Datasets:
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license: other
task_categories:
- image-to-text
tags:
- structured-image-understanding
- svg
- mermaid
- graphviz
- plantuml
- vega-lite
- diagram-to-code
pretty_name: StructCrawl Sample (image → structured text)
size_categories:
- 10K<n<100K
configs:
- config_name: svg
data_files: svg/*.parquet
- config_name: mermaid
data_files: mermaid/*.parquet
- config_name: dot
data_files: dot/*.parquet
- config_name: plantuml
data_files: plantuml/*.parquet
- config_name: vega
data_files: vega/*.parquet
---
# StructCrawl Sample — Structured Image Understanding
A **uniform random subsample** (~500 per format×stage×source cell) of a larger
web-crawled corpus for **structured image understanding**: converting a
rendered image of a diagram/chart into the editable structured-text code that
produces it.
This is a *sharing sample*, not the full dataset — it is stratified so every
length band and every source is represented for inspection.
## Configs (one per code representation)
| Config | Rows | Description |
|--------|-----:|-------------|
| `svg` | ~21.7K | Diagrams/charts/schematics as SVG XML |
| `dot` | ~9.7K | Graphs as Graphviz DOT |
| `plantuml` | ~3.7K | UML/sequence diagrams as PlantUML |
| `vega` | ~3.5K | Charts as Vega-Lite JSON specs |
| `mermaid` | ~3.3K | Flowcharts/sequence/class/ER as Mermaid |
*(Exact per-config counts in `_manifest.json`.)*
## Usage
```python
from datasets import load_dataset
ds = load_dataset("<your-username>/<repo-name>", "svg", split="train")
ex = ds[0]
ex["image"] # PIL.Image — the rendered diagram
ex["target_text"] # the SVG/Mermaid/DOT/PlantUML/Vega source to reproduce it
ex["stage"] # token-length band, e.g. "stage_0-2048"
ex["source"] # crawl origin, e.g. "commoncrawl_inline"
ex["token_count"] # target_text length in SFT tokenizer tokens
```
## Fields
| Field | Type | Notes |
|-------|------|-------|
| `id` | string | unique sample id |
| `format` | string | svg \| mermaid \| dot \| plantuml \| vega |
| `source` | string | crawl origin (commoncrawl_inline, github_repos, github_search, thestack_*, web_linked, wikimedia, ...) |
| `stage` | string | token-length band (2048-token steps to 8192, 4096-steps to 32768, then `stage_32768_plus`) |
| `token_count` | int64 | `target_text` length (SFT tokenizer) |
| `image` | Image | rendered diagram (native dimensions, white background) |
| `target_text` | string | the structured-text code that renders to `image` |
| `provenance` | string | original url / repo / path when available |
## Sampling & processing
- **Stratified:** up to 500 samples drawn uniformly at random (seed 42,
reservoir sampling) from every (format × stage × source) cell.
- **Washed:** samples whose code cannot be reproduced from the image alone
were removed upstream — base64-embedded rasters/fonts, external image/data
references, scripts, `<foreignObject>`, PlantUML `!include`/sprites.
- **Deduplicated:** exact content-level dedup (whitespace-normalized SHA-256),
filename-independent. Near-duplicate template families may still remain.
- **Render-validated:** every `target_text` was executed and produced a
non-blank image; the paired `image` is that render.
## Caveats
- Licensing is mixed (public web, license-filtered source corpora, CC-BY
Commons) — intended for research; per-sample `provenance` retained for
attribution.
- SVG length is heavy-tailed; use `stage` / `token_count` to filter.
- This is a small representative slice of a ~860K-pair processed corpus.
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