readme: ---
  license: mit
  language:
  - en
  library_name: transformers
  pipeline_tag: token-classification
  inference:
    parameters:
      aggregation_strategy: "first"
  widget:
  - text: "Prince Raoden went to Elantris."
  ---
  
  # bert-base-cased-literary-NER
  
  A NER model trained on a literary dataset of the first chapter of 40 novels. The model supports the following NER class: `PER`, `ORG` and `LOC`.
  
  ## Dataset
  
  We corrected the dataset of Dekker et al. (2019) and added LOC and ORG annotations.
  
  ## Citation
  
  If you use this model in your research, please cite:
  
  ```bibtex
  @Article{amalvy-2022-bert_meets_dartagnan,
    title	       = {{BERT meets d'Artagnan: Data Augmentation for Robust
                    Character Detection in Novels}},
    author       = {Amalvy, A. and Labatut, V. and Dufour, R.},
    url	       = {https://hal.archives-ouvertes.fr/hal-03617722},
    booktitle    = {{Workshop on Computational Methods in the Humanities
                    (COMHUM)}},
    year	       = {2022},
    journal      = {HAL},
    pages        = {03617722},
  }
  ```
  
  The dataset was originally published and annotated by Dekker et al (2019):
  
  ```bibtex
  @Article{dekker-2019-evaluation_ner_social_networks_novels,
    author       = {Dekker, N. and Kuhn, T. and van Erp, M.},
    journal      = {PeerJ Computer Science},
    title        = {Evaluating named entity recognition tools for extracting social networks from novels},
    year         = {2019},
    pages        = {e189},
    volume       = {5},
    doi          = {10.7717/peerj-cs.189},
  }
  ```