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scibert_token_classifier

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metadata
base_model:
  - allenai/scibert_scivocab_uncased
datasets:
  - noystl/Recombination-Extraction
language:
  - en
library_name: transformers
license: cc
pipeline_tag: feature-extraction

This Hugging Face repository contains a fine-tuned allenai/scibert_scivocab_uncased model trained for the task of extracting recombination examples from scientific abstracts, as described in the paper CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature. The model can be used for the information extraction task of identifying recombination examples within scientific text. For detailed usage instructions and reproduction of results, please refer to the Github repository linked above.

Non-Default Hyperparameters

  • per_device_train_batch_size: 1
  • max_steps: 500
  • weight_decay: 0.1
  • learning_rate: 6.e-5

Bibtex

@misc{sternlicht2025chimeraknowledgebaseidea,
      title={CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature}, 
      author={Noy Sternlicht and Tom Hope},
      year={2025},
      eprint={2505.20779},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20779}, 
}

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