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--- |
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license: cdla-permissive-2.0 |
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task_categories: |
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- image-text-to-text |
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tags: |
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- code |
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- ocr |
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size_categories: |
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- 1M<n<10M |
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pretty_name: SynthCodeNet |
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--- |
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# SynthCodeNet |
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<div style="display: flex; justify-content: center; align-items: center;"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/663e1254887b6f5645a0399f/whc8Bpip5P8uuzZOS0MQJ.png" alt="Code Example" style="width: 500px; height: auto"> |
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</div> |
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**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. |
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--- |
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## Dataset Statistics |
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* **Total samples**: 9,334,257 |
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* **Training set**: 8,400,838 |
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* **Validation set**: 466,703 |
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* **Test set**: 466,716 |
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* **Modalities**: Image, Text |
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* **Image Generation**: Synthetic (LaTeX, Pygments) |
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### Programming Languages & Sample Counts |
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| Language | Samples | Language | Samples | Language | Samples | |
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| -------- | ------- | ---------- | ------- | ----------- | --------- | |
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| Ada | 20,094 | Dart | 20,415 | Matlab | 1,170 | |
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| Awk | 22,334 | Dockerfile | 99,459 | MoonScript | 6,237 | |
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| Bash | 98,950 | Elixir | 20,387 | Nim | 37,236 | |
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| C | 599,096 | Erlang | 20,039 | OCaml | 32,297 | |
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| C# | 303,720 | FORTRAN | 34,023 | ObjectiveC | 158,398 | |
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| C++ | 698,870 | Forth | 5,548 | Octave | 2,537 | |
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| CMake | 19,910 | Go | 333,722 | PHP | 249,566 | |
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| COBOL | 5,153 | HTML | 245,228 | Pascal | 28,254 | |
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| CSS | 236,596 | Haskell | 39,848 | Perl | 33,938 | |
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| Ceylon | 8,369 | Haxe | 20,070 | Prolog | 2,058 | |
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| Clojure | 20,765 | Java | 698,421 | Python | 1,797,063 | |
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| Crystal | 24,720 | JavaScript | 530,899 | Racket | 4,340 | |
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| Cuda | 142,344 | Julia | 29,681 | Ruby | 348,976 | |
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| Cython | 22,136 | Kotlin | 292,986 | Rust | 344,491 | |
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| D | 20,338 | Lisp | 29,749 | SML | 19,333 | |
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| Lua | 25,328 | SQL | 493,412 | YAML | 249,011 | |
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| Scala | 273,825 | Scheme | 23,242 | VisualBasic | 13,908 | |
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| Swift | 25,374 | TypeScript | 255,475 | XML | 246,209 | |
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| bc | 249 | dc | 1,713 | | | |
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--- |
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## Data Format |
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Each dataset entry is structured as follows: |
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```json |
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{ |
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"images": [PIL Image], |
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"texts": [ |
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{ |
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"assistant": "<loc_x0><loc_y0><loc_x1><loc_y1><_Language_>CODE_SNIPPET</code>", |
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"source": "SynthCodeNetNoImageTag", |
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"user": "<code>" |
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} |
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] |
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} |
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``` |
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--- |
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## Intended Use |
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* Training multimodal models for **document understanding**, specifically: |
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* Code snippet extraction and transcription |
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--- |
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## Citation |
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If you use SynthCodeNet, please cite: |
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```bibtex |
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@article{nassar2025smoldocling, |
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title={SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion}, |
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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}, |
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journal={arXiv preprint arXiv:2503.11576}, |
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year={2025} |
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} |
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``` |
