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SynthCodeNet

Tasks:
Image-Text-to-Text
Modalities:
Image
Text
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Size:
1M - 10M
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Tags:
code
ocr
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metadata
license: cdla-permissive-2.0
task_categories:
  - image-text-to-text
tags:
  - code
  - ocr
size_categories:
  - 1M<n<10M
pretty_name: SynthCodeNet

SynthCodeNet

Code Example

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:

{
  "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:

@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}
}