---
license: mit
language:
- en
library_name: transformers
pipeline_tag: text-ranking
widget:
- text: Prince Raoden went to Elantris. [SEP] Elantris is a great city.
tags:
- sentence-transformers
---

# bert-base-cased-NER-reranker

A BERT model trained on the synthetic literary NER context retrieval dataset [Amalvy et. al, 2023](https://aclanthology.org/2023.emnlp-main.642/) [(arXiv)](https://arxiv.org/abs/2310.10118).

To use this model, construct a text of the form **NER-sentence [SEP] context-sentence**.
The model should predict the positive class if **context-sentence** is useful to predict **NER-sentence**, and the negative class otherwise.


# Performance Metrics

The model obtains 98.34 F1 on the synthetic test set.
See [Amalvy et. al, 2023](https://aclanthology.org/2023.emnlp-main.642/) for details about NER performance gains when using this retriever model to assit a NER model at inference.


# How to Reproduce Training

See the training script [here](https://github.com/CompNet/conivel/blob/gen/train_reranker.py).


# Citation

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

```bibtex
@InProceedings{Amalvy2023,
  title	       = {Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset},
  author       = {Amalvy, A. and Labatut, V. and Dufour, R.},
  booktitle    = {2023 Conference on Empirical Methods in Natural Language Processing},
  year	       = {2023},
  doi	       = {10.18653/v1/2023.emnlp-main.642},
  pages	       = {10372-10382},
}
```