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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: DISO_bsc_test |
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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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# DISO_bsc_test |
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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: 0.0959 |
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- Diso Precision: 0.7766 |
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- Diso Recall: 0.7803 |
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- Diso F1: 0.7784 |
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- Diso Number: 4552 |
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- Overall Precision: 0.7766 |
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- Overall Recall: 0.7803 |
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- Overall F1: 0.7784 |
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- Overall Accuracy: 0.9744 |
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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 | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0849 | 1.0 | 2799 | 0.0734 | 0.7360 | 0.7676 | 0.7515 | 4552 | 0.7360 | 0.7676 | 0.7515 | 0.9726 | |
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| 0.0572 | 2.0 | 5598 | 0.0736 | 0.7674 | 0.7768 | 0.7721 | 4552 | 0.7674 | 0.7768 | 0.7721 | 0.9743 | |
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| 0.0462 | 3.0 | 8397 | 0.0836 | 0.7737 | 0.7707 | 0.7722 | 4552 | 0.7737 | 0.7707 | 0.7722 | 0.9736 | |
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| 0.0318 | 4.0 | 11196 | 0.0959 | 0.7766 | 0.7803 | 0.7784 | 4552 | 0.7766 | 0.7803 | 0.7784 | 0.9744 | |
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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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