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--- |
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license: apache-2.0 |
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base_model: PlanTL-GOB-ES/bsc-bio-ehr-es |
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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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model-index: |
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- name: vih_explainability2 |
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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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# vih_explainability2 |
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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: 0.3811 |
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- Roc Auc: 0.8939 |
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- Ap Score: 0.7760 |
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- Precision: 0.9146 |
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- Recall: 0.8065 |
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- F1: 0.8571 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Roc Auc | Ap Score | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:--------:|:---------:|:------:|:------:| |
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| 0.3644 | 0.8475 | 100 | 0.2350 | 0.8402 | 0.6907 | 0.9028 | 0.6989 | 0.7879 | |
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| 0.1848 | 1.6949 | 200 | 0.2358 | 0.8765 | 0.7168 | 0.8588 | 0.7849 | 0.8202 | |
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| 0.1462 | 2.5424 | 300 | 0.2215 | 0.9021 | 0.7509 | 0.8571 | 0.8387 | 0.8478 | |
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| 0.1105 | 3.3898 | 400 | 0.2671 | 0.8778 | 0.7504 | 0.9114 | 0.7742 | 0.8372 | |
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| 0.079 | 4.2373 | 500 | 0.3124 | 0.8630 | 0.7338 | 0.92 | 0.7419 | 0.8214 | |
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| 0.0248 | 5.0847 | 600 | 0.3464 | 0.8765 | 0.7416 | 0.9 | 0.7742 | 0.8324 | |
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| 0.0127 | 5.9322 | 700 | 0.3822 | 0.8617 | 0.7248 | 0.9079 | 0.7419 | 0.8166 | |
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| 0.0089 | 6.7797 | 800 | 0.3625 | 0.8885 | 0.7674 | 0.9136 | 0.7957 | 0.8506 | |
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| 0.0042 | 7.6271 | 900 | 0.3643 | 0.8885 | 0.7674 | 0.9136 | 0.7957 | 0.8506 | |
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| 0.0052 | 8.4746 | 1000 | 0.3772 | 0.8939 | 0.7760 | 0.9146 | 0.8065 | 0.8571 | |
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| 0.0031 | 9.3220 | 1100 | 0.3811 | 0.8939 | 0.7760 | 0.9146 | 0.8065 | 0.8571 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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