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Add complete QASPER Longformer model card
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metadata
language: en
license: mit
library_name: transformers
pipeline_tag: question-answering
base_model: valhalla/longformer-base-4096-finetuned-squadv1
datasets:
  - allenai/qasper
tags:
  - longformer
  - question-answering
  - long-document-qa
  - qasper
  - document-ai

Longformer QASPER Extractive Document QA

This model card is the upload template for the checkpoint produced by notebooks/complete_longformer_training_evaluation_pipeline.ipynb.

Base model

valhalla/longformer-base-4096-finetuned-squadv1

Project fine-tuning

The checkpoint is further fine-tuned by this project on a documented subset of QASPER v0.3 containing only answerable questions with one contiguous extractive span that can be located in the reconstructed paper text. Free-form, yes/no, unanswerable, unresolved, and multi-span annotations are excluded because this model predicts one contiguous span.

Evaluation

The model was evaluated on 200 contiguous-extractive examples from the QASPER validation set.

Model Exact Match Token F1 Evidence Recovery Evidence Token Recall
BERT truncated to 512 tokens 1.50% 7.37% 26.00% 41.34%
Base Longformer with sliding windows 6.00% 16.16% 30.00% 45.88%
QASPER-fine-tuned Longformer 12.50% 26.66% 49.00% 60.14%

Training configuration

  • Training examples: 803
  • Validation examples: 419
  • Evaluation examples: 200
  • Maximum training length: 3,072 tokens
  • Sliding-window stride: 384 tokens
  • Training epochs: 2
  • Learning rate: 1e-5
  • Precision: BF16
  • GPU: NVIDIA GeForce RTX 5090
  • Fine-tuned by this project: Yes

Performance interpretation

The QASPER-fine-tuned Longformer outperformed both the truncated BERT baseline and the original SQuAD-fine-tuned Longformer checkpoint across Exact Match, token-level F1, evidence recovery, and evidence-token recall.

The model remains imperfect. Exact Match is a strict metric, and QASPER questions frequently contain long, technical, and semantically complex answers. The reported values should not be interpreted as production-level reliability.