Upload checkpoint from parser_v4_5_gold_from_giant_hf

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library_name: pytorch
tags:
  - bibr
  - reference-parsing
  - token-classification
  - crf
  - modernbert

bibr Parser v4.5 Gold

ModernBERT + feature-gated CRF reference-field parser for bibr's REF_PARSE_STRATEGY=ner path.

Training

Encoder: answerdotai/ModernBERT-base
Initial checkpoint: parser_giant_v4_fixed/best.pt
Fine-tune corpus: data/parser_v4_5_gold
Train rows: 16,459
Validation rows: 4,031
Tags: 39 BIO parser tags
Max sequence length: 256
Tokenizer convention: add_special_tokens=False

Validation

Best training validation micro entity F1: 0.9653892730.

Same-split compatibility check against bibr's runtime all-zero token features:

Checkpoint	Feature Mode	Validation Entity F1
`parser_giant_v4_fixed/best.pt`	zero features	`0.0543892926`
this checkpoint	true features	`0.9651912333`
this checkpoint	zero features	`0.9652572991`
this checkpoint	no features (`None`)	`0.8276018305`

bibr's runtime parser should continue passing all-zero token_features, not None, because the feature projection bias is part of the trained forward path.

Files

best.pt - PyTorch state dict for FeatureGatedEncoderCRF
metrics.json - training validation summary
report.txt - per-field validation classification report
tag_config.json - tag metadata from bibr_training.tags