| | --- |
| | 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} |
| | } |
| | ``` |
