NER_EHR_Spanish_model_Mulitlingual_BERT

This model is a fine-tuned version of bert-base-multilingual-cased on the DisTEMIST shared task 2022 dataset. It is available at: https://temu.bsc.es/distemist/category/data/

It achieves the following results on the evaluation set:

Loss: 0.2603
Precision: 0.5637
Recall: 0.5801
F1: 0.5718
Accuracy: 0.9534

Model description

For a complete description of our system, please go to: https://ceur-ws.org/Vol-3180/paper-26.pdf

Training and evaluation data

Dataset provided by DisTEMIST shared task, it is available at: https://temu.bsc.es/distemist/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	71	0.2060	0.5017	0.5540	0.5266	0.9496
No log	2.0	142	0.2163	0.5363	0.5433	0.5398	0.9495
No log	3.0	213	0.2245	0.5521	0.5356	0.5438	0.9514
No log	4.0	284	0.2453	0.5668	0.5985	0.5822	0.9522
No log	5.0	355	0.2433	0.5657	0.5579	0.5617	0.9530
No log	6.0	426	0.2553	0.5762	0.5762	0.5762	0.9536
No log	7.0	497	0.2603	0.5637	0.5801	0.5718	0.9534

How to cite this work:

Tamayo, A., Burgos, D. A., & Gelbukh, A. (2022). mbert and simple post-processing: A baseline for disease mention detection in spanish. In Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings.

@inproceedings{tamayo2022mbert, title={mbert and simple post-processing: A baseline for disease mention detection in spanish}, author={Tamayo, Antonio and Burgos, Diego A and Gelbukh, Alexander}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2022} }

Framework versions

Transformers 4.18.0
Pytorch 1.11.0+cu113
Datasets 2.2.0
Tokenizers 0.12.1