NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2

This model is a fine-tuned version of ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT on the dataset provided by SocialDisNER shared task, it is available at: https://temu.bsc.es/socialdisner/category/data/.

It achieves the following results on the evaluation set:

Loss: 0.1483
Precision: 0.8699
Recall: 0.8722
F1: 0.8711
Accuracy: 0.9771

Model description

For a complete description of our system, please go to: https://aclanthology.org/2022.smm4h-1.6.pdf

Training and evaluation data

Dataset provided by SocialDisNER shared task, it is available at: https://temu.bsc.es/socialdisner/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
No log	1.0	467	0.0851	0.8415	0.8209	0.8310	0.9720
0.1011	2.0	934	0.1034	0.8681	0.8464	0.8571	0.9744
0.0537	3.0	1401	0.1094	0.8527	0.8608	0.8568	0.9753
0.0335	4.0	1868	0.1239	0.8617	0.8603	0.8610	0.9751
0.0185	5.0	2335	0.1192	0.8689	0.8627	0.8658	0.9756
0.0112	6.0	2802	0.1426	0.8672	0.8663	0.8667	0.9765
0.0067	7.0	3269	0.1483	0.8699	0.8722	0.8711	0.9771

How to cite this work:

Tamayo, A., Gelbukh, A., & Burgos, D. A. (2022, October). Nlp-cic-wfu at socialdisner: Disease mention extraction in spanish tweets using transfer learning and search by propagation. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task (pp. 19-22).

@inproceedings{tamayo2022nlp, title={Nlp-cic-wfu at socialdisner: Disease mention extraction in spanish tweets using transfer learning and search by propagation}, author={Tamayo, Antonio and Gelbukh, Alexander and Burgos, Diego A}, booktitle={Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task}, pages={19--22}, year={2022} }

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1