rwgbench-citation-frame-classifier

Anonymous2876/rwgbench-citation-frame-classifier

rwgbench-citation-frame-classifier is a text classification model from Anonymous2876. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

This model is the citation-frame classifier used by RWGBench to compute Citation Frame Alignment (CFA). CFA evaluates whether a generated related work section uses citations with a rhetorical frame distribution simila…

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: deberta-v2
Access: Public
Created: Jul 8, 2026
Updated: Jul 8, 2026
SHA: 7d022407

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Base models

microsoft/deberta-v3-large

Datasets

multicite

Model card

From the Hugging Face model README

RWGBench Citation Frame Classifier

The classifier is a DeBERTa-v3-large sequence-classification model fine-tuned for multi-label citation-function prediction. It predicts seven MultiCite-derived labels:

Label	Intended citation function
`motivation`	motivates the target problem or research direction
`background`	provides general background or prior findings
`uses`	describes methods, data, tools, or results used by the target work
`extends`	indicates that the target work builds on or extends prior work
`similarities`	marks similarity or close relationship to prior work
`differences`	marks contrast, limitation, gap, or distinction
`future_work`	discusses future directions

Intended Use

The model is intended for automatic evaluation and analysis of related work generation systems. In RWGBench, citation contexts are extracted from generated and reference related work sections, classified with this model, aggregated into frame-count profiles, and compared with macro count F1.

It is not intended as a general-purpose judge of paper quality or citation correctness. It should be used together with citation-selection and citation-support metrics.

Files

File	Description
`config.json`, `model.safetensors`	Transformers sequence-classification checkpoint
`tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, `spm.model`, `added_tokens.json`	Tokenizer files
`thresholds.json`	Per-label development thresholds used for multi-label decoding
`metrics.json`	Development and held-out MultiCite evaluation metrics
`label_map.json`	Label mapping used during training
`rwgb_classifier_config.json`	RWGBench-specific usage metadata

Evaluation

Held-out MultiCite test performance:

Metric	Score
Macro F1	0.5585
Micro F1	0.6751
Samples F1	0.6933
Weighted F1	0.6750
Subset accuracy	0.5119

Per-label held-out F1:

Label	F1
`motivation`	0.3523
`background`	0.7756
`uses`	0.7372
`extends`	0.4450
`similarities`	0.5508
`differences`	0.6274
`future_work`	0.4211

The thresholds in thresholds.json are applied after sigmoid probabilities. If no label passes its threshold, RWGBench assigns the highest-probability label so that every citation context contributes at least one frame.

Usage With RWGBench

Download the model into the RWGBench code repository:

models/citation_frame_multicite/
  config.json
  model.safetensors
  tokenizer.json
  thresholds.json
  ...

run:

python src/evaluation/evaluate_generated_results.py \
  --input results/your_results.json \
  --output results/your_eval.json \
  --gold data/gold100_papers.json \
  --corpus data/corpus.json \
  --citation-frame-model-path models/citation_frame_multicite

RWGBench automatically marks numbered citations with <cite>...</cite> before classification, matching the input format used during training.

Task: Text Classification
Library: transformers
Type: deberta-v2
License: mit
Created: Jul 8, 2026
Updated: Jul 8, 2026