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
---
license: apache-2.0
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
language:
- ar
- en
tags:
- chemistry
- biology
- finance
- legal
- music
- art
- code
- climate
- medical
pretty_name: PETRA
size_categories:
- 1M<n<10M
---
# 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]


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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:


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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.

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Read more about paper pages here.

https://huggingface.co/docs/hub/paper-pages