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---
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dataset_info:
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features:
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- name: latex
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dtype: string
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- name: image
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dtype: image
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splits:
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- name: train
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num_bytes: 66713771
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num_examples: 12312
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download_size: 66713771
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dataset_size: 66713771
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configs:
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- config_name: default
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data_files:
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- split: train
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path: "*.parquet"
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language:
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- en
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tags:
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- mathematics
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- latex
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- computer-vision
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- OCR
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size_categories:
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- 10K<n<100K
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task_categories:
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- image-to-text
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pretty_name: Mathematical Expressions Dataset
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viewer: true
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---
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# Mathematical Expressions Dataset
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## Dataset Description
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This dataset contains images of mathematical expressions along with their corresponding LaTeX code.
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**Images will automatically be displayed as thumbnails in Hugging Face's Data Studio.**
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### Dataset Summary
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- **Number of files**: 1 Parquet files
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- **Estimated number of samples**: 12,312
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- **Format**: Parquet optimized for Hugging Face
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- **Features configured for thumbnails**: ✅
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- **Columns**:
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- `latex`: LaTeX code of the mathematical expression (string)
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- `image`: Image of the mathematical expression (Image with decode=True)
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### Supported Tasks
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- **Image-to-Text**: Conversion of mathematical expression images to LaTeX code
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- **OCR**: Optical character recognition for mathematical characters
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- **Mathematical Expression Recognition**: Recognition of mathematical expressions
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### Languages
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The dataset contains mathematical expressions that are universal. The LaTeX code and associated metadata are primarily in English.
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## Dataset Structure
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### Data Fields
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- `latex`: String with the LaTeX code that generates the mathematical expression.
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- `image`: PIL image containing the rendered mathematical expression.
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- **Type**: `datasets.Image(decode=True)`
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- **Format**: Images are automatically decoded to PIL.Image.
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- **Thumbnails**: Automatically generated in Data Studio.
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### Data Splits
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| Split | Examples |
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|-------|-----------|
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| train | 12,312 |
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## Usage
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```python
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from datasets import load_dataset
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# Load the dataset
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# Make sure to replace {repo_id_placeholder} with your actual Hugging Face repository ID
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# For example: "your_username/your_dataset_name"
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dataset = load_dataset("ToniDO/TeXtract_augraphy_v1")
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# Access a sample
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sample = dataset['train'][0]
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image = sample['image'] # Already a PIL.Image thanks to decode=True
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latex_code = sample['latex']
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print(f"LaTeX: {latex_code}")
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image.show() # Display the image
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# Images will also appear as thumbnails in Data Studio
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```
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## Visualization
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Images will automatically be displayed as thumbnails in Hugging Face's Data Studio thanks to the Features configuration:
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```python
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from datasets import Features, Value, Image
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features = Features({
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"latex": Value("string"),
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"image": Image(decode=True) # This generates the thumbnails
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})
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```
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## Dataset Creation
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This dataset was created by converting WebDataset (.tar) files to Parquet format optimized for Hugging Face.
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### Source Data
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- **Original format**: WebDataset (.tar)
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- **Conversion**: Using a custom Python script.
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- **Optimization**: Parquet format with Snappy compression (default for `pyarrow.parquet.write_table` if not specified otherwise, actual compression depends on how your Parquet files were created).
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- **Features**: Explicitly configured for automatic thumbnails.
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## Technical Details
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### Image Handling
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- **Storage**: Images are stored as bytes within the Parquet file.
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- **Decoding**: Automatic to PIL.Image when loading the dataset using `datasets.Image(decode=True)`.
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- **Thumbnails**: Automatically generated by Hugging Face Data Studio.
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- **Compatibility**: Works with image formats supported by PIL (Pillow) when decoded.
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## Considerations for Using the Data
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### Social Impact of Dataset
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This dataset can be used to:
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- Improve mathematical expression recognition tools.
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- Develop OCR systems specialized in mathematics.
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- Create accessibility tools for mathematical content.
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### Licensing Information
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- Apache 2.0
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### Citation Information
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If you use this dataset in your research, please cite it as follows:
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```bibtex
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@misc{ToniDO_TeXtract_parquet_2025},
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author = {ToniDO},
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title = {{TeXtract_parquet (Parquet Format)}},
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year = {2025},
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publisher = {Hugging Face},
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version = {1.0.0},
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url = {https://huggingface.co/datasets/ToniDO/TeXtract_augraphy_v1}
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}
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```
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