| --- |
| license: cdla-permissive-2.0 |
| datasets: |
| - ds4sd/SynthFormulaNet |
| - ds4sd/SynthCodeNet |
| tags: |
| - ocr |
| - code |
| - math |
| - formula |
| --- |
| |
| # Code Formula Model |
|
|
| The **Code Formula Model** processes an image of a code snippet or formula at 120 DPI and outputs its content. |
|
|
| - **Code Snippets**: |
| The model identifies the programming language and outputs the code repsecting the indendation shown in the given image. The output format will be:<br> |
| "<\_\<programming language\>\_> \<content of the image\>"<br> |
| Example:<br> |
| "<_Java_> System.out.println("Hello World.");" |
|
|
| - **Formulas**: |
| The model generates the corresponding LaTeX code. |
|
|
|
|
| This model was trained using the following two datasets: |
| 1. https://huggingface.co/datasets/ds4sd/SynthFormulaNet |
| 2. https://huggingface.co/datasets/ds4sd/SynthCodeNet |
|
|
| # References |
| ```bibtex |
| @techreport{Docling, |
| author = {Deep Search Team}, |
| month = {8}, |
| title = {{Docling Technical Report}}, |
| url={https://arxiv.org/abs/2408.09869}, |
| eprint={2408.09869}, |
| doi = "10.48550/arXiv.2408.09869", |
| version = {1.0.0}, |
| year = {2024} |
| } |
| |
| @article{nassar2025smoldocling, |
| title={SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion}, |
| author={Nassar, Ahmed and Marafioti, Andres and Omenetti, Matteo and Lysak, Maksym and Livathinos, Nikolaos and Auer, Christoph and Morin, Lucas and de Lima, Rafael Teixeira and Kim, Yusik and Gurbuz, A Said and others}, |
| journal={arXiv preprint arXiv:2503.11576}, |
| year={2025} |
| } |
| |
| ``` |
