A BERT model trained on the synthetic literary NER context retrieval dataset
Amalvy et. al, 2023
.
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
for details about NER performance gains when using this retriever model to assit a NER model at inference.
If you use this model in your research, please cite:
@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},
}
Runs of compnet-renard bert-base-cased-NER-reranker on huggingface.co
1
Total runs
0
24-hour runs
0
3-day runs
-3
7-day runs
-6
30-day runs
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