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Citation Classifier

A DistilBERT-based binary classifier that identifies sentences in biomedical text that require citations.

Model Description

This model takes a sentence from a scientific/biomedical article and predicts whether it should contain a citation (1) or not (0). It is a key component of the pubciter pipeline for automated citation generation.

Base model: distilbert-base-uncased
Task: Binary text classification
Domain: Biomedical / scientific literature

Variants

coteaching/ — Trained with co-teaching strategy for noise-robust learning
self_filtering/ — Trained with self-filtering for label noise reduction
last-checkpoint/ — Standard training final checkpoint

Training

Dataset: vgainullin/xciting_data — PubMed sentences annotated for citation presence
Samples: 100k balanced (50k cited, 50k uncited)
Epochs: 3
Learning rate: 1e-6
Batch size: 16 (train), 64 (eval)
Optimizer: AdamW

vgainullin
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citation_classifier

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Citation Classifier

Model Description

Variants

Training

Usage

Citation

Dataset used to train vgainullin/citation_classifier