| --- |
| license: cdla-permissive-2.0 |
| task_categories: |
| - image-text-to-text |
| tags: |
| - code |
| - ocr |
| size_categories: |
| - 1M<n<10M |
| pretty_name: SynthCodeNet |
| --- |
| # SynthCodeNet |
| <div style="display: flex; justify-content: center; align-items: center;"> |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/663e1254887b6f5645a0399f/whc8Bpip5P8uuzZOS0MQJ.png" alt="Code Example" style="width: 500px; height: auto"> |
| </div> |
| |
| **SynthCodeNet** is a multimodal dataset created for training the **SmolDocling** model. It consists of over **9.3 million** synthetically generated image-text pairs, covering code snippets from **56** different programming languages. Text data was sourced from permissively licensed sources, while images were synthetically generated at 120 DPI using LaTeX and Pygments to ensure visual diversity. |
|
|
| --- |
|
|
| ## Dataset Statistics |
|
|
| * **Total samples**: 9,334,257 |
|
|
| * **Training set**: 8,400,838 |
| * **Validation set**: 466,703 |
| * **Test set**: 466,716 |
|
|
| * **Modalities**: Image, Text |
|
|
| * **Image Generation**: Synthetic (LaTeX, Pygments) |
|
|
| ### Programming Languages & Sample Counts |
|
|
| | Language | Samples | Language | Samples | Language | Samples | |
| | -------- | ------- | ---------- | ------- | ----------- | --------- | |
| | Ada | 20,094 | Dart | 20,415 | Matlab | 1,170 | |
| | Awk | 22,334 | Dockerfile | 99,459 | MoonScript | 6,237 | |
| | Bash | 98,950 | Elixir | 20,387 | Nim | 37,236 | |
| | C | 599,096 | Erlang | 20,039 | OCaml | 32,297 | |
| | C# | 303,720 | FORTRAN | 34,023 | ObjectiveC | 158,398 | |
| | C++ | 698,870 | Forth | 5,548 | Octave | 2,537 | |
| | CMake | 19,910 | Go | 333,722 | PHP | 249,566 | |
| | COBOL | 5,153 | HTML | 245,228 | Pascal | 28,254 | |
| | CSS | 236,596 | Haskell | 39,848 | Perl | 33,938 | |
| | Ceylon | 8,369 | Haxe | 20,070 | Prolog | 2,058 | |
| | Clojure | 20,765 | Java | 698,421 | Python | 1,797,063 | |
| | Crystal | 24,720 | JavaScript | 530,899 | Racket | 4,340 | |
| | Cuda | 142,344 | Julia | 29,681 | Ruby | 348,976 | |
| | Cython | 22,136 | Kotlin | 292,986 | Rust | 344,491 | |
| | D | 20,338 | Lisp | 29,749 | SML | 19,333 | |
| | Lua | 25,328 | SQL | 493,412 | YAML | 249,011 | |
| | Scala | 273,825 | Scheme | 23,242 | VisualBasic | 13,908 | |
| | Swift | 25,374 | TypeScript | 255,475 | XML | 246,209 | |
| | bc | 249 | dc | 1,713 | | | |
|
|
| --- |
|
|
| ## Data Format |
|
|
| Each dataset entry is structured as follows: |
|
|
| ```json |
| { |
| "images": [PIL Image], |
| "texts": [ |
| { |
| "assistant": "<loc_x0><loc_y0><loc_x1><loc_y1><_Language_>CODE_SNIPPET</code>", |
| "source": "SynthCodeNetNoImageTag", |
| "user": "<code>" |
| } |
| ] |
| } |
| ``` |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| * Training multimodal models for **document understanding**, specifically: |
|
|
| * Code snippet extraction and transcription |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use SynthCodeNet, please cite: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
