
# 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`. If you use the model in a huggingface pipeline, pass `aggregation_strategy="first"`.

## 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
@InProceedings{amalvy:hal-03972448,
  title	       = {{Data Augmentation for Robust Character Detection in
                  Fantasy Novels}},
  author       = {Amalvy, Arthur and Labatut, Vincent and Dufour,
                  Richard},
  url	       = {https://hal.science/hal-03972448},
  booktitle    = {{Workshop on Computational Methods in the Humanities
                  2022}},
  YEAR	       = {2022},
  hal_id       = {hal-03972448},
  hal_version  = {v1},
}
```

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},
}
```
{"language": ["en"], "license": "mit", "library_name": "transformers", "pipeline_tag": "token-classification", "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": [{"text": "Prince Raoden went to Elantris."}]}
transformers pytorch bert token-classification en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# 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'. If you use the model in a huggingface pipeline, pass 'aggregation_strategy="first"'.

## Dataset

We corrected the dataset of Dekker et al. (2019) and added LOC and ORG annotations.

If you use this model in your research, please cite:



The dataset was originally published and annotated by Dekker et al (2019):


# 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'. If you use the model in a huggingface pipeline, pass 'aggregation_strategy="first"'. ## Dataset

We corrected the dataset of Dekker et al. (2019) and added LOC and ORG annotations.

If you use this model in your research, please cite:



The dataset was originally published and annotated by Dekker et al (2019):
TAGS
#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # 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'. If you use the model in a huggingface pipeline, pass 'aggregation_strategy="first"'. ## Dataset

We corrected the dataset of Dekker et al. (2019) and added LOC and ORG annotations.

If you use this model in your research, please cite:



The dataset was originally published and annotated by Dekker et al (2019):
TAGS
#transformers #pytorch #bert #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# 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'. If you use the model in a huggingface pipeline, pass 'aggregation_strategy="first"'.## Dataset

We corrected the dataset of Dekker et al. (2019) and added LOC and ORG annotations.

If you use this model in your research, please cite:



The dataset was originally published and annotated by Dekker et al (2019):