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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2 |
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results: [] |
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widget: |
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- 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" |
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- 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" |
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- 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" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# NLP-CIC-WFU_SocialDisNER_fine_tuned_NER_EHR_Spanish_model_Mulitlingual_BERT_v2 |
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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/. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1483 |
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- Precision: 0.8699 |
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- Recall: 0.8722 |
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- F1: 0.8711 |
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- Accuracy: 0.9771 |
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## Model description |
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For a complete description of our system, please go to: https://aclanthology.org/2022.smm4h-1.6.pdf |
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## Training and evaluation data |
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Dataset provided by SocialDisNER shared task, it is available at: https://temu.bsc.es/socialdisner/category/data/. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 467 | 0.0851 | 0.8415 | 0.8209 | 0.8310 | 0.9720 | |
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| 0.1011 | 2.0 | 934 | 0.1034 | 0.8681 | 0.8464 | 0.8571 | 0.9744 | |
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| 0.0537 | 3.0 | 1401 | 0.1094 | 0.8527 | 0.8608 | 0.8568 | 0.9753 | |
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| 0.0335 | 4.0 | 1868 | 0.1239 | 0.8617 | 0.8603 | 0.8610 | 0.9751 | |
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| 0.0185 | 5.0 | 2335 | 0.1192 | 0.8689 | 0.8627 | 0.8658 | 0.9756 | |
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| 0.0112 | 6.0 | 2802 | 0.1426 | 0.8672 | 0.8663 | 0.8667 | 0.9765 | |
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| 0.0067 | 7.0 | 3269 | 0.1483 | 0.8699 | 0.8722 | 0.8711 | 0.9771 | |
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### How to cite this work: |
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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). |
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@inproceedings{tamayo2022nlp, |
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title={Nlp-cic-wfu at socialdisner: Disease mention extraction in spanish tweets using transfer learning and search by propagation}, |
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author={Tamayo, Antonio and Gelbukh, Alexander and Burgos, Diego A}, |
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booktitle={Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop \& Shared Task}, |
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pages={19--22}, |
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year={2022} |
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} |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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