README.md

metadata

license: cdla-permissive-2.0
task_categories:
  - image-text-to-text
tags:
  - code
  - ocr
size_categories:
  - 1M<n<10M
pretty_name: SynthCodeNet

SynthCodeNet

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}
}