Create README.md

README.md

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+---
+license: cdla-permissive-2.0
+task_categories:
+- text-generation
+tags:
+- code
+- ocr
+size_categories:
+- 1M<n<10M
+---
+**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 resources, while images were synthetically generated 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}
+}
+```