readme: ---
  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"
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # 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](https://huggingface.co/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
  