license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: >-
NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2
results: []
widget:
- text: "Desperté del coma con una inquietud espiritual, que me llevó a mirar al cielo y a encontrar la paz, entrevista a Piki\_Pfaff https://t.co/JgXnDrXjLN https://t.co/95eVVQOfZo"
- text: "Efectividad y seguridad a largo plazo de la implantación de un stent microbypass trabecular en la cirugía de cataratas: 5 años de\_resultados https://t.co/tO71HYeCLh https://t.co/mnMGhMNtwx"
- text: >-
Tuitea con #gotasdesolidaridad y brindemos nuestro apoyo a los pacientes y
familiares en el cáncer de mamá @Solan_de_Cabras Uniros a compartirlo
@azuchristeamo y @luismi12c https://t.co/TgQizz2kpT
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