AlpachinoNLP/GlazyBench

GLAZE Benchmark for Hugging Face

This folder is a publication-ready export of the benchmark assets that are currently treated as canonical in this repository.

It is organized into two benchmark tracks:

• property_prediction/: fixed train/test split for glaze property prediction.
• image_generation/: fixed train/test split for glaze image generation with paired metadata and conditioning signals.

It also contains an optional source-information layer for users who want to extract additional fields themselves:

• source_records/: one merged JSON file per packaged sample, plus filtered HTML-derived metadata.
• raw_html/: raw Glazy HTML pages for the packaged sample IDs only.
• tools/: small utilities that operate directly on the exported Hugging Face package.
• baselines/: minimal executable reference baselines for both benchmark tracks.

Included benchmark assets

1. Property prediction benchmark

Canonical source: data/

Tasks:

• transparency: 4-class classification
  • Labels: Opaque, Semi-opaque, Translucent, Transparent
• surface: 9-class classification
  • Labels: Glossy, Semi-glossy, Satin, Satin-matte, Matte, Semi-matte, Smooth Matte, Dry Matte, Stony Matte
• color_family: 9-class classification
  • Labels: Black, Blue, Gray, Green, Orange, Purple, Red, White, Yellow
• color_rgb: RGB regression target derived from the same canonical target file

Current canonical split sizes:

• Train: 16,781 examples
• Test: 4,903 examples

Per-task labeled coverage in the current canonical files:

• Train transparency: 9,023
• Test transparency: 3,322
• Train surface: 9,378
• Test surface: 3,730
• Train color family: 16,781
• Test color family: 4,903

2. Image generation benchmark

Canonical source: image_gen/

This track contains images plus structured metadata for conditional image generation.

Current canonical split sizes:

• Train: 4,490 examples
• Test: 443 examples

Per-task labeled coverage in the current canonical files:

• Train transparency: 2,968
• Test transparency: 344
• Train surface: 3,381
• Test surface: 331
• Train color family: 4,483
• Test color family: 443

Intentionally excluded from this export

These files exist in the repository but are not part of the public benchmark package because they are historical backups, intermediate variants, or analysis-side artifacts:

• data/raw_data/
• data/train/targets_filtered_by_models.json
• data/train/targets_filtered_voting.json
• data/sample_types_train.csv
• data/sample_type_report.md
• training logs, model outputs, checkpoints, and analysis-only files

The rule used here is simple: if a file is not part of the canonical benchmark split consumed by current benchmark code or benchmark-facing documentation, it is excluded.

Optional source-information layer

Some fields in the canonical metadata are intentionally lightweight. In particular, page-derived attributes such as title, author, author URL, and long-form description are richer in the original HTML pages than in the benchmark-facing metadata.json files.

To support downstream custom extraction without changing the canonical splits, the package may include:

• source_records/by_id/<id>.json: merged per-sample record assembled from packaged recipe, target, metadata, and parsed HTML metadata.
• source_records/html_metadata.json: filtered HTML-derived metadata for packaged sample IDs.
• raw_html/<id>.html: original raw HTML page for the packaged sample ID.
• tools/read_source_record.py: convenience CLI for reading sample records and raw HTML from the exported package itself.

These supplemental files are aligned to packaged sample IDs and are meant for provenance and user-defined parsing, not as the primary benchmark interface.

Minimal baselines included

To make the package directly usable after download, this export also includes dependency-light baseline scripts:

• baselines/property_prediction_baseline.py: majority-class baseline for transparency, surface, and color_family, plus a mean-RGB baseline for color_rgb.
• baselines/image_generation_baseline.py: nearest-train-sample retrieval baseline using RGB distance.

Run them from the repository root with:

python huggingface/baselines/property_prediction_baseline.py
python huggingface/baselines/image_generation_baseline.py

See baselines/README.md for output details and optional flags.

Publication note

This folder organizes the benchmark for sharing, but it does not assert a license on behalf of the original data sources. Before uploading to Hugging Face, confirm that the Glazy-derived metadata and images are cleared for redistribution under your intended release terms.

Recommended Hugging Face presentation

If you publish this as a dataset repository, keep the two subfolders as two benchmark configurations inside one dataset card:

• property_prediction
• image_generation

This makes the public package match the benchmark structure already used in this repository.