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--- |
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license: mit |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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widget: |
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- text: Prince Raoden went to Elantris. [SEP] Elantris is a great city. |
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--- |
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# bert-base-cased-NER-reranker |
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A BERT model trained on the synthetic literary NER context retrieval dataset [Amalvy et. al, 2023](https://aclanthology.org/2023.emnlp-main.642/). |
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To use this model, construct a text of the form **NER-sentence [SEP] context-sentence**. |
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The model should predict the positive class if **context-sentence** is useful to predict **NER-sentence**, and the negative class otherwise. |
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# Performance Metrics |
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The model obtains 98.34 F1 on the synthetic test set. |
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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. |
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# How to Reproduce Training |
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See the training script [here](https://github.com/CompNet/conivel/blob/gen/train_reranker.py). |
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# Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@InProceedings{Amalvy2023, |
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title = {Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset}, |
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author = {Amalvy, A. and Labatut, V. and Dufour, R.}, |
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booktitle = {2023 Conference on Empirical Methods in Natural Language Processing}, |
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year = {2023}, |
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doi = {10.18653/v1/2023.emnlp-main.642}, |
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pages = {10372-10382}, |
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} |
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``` |
