NLP-CIC-WFU_Clinical_Cases_NER_Sents_tokenized_mBERT_cased_fine_tuned
This model is a fine-tuned version of
bert-base-multilingual-cased on the LivingNER shared task 2022 dataset. It is available at:
https://temu.bsc.es/livingner/category/data/It achieves the following results on the evaluation set:
- Loss: 0.0546
- Precision: 0.8574
- Recall: 0.7366
- F1: 0.7924
- Accuracy: 0.9893
Model description
For a complete description of our system, please go to:
https://ceur-ws.org/Vol-3202/livingner-paper13.pdfTraining and evaluation data
Dataset provided by LivingNER shared task, it is available at:
https://temu.bsc.es/livingner/category/data/Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|
| 0.0505 | 1.0 | 2568 | 0.0434 | 0.9399 | 0.6781 | 0.7878 | 0.9886 |
| 0.0393 | 2.0 | 5136 | 0.0450 | 0.9384 | 0.6947 | 0.7984 | 0.9892 |
| 0.0306 | 3.0 | 7704 | 0.0451 | 0.9497 | 0.6951 | 0.8027 | 0.9897 |
| 0.0266 | 4.0 | 10272 | 0.0422 | 0.9646 | 0.6904 | 0.8048 | 0.9900 |
| 0.0208 | 5.0 | 12840 | 0.0494 | 0.9576 | 0.6969 | 0.8067 | 0.9902 |
| 0.0141 | 6.0 | 15408 | 0.0506 | 0.8407 | 0.7352 | 0.7844 | 0.9890 |
| 0.0093 | 7.0 | 17976 | 0.0546 | 0.8574 | 0.7366 | 0.7924 | 0.9893 |
How to cite this work:
Tamayo, A., Burgos, D., & Gelbukh, A. (2022). ParTNER: Paragraph Tuning for Named Entity Recognition on Clinical Cases in Spanish using mBERT+ Rules. In CEUR Workshop Proceedings (Vol. 3202). CEUR-WS.
@inproceedings{tamayo2022partner,
title={ParTNER: Paragraph Tuning for Named Entity Recognition on Clinical Cases in Spanish using mBERT+ Rules},
author={Tamayo, Antonio and Burgos, Diego and Gelbukh, Alexander}
}
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
