subjqa-electronics-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-base-subjqa-electronics-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:25:49+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-subjqa-electronics-qg'
==================================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-subjqa-restaurants-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-base-subjqa-restaurants-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:27:03+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-subjqa-restaurants-qg'
====================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Lang... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-subjqa-tripadvisor-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-base-subjqa-tripadvisor-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:28:48+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-subjqa-tripadvisor-qg'
====================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Lang... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-subjqa-tripadvisor-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/asahi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-base-subjqa-tripadvisor-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:29:52+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-subjqa-tripadvisor-qg'
==================================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-subjqa-books-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-qu... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-small-subjqa-books-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:31:07+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-subjqa-books-qg'
=============================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language: en
* Training da... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-subjqa-tripadvisor-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-large-subjqa-tripadvisor-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:32:10+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-subjqa-tripadvisor-qg'
=====================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* ... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-subjqa-grocery-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi417/l... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-small-subjqa-grocery-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:33:34+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-subjqa-grocery-qg'
===============================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language: en
* Train... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-subjqa-books-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-base-subjqa-books-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:34:25+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-subjqa-books-qg'
==============================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Language: en
* Trainin... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-subjqa-movies-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-small-subjqa-movies-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:35:25+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-subjqa-movies-qg'
==============================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language: en
* Training... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-subjqa-books-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-large-subjqa-books-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:36:24+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-subjqa-books-qg'
===============================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* Language: en
* Tra... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-subjqa-grocery-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi417/lm-q... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-base-subjqa-grocery-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:41:42+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-subjqa-grocery-qg'
==============================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en
* Training ... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-subjqa-movies-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-que... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-base-subjqa-movies-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:43:07+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-subjqa-movies-qg'
=============================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en
* Training dat... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-subjqa-electronics-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/as... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-small-subjqa-electronics-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:44:19+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-subjqa-electronics-qg'
===================================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-small-subjqa-tripadvisor-qg`
This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/as... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-small-subjqa-tripadvisor-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:45:04+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-small-subjqa-tripadvisor-qg'
===================================================
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Language model: lmqg/t5-small-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-small-squad\n* Language: en... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-subjqa-movies-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi4... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-large-subjqa-movies-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:45:57+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-subjqa-movies-qg'
================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* Language: en
* ... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="meln1k/q-Taxi-v3-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56... | meln1k/q-Taxi-v3-v1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-01T21:47:15+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-subjqa-movies-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Et... | research-backup/bart-base-subjqa-movies-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-01T21:47:38+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-subjqa-movies-qg'
===============================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Language: en
* Trai... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/... |
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-cnn
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Los... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-cnn", "results": []}]} | MadFace/t5-cnn | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-01T21:51:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-cnn
======
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4562
* Rouge1: 25.1836
* Rouge2: 12.0806
* Rougel: 20.818
* Rougelsum: 23.6868
* Gen Len: 18.9986
Model description
-----------------
More information needed
Intended use... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 2e-05\n* train\\_b... |
null | null | Mountain of water painted by monet | {} | Oscarnm/G | null | [
"region:us"
] | null | 2022-06-01T22:19:22+00:00 | [] | [] | TAGS
#region-us
| Mountain of water painted by monet | [] | [
"TAGS\n#region-us \n"
] |
null | null | does weather prediction | {} | tenzin/weather-predictor | null | [
"region:us"
] | null | 2022-06-02T00:08:12+00:00 | [] | [] | TAGS
#region-us
| does weather prediction | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | dkasti/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 | 2022-06-02T00:43:38+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: 0.1649
* F1: 0.8555
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*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny_kt_punctuator
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluatio... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_kt_punctuator", "results": []}]} | kktoto/tiny_kt_punctuator | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T00:44:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_kt\_punctuator
====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1424
* Precision: 0.6287
* Recall: 0.5781
* F1: 0.6023
* Accuracy: 0.9476
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\... |
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", "args": "PAN-X.fr"}, "me... | dkasti/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 | 2022-06-02T00:59:16+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: 0.2789
* F1: 0.8399
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 #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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | dkasti/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:03:25+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-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2388
* F1: 0.8233
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 #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... |
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