thesanogoeffect/bibr-parser-v1

bibr-parser-v1

ModernBERT-base + CRF reference parser for the bibr scientific-paper extraction pipeline. Takes a single segmented reference string and emits BIO field labels over 19 field types (TITLE, AUTHOR, YEAR, CONTAINER, VOLUME, ISSUE, PAGES, DOI, URL, ARXIV, PMID, PUBLISHER, EDITOR, EDITION, SERIES, ACCESSDATE, NOTE, PAGERANGESTART, PAGERANGE_END).

• —Architecture: answerdotai/ModernBERT-base encoder + linear head + torchcrf.CRF decode with BIO transition constraints.
• —Tag set: 39 BIO tags (see bibr/ner/tags.py).
• —Training corpus: gold-anchored, judge-corrected references from psych250 + Directorate-General Economics + MDPI Social Sciences (~440 papers, 2026-05-09/10).

Held-out validation (2026-05-10)

• —DOI: 43% → 94% vs the prior silver-trained checkpoint.
• —Year / title / authors: ≥95%.

psych250 eval (50 papers, 2026-05-11, vs gold)

• —ref_matching_f1: 0.969 (LLM baseline: 1.000 — partially circular, gold derived from LLM with judge corrections).
• —ref_title_acc: 0.995.
• —ref_year_acc: 0.997.
• —ref_count_ratio: 0.988.
• —ref_doi_recall (raw): 0.004 — URL-encoded DOI corner case in the gold corpus; Crossref enrichment recovers 75.7%.

Loading

from bibr.ner.parser import RefParser
parser = RefParser("thesanogoeffect/bibr-parser-v1")
fields = parser.parse(one_reference_string)

The RefParser resolver downloads the checkpoint via huggingface_hub.hf_hub_download (filename: parser_v3_gold.pt).

Caveats

• —In-distribution: APA-style social-science references.
• —Out-of-distribution: arXiv [N] Author... CoRR, abs/... patterns — container coverage drops to ~65% (vs 94% LLM). Fix path: expand gold corpus.
• —URL-encoded DOIs (info%3Adoi%2F10...) are not in the training distribution; rely on downstream Crossref enrichment for those.