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--- |
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base_model: dmis-lab/biobert-v1.1 |
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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: biobert-base-pubmed-multilabel |
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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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# biobert-base-pubmed-multilabel |
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co./dmis-lab/biobert-v1.1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2677 |
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- Precision: 0.9044 |
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- Recall: 0.8528 |
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- F1: 0.8778 |
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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: 5e-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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
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| 0.3373 | 0.2 | 500 | 0.2859 | 0.9093 | 0.8006 | 0.8515 | |
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| 0.2781 | 0.4 | 1000 | 0.2748 | 0.8972 | 0.8388 | 0.8670 | |
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| 0.269 | 0.6 | 1500 | 0.2639 | 0.9026 | 0.8405 | 0.8705 | |
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| 0.2598 | 0.81 | 2000 | 0.2610 | 0.9037 | 0.8435 | 0.8726 | |
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| 0.2543 | 1.01 | 2500 | 0.2559 | 0.9052 | 0.8494 | 0.8764 | |
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| 0.2191 | 1.21 | 3000 | 0.2554 | 0.9091 | 0.8437 | 0.8752 | |
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| 0.2217 | 1.41 | 3500 | 0.2620 | 0.8917 | 0.8676 | 0.8795 | |
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| 0.2232 | 1.61 | 4000 | 0.2529 | 0.9070 | 0.8470 | 0.8759 | |
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| 0.2256 | 1.81 | 4500 | 0.2567 | 0.9231 | 0.8176 | 0.8671 | |
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| 0.2191 | 2.02 | 5000 | 0.2591 | 0.8936 | 0.8731 | 0.8832 | |
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| 0.1744 | 2.22 | 5500 | 0.2674 | 0.8978 | 0.8631 | 0.8801 | |
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| 0.1745 | 2.42 | 6000 | 0.2736 | 0.8974 | 0.8566 | 0.8766 | |
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| 0.1749 | 2.62 | 6500 | 0.2677 | 0.9044 | 0.8528 | 0.8778 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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