idref-alignment/README.md

IdRef Alignment — Evaluation Script

eval_idref_alignment.py is a self-contained uv script that benchmarks the Humatheque IdRef-Qualinka alignment API against a golden dataset of person → IdRef PPN mappings.

• API under test: idref-linker.smartbiblia.fr (source: gegedenice/humatheque-idref-qualinka-api)
• Evaluation dataset: Geraldine/humatheque-vlm-sudoc-grounded-idref
• Endpoint: POST /align/person

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What it does

For every row of the dataset:

1. Build the document context from the sudoc_record_templated column: title, subtitle, discipline, institution (granting_institution), doctoral_school, degree_type, year (defense_year).
2. Read the golden truth from the idref_persname_ppns column — a list of { "Firstname Lastname": "PPN" } pairs.
3. For each person, POST the name + context to /align/person and compare the returned PPN against the golden PPN.

The request body mirrors the API's AlignPersonRequest (see build_align_payload), including the embedding model used for semantic similarity.

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Metrics

Metric	Definition
Accepted & correct	status == "accepted" and best_ppn == gold — the production decision is right
Accepted decisions	rows where the API returned status == "accepted" (regardless of correctness)
Top-1 correct	best_candidate.ppn == gold — ranking quality, ignoring the accept threshold
Candidate recall	gold PPN appears anywhere in the returned candidates[] — recall of the candidate search
Precision / Recall / F1	of the "accepted" decision
Status breakdown	counts of accepted / ambiguous / low_confidence / not_found / error

Each person also gets a gold_rank (1-based position of the gold PPN in the ranked candidate list, or null if absent).

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Usage

Run directly from the Hub (no clone needed):

uv run https://huggingface.co/datasets/Geraldine/uv-scripts/resolve/main/idref-alignment/eval_idref_alignment.py

Or locally:

uv run eval_idref_alignment.py                       # full dataset, default settings
uv run eval_idref_alignment.py --limit 10 --concurrency 4
uv run eval_idref_alignment.py --embedding-model ""  # lexical similarity instead of embeddings
IDREF_API_KEY=xxx uv run eval_idref_alignment.py     # if the API requires an X-API-Key

Key options

Flag	Default	Description
--api-base	https://idref-linker.smartbiblia.fr	Base URL of the alignment API
--dataset	Geraldine/humatheque-vlm-sudoc-grounded-idref	HF dataset id
--split	train	Dataset split
--embedding-model	sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2	Embedding model (empty string ⇒ lexical similarity)
--max-candidates	20	Max IdRef candidates per name
--max-docs-per-role	20	Max reference docs per role
--reference-top-k	3	Top-k references used as evidence
--limit	0 (all)	Evaluate only the first N documents
--concurrency	4	Max concurrent API requests
--request-timeout	90.0	Per-request HTTP timeout (s)
--out	idref_eval_results.jsonl	Per-person JSONL output
--summary-out	idref_eval_summary.json	Aggregate summary JSON

The API key and base URL can also be set via the IDREF_API_KEY / IDREF_API_BASE environment variables. The deployed API is currently open (no key required).

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Outputs

• idref_eval_results.jsonl — one record per person with name, gold_ppn, status, best_ppn, top1_ppn, top1_score, candidate_ppns, gold_rank, and the correctness flags (accepted_correct, top1_correct, topk_correct).
• idref_eval_summary.json — aggregate metrics + status breakdown.
• A formatted summary table is also printed to the console.

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Notes

• The script follows the API's HTTP→HTTPS redirect automatically and retries failed requests with backoff.
• The dataset thumbnail (image) column is dropped on load to avoid an unnecessary Pillow dependency.
• Embedding-based requests are heavier server-side; for a quick smoke test use --limit and/or --embedding-model "".