137 lines
4.6 KiB
Python
137 lines
4.6 KiB
Python
"""Build the Hugging Face model card for a packaged pipeline.
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`spacy package` already writes a README into the wheel, and `spacy huggingface-hub push`
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uploads it as the card. That card is a metadata dump: no install line, no usage, no
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throughput, and no YAML frontmatter, so the Hub cannot index the model by language or task.
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This composes a card from the same sources of truth (`meta.json` and the JSON written by
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scripts/benchmark_throughput.py) rather than from hand-copied numbers, so the card cannot
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drift from the artifact it describes.
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"""
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import argparse
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import json
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from pathlib import Path
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# meta.json key -> (row label, reference note). Only keys the pipeline actually evidences
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# are emitted; a missing key means the corpus could not score it.
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METRICS = [
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("token_acc", "Tokenization accuracy", ""),
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("tag_acc", "XPOS tag accuracy", ""),
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("pos_acc", "UPOS tag accuracy", ""),
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("morph_acc", "Morphological features", ""),
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("lemma_acc", "Lemma accuracy", ""),
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("dep_uas", "Unlabelled attachment (UAS)", ""),
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("dep_las", "Labelled attachment (LAS)", ""),
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("sents_f", "Sentence segmentation F", ""),
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("ents_p", "NER precision", ""),
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("ents_r", "NER recall", ""),
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("ents_f", "NER F-score", ""),
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]
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def load(path):
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return json.loads(Path(path).read_text())
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def throughput_rows(paths):
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rows = []
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for p in paths:
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if not Path(p).exists():
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continue
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d = load(p)
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rows.append((d["device"], d["batch_size"], d["wps_median"]))
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return rows
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--meta", required=True, help="meta.json of the finalized pipeline")
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ap.add_argument("--throughput", nargs="*", default=[], help="benchmark_throughput JSONs")
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ap.add_argument("--repo-id", required=True, help="e.g. Phazel/fa_core_news_trf")
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ap.add_argument("--wheel-name", required=True)
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ap.add_argument("--out", required=True)
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args = ap.parse_args()
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meta = load(args.meta)
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name = f"{meta['lang']}_{meta['name']}"
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perf = meta.get("performance", {})
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lines = []
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# Frontmatter: without this the Hub cannot filter the model by language or library.
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lines += [
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"---",
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"language:",
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"- fa",
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f"license: {meta.get('license', 'cc-by-sa-4.0').lower().replace(' ', '-')}",
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"library_name: spacy",
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"pipeline_tag: token-classification",
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"tags:",
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"- spacy",
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"- token-classification",
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"- persian",
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"- farsi",
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"---",
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"",
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f"# {name}",
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"",
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meta.get("description", "").strip(),
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"",
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]
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lines += [
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"## Install",
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"",
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"```bash",
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f"pip install https://huggingface.co/{args.repo_id}/resolve/main/{args.wheel_name}",
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"```",
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"",
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"```python",
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"import spacy",
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f'nlp = spacy.load("{name}")',
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'doc = nlp("شرکت ایران خودرو اعلام کرد که تولید خود را افزایش می\u200cدهد.")',
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"print([(t.text, t.pos_, t.lemma_, t.dep_) for t in doc])",
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"print([(e.text, e.label_) for e in doc.ents])",
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"```",
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"",
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]
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lines += ["## Accuracy", "",
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"Scored with `spacy benchmark accuracy` on the held-out PerDT test split.",
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"", "| Metric | Score |", "| --- | ---: |"]
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for key, label, _ in METRICS:
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v = perf.get(key)
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if isinstance(v, (int, float)):
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lines.append(f"| {label} | {v * 100:.2f} |")
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lines.append("")
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rows = throughput_rows(args.throughput)
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if rows:
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lines += ["## Throughput", "",
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"Median of repeated `nlp.pipe` passes over the 146-document PerDT test",
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"split (23,825 tokens), timing the pipe only. Warmup pass discarded.",
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"", "| Device | Batch | Words/s |", "| --- | ---: | ---: |"]
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for device, batch, wps in rows:
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lines.append(f"| {device} | {batch} | {wps:,.0f} |")
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lines.append("")
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lines += ["## Sources", "", "| Source | Author | Licence |", "| --- | --- | --- |"]
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for s in meta.get("sources", []):
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url, nm = s.get("url"), s.get("name", "")
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label = f"[{nm}]({url})" if url else nm
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lines.append(f"| {label} | {s.get('author', '')} | {s.get('license', '')} |")
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lines.append("")
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notes = (meta.get("notes") or "").strip()
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if notes:
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lines += ["## Notes", "", notes, ""]
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out = Path(args.out)
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out.parent.mkdir(parents=True, exist_ok=True)
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out.write_text("\n".join(lines), encoding="utf-8")
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print(f"wrote {out} ({out.stat().st_size} bytes)")
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if __name__ == "__main__":
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main()
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