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ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT
NER_EHR_Spanish_model_Mulitlingual_BERT is a token classification model from ajtamayoh. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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From the Hugging Face model README
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:
For a complete description of our system, please go to: https://ceur-ws.org/Vol-3180/paper-26.pdf
Dataset provided by DisTEMIST shared task, it is available at: https://temu.bsc.es/distemist/category/data/
The following hyperparameters were used during training:
| 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 |
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} }