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.pdf

Training 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