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---
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}
}
``` |