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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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model-index: |
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- name: ner_ANAT_DISO |
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results: [] |
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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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# ner_ANAT_DISO |
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This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0746 |
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- Anat Precision: 0.6512 |
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- Anat Recall: 0.6573 |
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- Anat F1: 0.6542 |
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- Anat Number: 534 |
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- Diso Precision: 0.8727 |
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- Diso Recall: 0.8844 |
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- Diso F1: 0.8785 |
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- Diso Number: 2915 |
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- Overall Precision: 0.8385 |
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- Overall Recall: 0.8492 |
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- Overall F1: 0.8438 |
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- Overall Accuracy: 0.9838 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 2e-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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0625 | 1.0 | 1682 | 0.0591 | 0.5407 | 0.6723 | 0.5993 | 534 | 0.8516 | 0.8624 | 0.8570 | 2915 | 0.7945 | 0.8330 | 0.8133 | 0.9808 | |
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| 0.0397 | 2.0 | 3364 | 0.0633 | 0.6237 | 0.6798 | 0.6505 | 534 | 0.8576 | 0.8820 | 0.8696 | 2915 | 0.8196 | 0.8507 | 0.8348 | 0.9826 | |
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| 0.0181 | 3.0 | 5046 | 0.0698 | 0.6452 | 0.6948 | 0.6691 | 534 | 0.8670 | 0.8878 | 0.8773 | 2915 | 0.8312 | 0.8579 | 0.8443 | 0.9833 | |
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| 0.0121 | 4.0 | 6728 | 0.0746 | 0.6512 | 0.6573 | 0.6542 | 534 | 0.8727 | 0.8844 | 0.8785 | 2915 | 0.8385 | 0.8492 | 0.8438 | 0.9838 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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