# PETRA 

## Overview

PETRA is a multilingual dataset for training and evaluating AI systems on a diverse range of tasks across multiple modalities. It contains data in Arabic and English for tasks including translation, summarization, question answering, and more.

## Dataset Structure

- Data is separated by language into `/ar` and `/en` directories
- Within each language directory, data is separated by task into subdirectories  
- Tasks include:
  - Translation
  - Summarization
  - Conversational
  - Feature extraction
  - Zero-shot classification
  - Text generation
  - Fill mask
  - Sentence similarity
  - Text-to-speech
  - Automatic speech recognition
  - Text classification
  - Token classification
  - Table question answering
  - Question answering
  - Text2text generation
  - Audio-to-audio
  - Audio classification
  - Voice activity detection
  - Depth estimation
  - Image classification
  - Object detection
  - Image segmentation
  - Text-to-image
  - Image-to-text
  - Image-to-image
  - Unconditional image generation
  - Reinforcement learning
  - Video classification
  - Robotics
  - Tabular classification
  - Tabular regression
  - Table-to-text
  - Multiple choice
  - Text retrieval
  - Tabular-to-text
  - Text-to-video
  - Time series forecasting
  - Visual question answering
  - Zero-shot image classification
  - Graph ML

## Dataset Tags 

- code
- art
- chemistry
- biology  
- finance
- legal
- music
- climate
- medical

## Dataset Size

1M < n < 10M samples  

## Licenses

Apache 2.0

## Citation

If you use this dataset, please cite it as:

[cite paper, arXiv, etc] 

@article{PetraAI2022PetraAI,
  title={PetraAI: A Massive Multilingual Dataset for Machine Learning}, 
  author={First Last and First Last},
  journal={arXiv},
  year={2022},
  url={https://huggingface.co/datasets/PetraAI/PetraAI}
}

## Contact

For any questions, please reach out to [shadilytn@gmail.com]


# Dataset Cards

## What are Dataset Cards?

Each dataset may be documented by the `README.md` file in the repository. This file is called a **dataset card**, and the Hugging Face Hub will render its contents on the dataset’s main page. To inform users about how to responsibly use the data, it’s a good idea to include information about any potential biases within the dataset. Generally, dataset cards help users understand the contents of the dataset and give context for how the dataset should be used. 

You can also add dataset metadata to your card. The metadata describes important information about a dataset such as its license, language, and size. It also contains tags to help users discover a dataset on the Hub. Tags are defined in a YAML metadata section at the top of the `README.md` file.

## Dataset card metadata

A dataset repo will render its README.md as a dataset card. To control how the Hub displays the card, you should create a YAML section in the README file to define some metadata. Start by adding three --- at the top, then include all of the relevant metadata, and close the section with another group of --- like the example below:


The metadata that you add to the dataset card enables certain interactions on the Hub. For example:

- Allow users to filter and discover datasets at https://huggingface.co/datasets.
  
- If you choose a license using the keywords listed in the right column of this table, the license will be displayed on the dataset page.

When creating a README.md file in a dataset repository on the Hub, use Metadata UI to fill the main metadata:

To see metadata fields, see the detailed dataset card metadata specification here.

### Dataset card creation guide

For a step-by-step guide on creating a dataset card, check out the Create a dataset card guide. 

Reading through existing dataset cards, such as the ELI5 dataset card, is a great way to familiarize yourself with the common conventions.

### Linking a Paper

If the dataset card includes a link to a paper on arXiv, the Hub will extract the arXiv ID and include it in the dataset tags with the format `arxiv:<PAPER ID>`. Clicking on the tag will let you:

- Visit the Paper page
  
- Filter for other models on the Hub that cite the same paper.

Read more about paper pages here.

https://huggingface.co/docs/hub/paper-pages
task_categories:text-classification task_categories:token-classification task_categories:table-question-answering task_categories:question-answering task_categories:zero-shot-classification task_categories:translation task_categories:summarization task_categories:conversational task_categories:feature-extraction task_categories:text-generation task_categories:text2text-generation task_categories:fill-mask task_categories:sentence-similarity task_categories:text-to-speech task_categories:automatic-speech-recognition task_categories:audio-to-audio task_categories:audio-classification task_categories:voice-activity-detection task_categories:depth-estimation task_categories:image-classification task_categories:object-detection task_categories:image-segmentation task_categories:text-to-image task_categories:image-to-text task_categories:image-to-image task_categories:unconditional-image-generation task_categories:video-classification task_categories:reinforcement-learning task_categories:robotics task_categories:tabular-classification task_categories:tabular-regression task_categories:tabular-to-text task_categories:table-to-text task_categories:multiple-choice task_categories:text-retrieval task_categories:time-series-forecasting task_categories:text-to-video task_categories:visual-question-answering task_categories:zero-shot-image-classification task_categories:graph-ml size_categories:1M<n<10M language:ar language:en license:apache-2.0 chemistry biology finance legal music art code climate medical doi:10.57967/hf/0945 region:us
{"language": ["ar", "en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "translation", "summarization", "conversational", "feature-extraction", "text-generation", "text2text-generation", "fill-mask", "sentence-similarity", "text-to-speech", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "voice-activity-detection", "depth-estimation", "image-classification", "object-detection", "image-segmentation", "text-to-image", "image-to-text", "image-to-image", "unconditional-image-generation", "video-classification", "reinforcement-learning", "robotics", "tabular-classification", "tabular-regression", "tabular-to-text", "table-to-text", "multiple-choice", "text-retrieval", "time-series-forecasting", "text-to-video", "visual-question-answering", "zero-shot-image-classification", "graph-ml"], "pretty_name": "PETRA", "tags": ["chemistry", "biology", "finance", "legal", "music", "art", "code", "climate", "medical"]}
TAGS
#task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-translation #task_categories-summarization #task_categories-conversational #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #task_categories-fill-mask #task_categories-sentence-similarity #task_categories-text-to-speech #task_categories-automatic-speech-recognition #task_categories-audio-to-audio #task_categories-audio-classification #task_categories-voice-activity-detection #task_categories-depth-estimation #task_categories-image-classification #task_categories-object-detection #task_categories-image-segmentation #task_categories-text-to-image #task_categories-image-to-text #task_categories-image-to-image #task_categories-unconditional-image-generation #task_categories-video-classification #task_categories-reinforcement-learning #task_categories-robotics #task_categories-tabular-classification #task_categories-tabular-regression #task_categories-tabular-to-text #task_categories-table-to-text #task_categories-multiple-choice #task_categories-text-retrieval #task_categories-time-series-forecasting #task_categories-text-to-video #task_categories-visual-question-answering #task_categories-zero-shot-image-classification #task_categories-graph-ml #size_categories-1M<n<10M #language-Arabic #language-English #license-apache-2.0 #chemistry #biology #finance #legal #music #art #code #climate #medical #doi-10.57967/hf/0945 #region-us 

# PETRA 

## Overview

PETRA is a multilingual dataset for training and evaluating AI systems on a diverse range of tasks across multiple modalities. It contains data in Arabic and English for tasks including translation, summarization, question answering, and more.

## Dataset Structure

- Data is separated by language into '/ar' and '/en' directories
- Within each language directory, data is separated by task into subdirectories  
- Tasks include:
  - Translation
  - Summarization
  - Conversational
  - Feature extraction
  - Zero-shot classification
  - Text generation
  - Fill mask
  - Sentence similarity
  - Text-to-speech
  - Automatic speech recognition
  - Text classification
  - Token classification
  - Table question answering
  - Question answering
  - Text2text generation
  - Audio-to-audio
  - Audio classification
  - Voice activity detection
  - Depth estimation
  - Image classification
  - Object detection
  - Image segmentation
  - Text-to-image
  - Image-to-text
  - Image-to-image
  - Unconditional image generation
  - Reinforcement learning
  - Video classification
  - Robotics
  - Tabular classification
  - Tabular regression
  - Table-to-text
  - Multiple choice
  - Text retrieval
  - Tabular-to-text
  - Text-to-video
  - Time series forecasting
  - Visual question answering
  - Zero-shot image classification
  - Graph ML

## Dataset Tags 

- code
- art
- chemistry
- biology  
- finance
- legal
- music
- climate
- medical

## Dataset Size

1M < n < 10M samples  

## Licenses

Apache 2.0

If you use this dataset, please cite it as:

[cite paper, arXiv, etc] 

@article{PetraAI2022PetraAI,
  title={PetraAI: A Massive Multilingual Dataset for Machine Learning}, 
  author={First Last and First Last},
  journal={arXiv},
  year={2022},
  url={URL
}

## Contact

For any questions, please reach out to [shadilytn@URL]


# Dataset Cards

## What are Dataset Cards?

Each dataset may be documented by the 'URL' file in the repository. This file is called a dataset card, and the Hugging Face Hub will render its contents on the dataset’s main page. To inform users about how to responsibly use the data, it’s a good idea to include information about any potential biases within the dataset. Generally, dataset cards help users understand the contents of the dataset and give context for how the dataset should be used. 

You can also add dataset metadata to your card. The metadata describes important information about a dataset such as its license, language, and size. It also contains tags to help users discover a dataset on the Hub. Tags are defined in a YAML metadata section at the top of the 'URL' file.

## Dataset card metadata

A dataset repo will render its URL as a dataset card. To control how the Hub displays the card, you should create a YAML section in the README file to define some metadata. Start by adding three --- at the top, then include all of the relevant metadata, and close the section with another group of --- like the example below:


The metadata that you add to the dataset card enables certain interactions on the Hub. For example:

- Allow users to filter and discover datasets at URL
  
- If you choose a license using the keywords listed in the right column of this table, the license will be displayed on the dataset page.

When creating a URL file in a dataset repository on the Hub, use Metadata UI to fill the main metadata:

To see metadata fields, see the detailed dataset card metadata specification here.

### Dataset card creation guide

For a step-by-step guide on creating a dataset card, check out the Create a dataset card guide. 

Reading through existing dataset cards, such as the ELI5 dataset card, is a great way to familiarize yourself with the common conventions.

### Linking a Paper

If the dataset card includes a link to a paper on arXiv, the Hub will extract the arXiv ID and include it in the dataset tags with the format 'arxiv:<PAPER ID>'. Clicking on the tag will let you:

- Visit the Paper page
  
- Filter for other models on the Hub that cite the same paper.

Read more about paper pages here.

URL
# PETRA ## Overview

PETRA is a multilingual dataset for training and evaluating AI systems on a diverse range of tasks across multiple modalities. It contains data in Arabic and English for tasks including translation, summarization, question answering, and more. ## Dataset Structure

- Data is separated by language into '/ar' and '/en' directories
- Within each language directory, data is separated by task into subdirectories  
- Tasks include:
  - Translation
  - Summarization
  - Conversational
  - Feature extraction
  - Zero-shot classification
  - Text generation
  - Fill mask
  - Sentence similarity
  - Text-to-speech
  - Automatic speech recognition
  - Text classification
  - Token classification
  - Table question answering
  - Question answering
  - Text2text generation
  - Audio-to-audio
  - Audio classification
  - Voice activity detection
  - Depth estimation
  - Image classification
  - Object detection
  - Image segmentation
  - Text-to-image
  - Image-to-text
  - Image-to-image
  - Unconditional image generation
  - Reinforcement learning
  - Video classification
  - Robotics
  - Tabular classification
  - Tabular regression
  - Table-to-text
  - Multiple choice
  - Text retrieval
  - Tabular-to-text
  - Text-to-video
  - Time series forecasting
  - Visual question answering
  - Zero-shot image classification
  - Graph ML ## Dataset Tags 

- code
- art
- chemistry
- biology  
- finance
- legal
- music
- climate
- medical ## Dataset Size

1M < n < 10M samples ## Licenses

Apache 2.0

If you use this dataset, please cite it as:

[cite paper, arXiv, etc] 

@article{PetraAI2022PetraAI,
  title={PetraAI: A Massive Multilingual Dataset for Machine Learning}, 
  author={First Last and First Last},
  journal={arXiv},
  year={2022},
  url={URL
} ## Contact

For any questions, please reach out to [shadilytn@URL] # Dataset Cards ## What are Dataset Cards?

Each dataset may be documented by the 'URL' file in the repository. This file is called a dataset card, and the Hugging Face Hub will render its contents on the dataset’s main page. To inform users about how to responsibly use the data, it’s a good idea to include information about any potential biases within the dataset. Generally, dataset cards help users understand the contents of the dataset and give context for how the dataset should be used. 

You can also add dataset metadata to your card. The metadata describes important information about a dataset such as its license, language, and size. It also contains tags to help users discover a dataset on the Hub. Tags are defined in a YAML metadata section at the top of the 'URL' file. ## Dataset card metadata

A dataset repo will render its URL as a dataset card. To control how the Hub displays the card, you should create a YAML section in the README file to define some metadata. Start by adding three --- at the top, then include all of the relevant metadata, and close the section with another group of --- like the example below:


The metadata that you add to the dataset card enables certain interactions on the Hub. For example:

- Allow users to filter and discover datasets at URL
  
- If you choose a license using the keywords listed in the right column of this table, the license will be displayed on the dataset page.

When creating a URL file in a dataset repository on the Hub, use Metadata UI to fill the main metadata:

To see metadata fields, see the detailed dataset card metadata specification here. ### Dataset card creation guide

For a step-by-step guide on creating a dataset card, check out the Create a dataset card guide. 

Reading through existing dataset cards, such as the ELI5 dataset card, is a great way to familiarize yourself with the common conventions. ### Linking a Paper

If the dataset card includes a link to a paper on arXiv, the Hub will extract the arXiv ID and include it in the dataset tags with the format 'arxiv:<PAPER ID>'. Clicking on the tag will let you:

- Visit the Paper page
  
- Filter for other models on the Hub that cite the same paper.

Read more about paper pages here.

URL
TAGS
#task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-translation #task_categories-summarization #task_categories-conversational #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #task_categories-fill-mask #task_categories-sentence-similarity #task_categories-text-to-speech #task_categories-automatic-speech-recognition #task_categories-audio-to-audio #task_categories-audio-classification #task_categories-voice-activity-detection #task_categories-depth-estimation #task_categories-image-classification #task_categories-object-detection #task_categories-image-segmentation #task_categories-text-to-image #task_categories-image-to-text #task_categories-image-to-image #task_categories-unconditional-image-generation #task_categories-video-classification #task_categories-reinforcement-learning #task_categories-robotics #task_categories-tabular-classification #task_categories-tabular-regression #task_categories-tabular-to-text #task_categories-table-to-text #task_categories-multiple-choice #task_categories-text-retrieval #task_categories-time-series-forecasting #task_categories-text-to-video #task_categories-visual-question-answering #task_categories-zero-shot-image-classification #task_categories-graph-ml #size_categories-1M<n<10M #language-Arabic #language-English #license-apache-2.0 #chemistry #biology #finance #legal #music #art #code #climate #medical #doi-10.57967/hf/0945 #region-us 
 # PETRA ## Overview

PETRA is a multilingual dataset for training and evaluating AI systems on a diverse range of tasks across multiple modalities. It contains data in Arabic and English for tasks including translation, summarization, question answering, and more. ## Dataset Structure

- Data is separated by language into '/ar' and '/en' directories
- Within each language directory, data is separated by task into subdirectories  
- Tasks include:
  - Translation
  - Summarization
  - Conversational
  - Feature extraction
  - Zero-shot classification
  - Text generation
  - Fill mask
  - Sentence similarity
  - Text-to-speech
  - Automatic speech recognition
  - Text classification
  - Token classification
  - Table question answering
  - Question answering
  - Text2text generation
  - Audio-to-audio
  - Audio classification
  - Voice activity detection
  - Depth estimation
  - Image classification
  - Object detection
  - Image segmentation
  - Text-to-image
  - Image-to-text
  - Image-to-image
  - Unconditional image generation
  - Reinforcement learning
  - Video classification
  - Robotics
  - Tabular classification
  - Tabular regression
  - Table-to-text
  - Multiple choice
  - Text retrieval
  - Tabular-to-text
  - Text-to-video
  - Time series forecasting
  - Visual question answering
  - Zero-shot image classification
  - Graph ML ## Dataset Tags 

- code
- art
- chemistry
- biology  
- finance
- legal
- music
- climate
- medical ## Dataset Size

1M < n < 10M samples ## Licenses

Apache 2.0

If you use this dataset, please cite it as:

[cite paper, arXiv, etc] 

@article{PetraAI2022PetraAI,
  title={PetraAI: A Massive Multilingual Dataset for Machine Learning}, 
  author={First Last and First Last},
  journal={arXiv},
  year={2022},
  url={URL
} ## Contact

For any questions, please reach out to [shadilytn@URL] # Dataset Cards ## What are Dataset Cards?

Each dataset may be documented by the 'URL' file in the repository. This file is called a dataset card, and the Hugging Face Hub will render its contents on the dataset’s main page. To inform users about how to responsibly use the data, it’s a good idea to include information about any potential biases within the dataset. Generally, dataset cards help users understand the contents of the dataset and give context for how the dataset should be used. 

You can also add dataset metadata to your card. The metadata describes important information about a dataset such as its license, language, and size. It also contains tags to help users discover a dataset on the Hub. Tags are defined in a YAML metadata section at the top of the 'URL' file. ## Dataset card metadata

A dataset repo will render its URL as a dataset card. To control how the Hub displays the card, you should create a YAML section in the README file to define some metadata. Start by adding three --- at the top, then include all of the relevant metadata, and close the section with another group of --- like the example below:


The metadata that you add to the dataset card enables certain interactions on the Hub. For example:

- Allow users to filter and discover datasets at URL
  
- If you choose a license using the keywords listed in the right column of this table, the license will be displayed on the dataset page.

When creating a URL file in a dataset repository on the Hub, use Metadata UI to fill the main metadata:

To see metadata fields, see the detailed dataset card metadata specification here. ### Dataset card creation guide

For a step-by-step guide on creating a dataset card, check out the Create a dataset card guide. 

Reading through existing dataset cards, such as the ELI5 dataset card, is a great way to familiarize yourself with the common conventions. ### Linking a Paper

If the dataset card includes a link to a paper on arXiv, the Hub will extract the arXiv ID and include it in the dataset tags with the format 'arxiv:<PAPER ID>'. Clicking on the tag will let you:

- Visit the Paper page
  
- Filter for other models on the Hub that cite the same paper.

Read more about paper pages here.

URL
passage:  passage: TAGS
#task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-translation #task_categories-summarization #task_categories-conversational #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #task_categories-fill-mask #task_categories-sentence-similarity #task_categories-text-to-speech #task_categories-automatic-speech-recognition #task_categories-audio-to-audio #task_categories-audio-classification #task_categories-voice-activity-detection #task_categories-depth-estimation #task_categories-image-classification #task_categories-object-detection #task_categories-image-segmentation #task_categories-text-to-image #task_categories-image-to-text #task_categories-image-to-image #task_categories-unconditional-image-generation #task_categories-video-classification #task_categories-reinforcement-learning #task_categories-robotics #task_categories-tabular-classification #task_categories-tabular-regression #task_categories-tabular-to-text #task_categories-table-to-text #task_categories-multiple-choice #task_categories-text-retrieval #task_categories-time-series-forecasting #task_categories-text-to-video #task_categories-visual-question-answering #task_categories-zero-shot-image-classification #task_categories-graph-ml #size_categories-1M<n<10M #language-Arabic #language-English #license-apache-2.0 #chemistry #biology #finance #legal #music #art #code #climate #medical #doi-10.57967/hf/0945 #region-us 
# PETRA## Overview

PETRA is a multilingual dataset for training and evaluating AI systems on a diverse range of tasks across multiple modalities. It contains data in Arabic and English for tasks including translation, summarization, question answering, and more.## Dataset Structure

- Data is separated by language into '/ar' and '/en' directories
- Within each language directory, data is separated by task into subdirectories  
- Tasks include:
  - Translation
  - Summarization
  - Conversational
  - Feature extraction
  - Zero-shot classification
  - Text generation
  - Fill mask
  - Sentence similarity
  - Text-to-speech
  - Automatic speech recognition
  - Text classification
  - Token classification
  - Table question answering
  - Question answering
  - Text2text generation
  - Audio-to-audio
  - Audio classification
  - Voice activity detection
  - Depth estimation
  - Image classification
  - Object detection
  - Image segmentation
  - Text-to-image
  - Image-to-text
  - Image-to-image
  - Unconditional image generation
  - Reinforcement learning
  - Video classification
  - Robotics
  - Tabular classification
  - Tabular regression
  - Table-to-text
  - Multiple choice
  - Text retrieval
  - Tabular-to-text
  - Text-to-video
  - Time series forecasting
  - Visual question answering
  - Zero-shot image classification
  - Graph ML## Dataset Tags 

- code
- art
- chemistry
- biology  
- finance
- legal
- music
- climate
- medical## Dataset Size

1M < n < 10M samples## Licenses

Apache 2.0

If you use this dataset, please cite it as:

[cite paper, arXiv, etc] 

@article{PetraAI2022PetraAI,
  title={PetraAI: A Massive Multilingual Dataset for Machine Learning}, 
  author={First Last and First Last},
  journal={arXiv},
  year={2022},
  url={URL
}## Contact

For any questions, please reach out to [shadilytn@URL]# Dataset Cards