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.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+property_prediction/train/recipes.json filter=lfs diff=lfs merge=lfs -text
+source_records/html_metadata.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,120 @@
----
-license: mit
----
+# 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:
+
+```bash
+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.

baselines/README.md

@@ -0,0 +1,48 @@
+# Simple Baselines
+
+This folder contains lightweight reference baselines for the packaged benchmark.
+
+These scripts are designed for quick sanity checks and reproducible loading of the shared data package. They are not the paper's strongest models.
+
+## Included scripts
+
+- `property_prediction_baseline.py`: majority-class baseline for classification targets plus mean-RGB baseline for `color_rgb`.
+- `image_generation_baseline.py`: nearest-neighbor retrieval baseline that matches each test image to the closest train sample in RGB space.
+
+## Why these baselines exist
+
+- They verify that the packaged JSON structure is directly consumable.
+- They give downstream users a minimal executable reference without requiring the full project training stack.
+- They produce simple metrics that are easy to compare after custom loaders or more advanced methods are added.
+
+## Commands
+
+Run the property prediction baseline:
+
+```bash
+python huggingface/baselines/property_prediction_baseline.py
+```
+
+Run the image generation retrieval baseline:
+
+```bash
+python huggingface/baselines/image_generation_baseline.py
+```
+
+Save image-generation retrievals for inspection:
+
+```bash
+python huggingface/baselines/image_generation_baseline.py --save-retrievals out/image_generation_retrievals.jsonl
+```
+
+## Output
+
+- Both scripts print a JSON summary to stdout by default.
+- `--output` writes the summary to a JSON file.
+- `image_generation_baseline.py --save-retrievals ...` writes one JSON object per test sample with the retrieved train sample and image path.
+
+## Notes
+
+- No extra Python packages are required.
+- The property baseline evaluates labeled test subsets only for `transparency` and `surface`.
+- The image-generation baseline is retrieval-based, not a generative model. It is included as a minimal conditioning-aware reference for the packaged benchmark.

baselines/image_generation_baseline.py

@@ -0,0 +1,158 @@
+"""Simple retrieval-style reference baseline for the image generation benchmark.
+
+The baseline uses only the packaged metadata and retrieves the nearest training
+example by RGB distance for every test sample.
+"""
+
+import argparse
+import json
+import math
+from pathlib import Path
+
+
+HF_ROOT = Path(__file__).resolve().parents[1]
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run a nearest-RGB retrieval baseline on the image generation split."
+    )
+    parser.add_argument(
+        "--hf-root",
+        default=str(HF_ROOT),
+        help="Path to the exported huggingface package root.",
+    )
+    parser.add_argument(
+        "--output",
+        help="Optional JSON output path for the retrieval summary.",
+    )
+    parser.add_argument(
+        "--save-retrievals",
+        help="Optional JSONL output path for per-sample retrieval results.",
+    )
+    parser.add_argument(
+        "--compact",
+        action="store_true",
+        help="Write compact JSON instead of pretty JSON.",
+    )
+    return parser.parse_args()
+
+
+def load_json(path: Path):
+    with open(path, "r", encoding="utf-8") as handle:
+        return json.load(handle)
+
+
+def dump_json(value, compact: bool):
+    if compact:
+        return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
+    return json.dumps(value, ensure_ascii=False, indent=2)
+
+
+def write_text(text: str, output_path: str | None):
+    if output_path:
+        path = Path(output_path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w", encoding="utf-8") as handle:
+            handle.write(text)
+        return
+    print(text)
+
+
+def rgb_distance(left, right):
+    return math.sqrt(
+        (left[0] - right[0]) ** 2
+        + (left[1] - right[1]) ** 2
+        + (left[2] - right[2]) ** 2
+    )
+
+
+def nearest_neighbor(sample, train_samples):
+    best_match = None
+    best_distance = None
+    for candidate in train_samples:
+        distance = rgb_distance(sample["color_rgb"], candidate["color_rgb"])
+        if best_distance is None or distance < best_distance:
+            best_distance = distance
+            best_match = candidate
+    return best_match, best_distance
+
+
+def safe_rate(correct: int, total: int):
+    if total == 0:
+        return None
+    return correct / total
+
+
+def main():
+    args = parse_args()
+    hf_root = Path(args.hf_root)
+
+    train_metadata = load_json(hf_root / "image_generation" / "train" / "metadata.json")
+    test_metadata = load_json(hf_root / "image_generation" / "test" / "metadata.json")
+
+    train_samples = list(train_metadata.values())
+    test_samples = list(test_metadata.values())
+
+    retrievals = []
+    total_distance = 0.0
+    transparency_correct = 0
+    transparency_total = 0
+    surface_correct = 0
+    surface_total = 0
+    color_family_correct = 0
+    color_family_total = 0
+
+    for sample in test_samples:
+        match, distance = nearest_neighbor(sample, train_samples)
+        total_distance += distance
+        retrieval = {
+            "test_id": sample["id"],
+            "retrieved_train_id": match["id"],
+            "distance": distance,
+            "test_image_path": sample["image_path"],
+            "retrieved_image_path": match["image_path"],
+        }
+        retrievals.append(retrieval)
+
+        if sample.get("transparency") is not None and match.get("transparency") is not None:
+            transparency_total += 1
+            transparency_correct += int(sample["transparency"] == match["transparency"])
+        if sample.get("surface") is not None and match.get("surface") is not None:
+            surface_total += 1
+            surface_correct += int(sample["surface"] == match["surface"])
+        if sample.get("color_family") is not None and match.get("color_family") is not None:
+            color_family_total += 1
+            color_family_correct += int(sample["color_family"] == match["color_family"])
+
+    summary = {
+        "benchmark": "image_generation",
+        "baseline": "nearest_train_rgb_retrieval",
+        "train_samples": len(train_samples),
+        "test_samples": len(test_samples),
+        "mean_retrieval_distance": total_distance / len(test_samples),
+        "label_agreement": {
+            "transparency": {
+                "evaluated_samples": transparency_total,
+                "accuracy": safe_rate(transparency_correct, transparency_total),
+            },
+            "surface": {
+                "evaluated_samples": surface_total,
+                "accuracy": safe_rate(surface_correct, surface_total),
+            },
+            "color_family": {
+                "evaluated_samples": color_family_total,
+                "accuracy": safe_rate(color_family_correct, color_family_total),
+            },
+        },
+    }
+
+    if args.save_retrievals:
+        jsonl = "\n".join(json.dumps(item, ensure_ascii=False) for item in retrievals)
+        write_text(jsonl, args.save_retrievals)
+
+    write_text(dump_json(summary, args.compact), args.output)
+
+
+if __name__ == "__main__":
+    main()

baselines/property_prediction_baseline.py

@@ -0,0 +1,150 @@
+"""Simple reference baseline for the property prediction benchmark.
+
+This script is intentionally lightweight and dependency-free so it can be
+shipped together with the public Hugging Face package.
+"""
+
+import argparse
+import json
+import math
+from collections import Counter
+from pathlib import Path
+
+
+HF_ROOT = Path(__file__).resolve().parents[1]
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run a simple majority/mean baseline on the property prediction split."
+    )
+    parser.add_argument(
+        "--hf-root",
+        default=str(HF_ROOT),
+        help="Path to the exported huggingface package root.",
+    )
+    parser.add_argument(
+        "--output",
+        help="Optional JSON output path for the evaluation summary.",
+    )
+    parser.add_argument(
+        "--compact",
+        action="store_true",
+        help="Write compact JSON instead of pretty JSON.",
+    )
+    return parser.parse_args()
+
+
+def load_json(path: Path):
+    with open(path, "r", encoding="utf-8") as handle:
+        return json.load(handle)
+
+
+def dump_json(value, compact: bool):
+    if compact:
+        return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
+    return json.dumps(value, ensure_ascii=False, indent=2)
+
+
+def write_output(text: str, output_path: str | None):
+    if output_path:
+        path = Path(output_path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w", encoding="utf-8") as handle:
+            handle.write(text)
+        return
+    print(text)
+
+
+def majority_label(samples, field_name: str):
+    counter = Counter(sample[field_name] for sample in samples if sample.get(field_name) is not None)
+    label, count = counter.most_common(1)[0]
+    return label, count, sum(counter.values())
+
+
+def evaluate_majority(samples, field_name: str, predicted_label: str):
+    labeled = [sample for sample in samples if sample.get(field_name) is not None]
+    correct = sum(1 for sample in labeled if sample[field_name] == predicted_label)
+    total = len(labeled)
+    accuracy = None if total == 0 else correct / total
+    return {
+        "predicted_label": predicted_label,
+        "labeled_samples": total,
+        "correct": correct,
+        "accuracy": accuracy,
+    }
+
+
+def mean_rgb(samples):
+    rgb_values = [sample["color_rgb"] for sample in samples if sample.get("color_rgb") is not None]
+    channels = [0.0, 0.0, 0.0]
+    for value in rgb_values:
+        channels[0] += value["r"]
+        channels[1] += value["g"]
+        channels[2] += value["b"]
+    count = len(rgb_values)
+    return {
+        "r": channels[0] / count,
+        "g": channels[1] / count,
+        "b": channels[2] / count,
+    }
+
+
+def evaluate_rgb_mean(samples, rgb_mean):
+    labeled = [sample for sample in samples if sample.get("color_rgb") is not None]
+    absolute_errors = []
+    euclidean_distances = []
+    for sample in labeled:
+        target = sample["color_rgb"]
+        delta_r = abs(target["r"] - rgb_mean["r"])
+        delta_g = abs(target["g"] - rgb_mean["g"])
+        delta_b = abs(target["b"] - rgb_mean["b"])
+        absolute_errors.append((delta_r + delta_g + delta_b) / 3.0)
+        euclidean_distances.append(
+            math.sqrt(
+                (target["r"] - rgb_mean["r"]) ** 2
+                + (target["g"] - rgb_mean["g"]) ** 2
+                + (target["b"] - rgb_mean["b"]) ** 2
+            )
+        )
+    total = len(labeled)
+    return {
+        "predicted_mean_rgb": rgb_mean,
+        "labeled_samples": total,
+        "mean_absolute_error_per_channel": None if total == 0 else sum(absolute_errors) / total,
+        "mean_euclidean_distance": None if total == 0 else sum(euclidean_distances) / total,
+    }
+
+
+def main():
+    args = parse_args()
+    hf_root = Path(args.hf_root)
+    train_targets = load_json(hf_root / "property_prediction" / "train" / "targets.json")
+    test_targets = load_json(hf_root / "property_prediction" / "test" / "targets.json")
+
+    summary = {
+        "benchmark": "property_prediction",
+        "baseline": "majority_class_and_mean_rgb",
+        "train_samples": len(train_targets),
+        "test_samples": len(test_targets),
+        "classification": {},
+        "regression": {},
+    }
+
+    for field_name in ("transparency", "surface", "color_family"):
+        label, count, labeled_total = majority_label(train_targets, field_name)
+        summary["classification"][field_name] = {
+            "train_majority_label": label,
+            "train_majority_count": count,
+            "train_labeled_samples": labeled_total,
+            "test_metrics": evaluate_majority(test_targets, field_name, label),
+        }
+
+    rgb_mean = mean_rgb(train_targets)
+    summary["regression"]["color_rgb"] = evaluate_rgb_mean(test_targets, rgb_mean)
+
+    write_output(dump_json(summary, args.compact), args.output)
+
+
+if __name__ == "__main__":
+    main()

image_generation/README.md

@@ -0,0 +1,45 @@
+# Image Generation Benchmark
+
+This subfolder contains the canonical train/test split for the glaze image generation benchmark.
+
+## Files
+
+- `train/images/`
+- `train/metadata.json`
+- `train/recipes.json`
+- `train/targets.json`
+- `train/sample_ids.json`
+- `test/images/`
+- `test/metadata.json`
+- `test/recipes.json`
+- `test/targets.json`
+- `test/sample_ids.json`
+- `dataset_statistics.json`
+
+## Current canonical counts
+
+- Train total: 4,490
+- Test total: 443
+- Train transparency labels: 2,968
+- Test transparency labels: 344
+- Train surface labels: 3,381
+- Test surface labels: 331
+- Train color family labels: 4,483
+- Test color family labels: 443
+
+## Benchmark usage
+
+This track supports conditional image generation from glaze-related conditioning signals, including:
+
+- RGB color targets
+- transparency labels when available
+- surface labels when available
+- recipe composition and ingredient lists
+- firing condition metadata when available
+
+## Notes
+
+- The image generation benchmark is separate from the larger property prediction benchmark.
+- `metadata.json` is the richest entry point for multimodal loading because it links images, attributes, and recipe-side information.
+- Missing `transparency` or `surface` values should be treated as unlabeled rather than negative labels.
+- A minimal retrieval-style reference is provided in `../baselines/image_generation_baseline.py`.

image_generation/dataset_statistics.json

@@ -0,0 +1,21 @@
+{
+  "dataset_name": "try_image2",
+  "creation_date": "2026-02-10",
+  "description": "图片生成任务benchmark - 二次标注作为测试集，一次标注作为训练集",
+  "train": {
+    "total_samples": 4490,
+    "successfully_copied_images": 4490,
+    "failed": 0,
+    "samples_with_valid_rgb": 4490
+  },
+  "test": {
+    "total_samples": 443,
+    "successfully_copied_images": 443,
+    "failed": 0,
+    "samples_with_valid_rgb": 443
+  },
+  "total": {
+    "samples": 4933,
+    "images": 4933
+  }
+}

image_generation/test/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14389ce1b7c040e6bd379a8a33018a740092e1481cffa03a73c01bc99a854378
+size 50726583

image_generation/test/sample_ids.json

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property_prediction/README.md

@@ -0,0 +1,55 @@
+# Property Prediction Benchmark
+
+This subfolder contains the canonical fixed split for the glaze property prediction benchmark.
+
+## Files
+
+- `train/targets.json`
+- `train/recipes.json`
+- `train/metadata.json`
+- `test/targets.json`
+- `test/recipes.json`
+- `test/metadata.json`
+- `test_ids.json`
+
+## Tasks
+
+### Transparency
+
+- Type: 4-class classification
+- Labels: `Opaque`, `Semi-opaque`, `Translucent`, `Transparent`
+
+### Surface
+
+- Type: 9-class classification
+- Labels: `Glossy`, `Semi-glossy`, `Satin`, `Satin-matte`, `Matte`, `Semi-matte`, `Smooth Matte`, `Dry Matte`, `Stony Matte`
+
+### Color family
+
+- Type: 9-class classification
+- Labels: `Black`, `Blue`, `Gray`, `Green`, `Orange`, `Purple`, `Red`, `White`, `Yellow`
+
+### Color RGB
+
+- Type: 3-target regression
+- Fields: `color_rgb.r`, `color_rgb.g`, `color_rgb.b`
+
+## Current canonical counts
+
+- Train total: 16,781
+- Test total: 4,903
+- Train transparency labels: 9,023
+- Test transparency labels: 3,322
+- Train surface labels: 9,378
+- Test surface labels: 3,730
+- Train color family labels: 16,781
+- Test color family labels: 4,903
+
+## Notes
+
+- This export intentionally uses `targets.json` as the canonical target file.
+- In this shared package, `train/recipes.json` and `train/metadata.json` are filtered to the canonical target IDs so the split is internally aligned.
+- Historical or intermediate variants are excluded from this package.
+- The split is fixed and should not be regenerated when reporting benchmark results.
+- `metadata.json` is included for traceability and optional multimodal extensions, but the benchmark labels live in `targets.json`.
+- A minimal executable reference is provided in `../baselines/property_prediction_baseline.py`.

property_prediction/test_ids.json

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property_prediction/train/recipes.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6d332596cbdd4156cb3d96c63f99093243b545699a311dd834c7102fca03e7d
+size 23320807

raw_html.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d4df8b392352f90737c8338762d3920d5c6ee21cc7343cb2502a0b802c621e1
+size 283040109

source_records/README.md

@@ -0,0 +1,110 @@
+# Source Records
+
+This folder stores supplemental source-side information for the packaged benchmark samples.
+
+## Contents
+
+- `by_id/<id>.json`: one merged JSON record per packaged sample ID.
+- `html_metadata.json`: filtered HTML-derived metadata for packaged sample IDs.
+- `summary.json`: coverage summary for the supplemental export.
+
+## Why this exists
+
+The canonical benchmark splits are intentionally task-oriented. They are good for training and evaluation, but some users may want richer provenance fields from the original Glazy pages, such as:
+
+- page title
+- author name
+- author URL
+- long-form description
+- page-level status
+- HTML-derived UMF and related ratios
+
+The per-sample JSON files expose these fields without forcing users to parse the raw HTML themselves.
+
+## Relationship to canonical benchmark files
+
+These files are supplemental. The benchmark-facing train/test splits remain the authoritative interface for evaluation.
+
+## Reader script
+
+Use `../tools/read_source_record.py` to inspect the packaged source-side data without writing your own loader.
+
+The script supports both quick one-off inspection and small batch exports.
+
+### What it can do
+
+- Read one or many sample IDs.
+- Read IDs from a text file.
+- Return the full merged sample record.
+- Return shortcuts such as `title`, `author`, `description`, `umf`, and `raw_html_path`.
+- Drill into nested fields with a dotted `--path` lookup.
+- Preview the raw HTML directly, with optional tag stripping.
+- Export results as `json`, `jsonl`, or plain text.
+- Save output to a file instead of stdout.
+
+### Common examples
+
+Quick summary:
+
+```bash
+python huggingface/tools/read_source_record.py --summary
+```
+
+List the first 10 packaged sample IDs:
+
+```bash
+python huggingface/tools/read_source_record.py --list-sample-ids 10
+```
+
+Read the full merged record for one sample:
+
+```bash
+python huggingface/tools/read_source_record.py --sample-id 100018 --field full
+```
+
+Read a convenience field:
+
+```bash
+python huggingface/tools/read_source_record.py --sample-id 100018 --field umf
+python huggingface/tools/read_source_record.py --sample-id 100018 --field html_metadata
+python huggingface/tools/read_source_record.py --sample-id 100018 --field title
+python huggingface/tools/read_source_record.py --sample-id 100018 --field raw_html_path
+```
+
+Read a nested path inside the selected field:
+
+```bash
+python huggingface/tools/read_source_record.py --sample-id 100018 --field full --path property_prediction.test.recipe.umf
+python huggingface/tools/read_source_record.py --sample-id 100018 --field full --path html_metadata.title
+```
+
+Preview the raw HTML:
+
+```bash
+python huggingface/tools/read_source_record.py --sample-id 100018 --html-preview-chars 500
+python huggingface/tools/read_source_record.py --sample-id 100018 --html-preview-chars 500 --html-preview-strip-tags
+```
+
+Read multiple samples at once:
+
+```bash
+python huggingface/tools/read_source_record.py --sample-id 100018 10009 10023 --field title
+```
+
+Read sample IDs from a file:
+
+```bash
+python huggingface/tools/read_source_record.py --sample-id-file sample_ids.txt --field raw_html_path --output-format jsonl
+```
+
+Export to a file:
+
+```bash
+python huggingface/tools/read_source_record.py --sample-id 100018 10009 --field full --output-format jsonl --output out/source_records.jsonl
+```
+
+### Notes
+
+- `--field umf` returns the first available UMF found in the merged sample record, preferring split-aligned recipe data and then HTML-derived metadata.
+- `--path` is applied after `--field` selection, so `--field full --path image_generation.train.metadata.image_path` works as expected.
+- For batch usage, `jsonl` is usually the easiest format to consume downstream.

source_records/by_id.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4e6d13739357c2ccf506fb21530928bb7546849eb74974ad28e8ef8557d2e49
+size 42204886

source_records/html_metadata.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a621211d3e867aa48dacbbd7c3b02b057097b32113c4d0cbf6814fb0fb0f3e3
+size 36608506

source_records/summary.json

@@ -0,0 +1,8 @@
+{
+  "total_unique_samples": 21691,
+  "copied_raw_html_count": 21691,
+  "missing_raw_html_count": 0,
+  "missing_raw_html_ids": [],
+  "html_metadata_count": 21684,
+  "source_records_count": 21691
+}

tools/read_source_record.py

@@ -0,0 +1,352 @@
+"""Convenience reader for the supplemental Hugging Face source bundle.
+
+This script lives inside the published `huggingface/` folder so users can read
+source-side data directly from the exported package without depending on the
+rest of the repository layout.
+"""
+
+import argparse
+import json
+import re
+import sys
+from pathlib import Path
+
+
+HF_ROOT = Path(__file__).resolve().parents[1]
+ROOT = HF_ROOT.parent
+SOURCE_RECORDS_DIR = HF_ROOT / "source_records"
+SOURCE_RECORDS_BY_ID_DIR = SOURCE_RECORDS_DIR / "by_id"
+SUMMARY_PATH = SOURCE_RECORDS_DIR / "summary.json"
+
+
+def flatten_sample_ids(sample_id_args):
+    sample_ids = []
+    for item in sample_id_args or []:
+        sample_ids.extend(item)
+    return [str(sample_id) for sample_id in sample_ids]
+
+
+def load_json(path: Path):
+    with open(path, "r", encoding="utf-8") as handle:
+        return json.load(handle)
+
+
+def load_sample_ids_from_file(path: Path):
+    sample_ids = []
+    with open(path, "r", encoding="utf-8") as handle:
+        for line in handle:
+            sample_id = line.strip()
+            if sample_id:
+                sample_ids.append(sample_id)
+    return sample_ids
+
+
+def strip_html_tags(text: str):
+    text = re.sub(r"<script.*?</script>", " ", text, flags=re.IGNORECASE | re.DOTALL)
+    text = re.sub(r"<style.*?</style>", " ", text, flags=re.IGNORECASE | re.DOTALL)
+    text = re.sub(r"<[^>]+>", " ", text)
+    text = re.sub(r"\s+", " ", text)
+    return text.strip()
+
+
+def walk_path(value, dotted_path: str):
+    current = value
+    for part in dotted_path.split("."):
+        if isinstance(current, dict):
+            if part not in current:
+                raise KeyError(part)
+            current = current[part]
+            continue
+        if isinstance(current, list):
+            index = int(part)
+            current = current[index]
+            continue
+        raise KeyError(part)
+    return current
+
+
+def first_nonempty(values):
+    for value in values:
+        if value not in (None, "", [], {}):
+            return value
+    return None
+
+
+def extract_preferred_umf(record):
+    candidates = []
+    for dataset_name in ("property_prediction", "image_generation"):
+        dataset_block = record.get(dataset_name, {})
+        for split_name in ("train", "test"):
+            split_block = dataset_block.get(split_name)
+            if not split_block:
+                continue
+            recipe = split_block.get("recipe") or {}
+            metadata = split_block.get("metadata") or {}
+            candidates.append(recipe.get("umf"))
+            candidates.append(metadata.get("umf"))
+    html_metadata = record.get("html_metadata") or {}
+    if isinstance(html_metadata, dict):
+        candidates.append(html_metadata.get("umf"))
+    return first_nonempty(candidates)
+
+
+def extract_shortcut(record, field_name: str):
+    html_metadata = record.get("html_metadata") or {}
+    if field_name == "title":
+        return first_nonempty([
+            html_metadata.get("title") if isinstance(html_metadata, dict) else None,
+        ])
+    if field_name == "author":
+        return first_nonempty([
+            html_metadata.get("author") if isinstance(html_metadata, dict) else None,
+        ])
+    if field_name == "description":
+        return first_nonempty([
+            html_metadata.get("description") if isinstance(html_metadata, dict) else None,
+            html_metadata.get("notes") if isinstance(html_metadata, dict) else None,
+        ])
+    if field_name == "umf":
+        return extract_preferred_umf(record)
+    raise KeyError(field_name)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Read supplemental GlazyBench source-side data from the Hugging Face export."
+    )
+    parser.add_argument(
+        "--sample-id",
+        nargs="+",
+        action="append",
+        help="One or more sample IDs to read from huggingface/source_records/by_id.",
+    )
+    parser.add_argument(
+        "--sample-id-file",
+        help="Optional text file with one sample ID per line for batch reading.",
+    )
+    parser.add_argument(
+        "--field",
+        choices=[
+            "full",
+            "memberships",
+            "property_prediction",
+            "image_generation",
+            "html_metadata",
+            "raw_html_path",
+            "title",
+            "author",
+            "description",
+            "umf",
+        ],
+        default="full",
+        help="Which part of the merged record to print.",
+    )
+    parser.add_argument(
+        "--dataset",
+        choices=["property_prediction", "image_generation"],
+        help="Optional dataset selector when printing dataset-specific content.",
+    )
+    parser.add_argument(
+        "--split",
+        choices=["train", "test"],
+        help="Optional split selector when printing dataset-specific content.",
+    )
+    parser.add_argument(
+        "--summary",
+        action="store_true",
+        help="Print source bundle summary instead of an individual sample.",
+    )
+    parser.add_argument(
+        "--list-sample-ids",
+        type=int,
+        default=0,
+        help="Print the first N sample IDs available in source_records/by_id.",
+    )
+    parser.add_argument(
+        "--list-fields",
+        action="store_true",
+        help="Print the top-level fields available in a sample record.",
+    )
+    parser.add_argument(
+        "--path",
+        help="Optional dotted path lookup inside the selected record value, for example property_prediction.test.recipe.umf.",
+    )
+    parser.add_argument(
+        "--html-preview-chars",
+        type=int,
+        default=0,
+        help="Also print the first N characters of the raw HTML file for the sample.",
+    )
+    parser.add_argument(
+        "--html-preview-strip-tags",
+        action="store_true",
+        help="When previewing HTML, strip tags into rough plain text.",
+    )
+    parser.add_argument(
+        "--output-format",
+        choices=["json", "jsonl", "text"],
+        default="json",
+        help="Output format for batch or single-sample exports.",
+    )
+    parser.add_argument(
+        "--output",
+        help="Optional output file path. If omitted, prints to stdout.",
+    )
+    parser.add_argument(
+        "--compact",
+        action="store_true",
+        help="Print compact JSON instead of pretty JSON.",
+    )
+    return parser.parse_args()
+
+
+def render_json(value, compact: bool):
+    if compact:
+        return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
+    return json.dumps(value, ensure_ascii=False, indent=2)
+
+
+def write_output(text: str, output_path: str | None):
+    if output_path:
+        path = Path(output_path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w", encoding="utf-8") as handle:
+            handle.write(text)
+        return
+    print(text)
+
+
+def list_sample_ids(limit: int):
+    sample_ids = sorted(path.stem for path in SOURCE_RECORDS_BY_ID_DIR.glob("*.json"))
+    if limit > 0:
+        sample_ids = sample_ids[:limit]
+    return sample_ids
+
+
+def get_record(sample_id: str):
+    path = SOURCE_RECORDS_BY_ID_DIR / f"{sample_id}.json"
+    if not path.exists():
+        raise FileNotFoundError(f"Sample record not found: {path}")
+    return load_json(path), path
+
+
+def select_value(record, args):
+    if args.field == "full":
+        value = record
+    elif args.field == "memberships":
+        value = record.get("memberships", [])
+    elif args.field == "html_metadata":
+        value = record.get("html_metadata")
+    elif args.field == "raw_html_path":
+        raw_relpath = record.get("raw_html_relpath")
+        value = None if raw_relpath is None else str(HF_ROOT / raw_relpath)
+    elif args.field in {"title", "author", "description", "umf"}:
+        value = extract_shortcut(record, args.field)
+    else:
+        dataset_block = record.get(args.field, {})
+        if args.split:
+            value = dataset_block.get(args.split)
+        elif args.dataset and args.dataset != args.field:
+            value = record.get(args.dataset, {})
+        else:
+            value = dataset_block
+    if args.path:
+        value = walk_path(value, args.path)
+    return value
+
+
+def maybe_get_html_preview(record, chars: int, strip_tags: bool):
+    if chars <= 0:
+        return None
+    raw_relpath = record.get("raw_html_relpath")
+    if raw_relpath is None:
+        return "<missing raw html>"
+    html_path = HF_ROOT / raw_relpath
+    if not html_path.exists():
+        return f"<file missing: {html_path}>"
+    with open(html_path, "r", encoding="utf-8", errors="ignore") as handle:
+        preview = handle.read(chars)
+    if strip_tags:
+        preview = strip_html_tags(preview)
+    return preview
+
+
+def build_sample_payload(sample_id: str, args):
+    record, record_path = get_record(str(sample_id))
+
+    if args.list_fields:
+        payload = {
+            "sample_id": str(sample_id),
+            "record_path": str(record_path),
+            "top_level_fields": sorted(record.keys()),
+            "memberships": record.get("memberships", []),
+        }
+    else:
+        value = select_value(record, args)
+        payload = {
+            "sample_id": str(sample_id),
+            "record_path": str(record_path),
+            "selected_field": args.field,
+            "value": value,
+        }
+
+    html_preview = maybe_get_html_preview(record, args.html_preview_chars, args.html_preview_strip_tags)
+    if html_preview is not None:
+        payload["raw_html_preview"] = html_preview
+    return payload
+
+
+def render_payloads(payloads, args):
+    if args.output_format == "jsonl":
+        return "\n".join(render_json(payload, compact=True) for payload in payloads)
+    if args.output_format == "text":
+        lines = []
+        for payload in payloads:
+            lines.append(f"sample_id={payload.get('sample_id')}")
+            lines.append(f"record_path={payload.get('record_path')}")
+            if "selected_field" in payload:
+                lines.append(f"selected_field={payload['selected_field']}")
+                lines.append(f"value={payload.get('value')}")
+            else:
+                lines.append(f"top_level_fields={payload.get('top_level_fields')}")
+            if "raw_html_preview" in payload:
+                lines.append("raw_html_preview=")
+                lines.append(str(payload["raw_html_preview"]))
+            lines.append("")
+        return "\n".join(lines).rstrip()
+    if len(payloads) == 1:
+        return render_json(payloads[0], args.compact)
+    return render_json(payloads, args.compact)
+
+
+def main():
+    args = parse_args()
+
+    if args.summary:
+        write_output(render_json(load_json(SUMMARY_PATH), args.compact), args.output)
+        return
+
+    if args.list_sample_ids:
+        payload = {
+            "count": len(list_sample_ids(0)),
+            "sample_ids": list_sample_ids(args.list_sample_ids),
+        }
+        write_output(render_json(payload, args.compact), args.output)
+        return
+
+    sample_ids = flatten_sample_ids(args.sample_id)
+    if args.sample_id_file:
+        sample_ids.extend(load_sample_ids_from_file(Path(args.sample_id_file)))
+    if not sample_ids:
+        raise SystemExit("Provide --sample-id, --sample-id-file, --summary, or --list-sample-ids.")
+
+    payloads = [build_sample_payload(sample_id, args) for sample_id in sample_ids]
+    write_output(render_payloads(payloads, args), args.output)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except FileNotFoundError as exc:
+        print(str(exc), file=sys.stderr)
+        raise SystemExit(1)