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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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- accuracy |
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model-index: |
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- name: biobert-all-deep |
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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-all-deep |
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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: 0.8095 |
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- Precision: 0.6591 |
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- Recall: 0.7116 |
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- F1: 0.6843 |
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- Accuracy: 0.8236 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 363 | 0.5639 | 0.5973 | 0.6865 | 0.6388 | 0.8149 | |
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| 0.6983 | 2.0 | 726 | 0.5410 | 0.6263 | 0.7052 | 0.6634 | 0.8238 | |
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| 0.3859 | 3.0 | 1089 | 0.5557 | 0.6544 | 0.7011 | 0.6769 | 0.8245 | |
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| 0.3859 | 4.0 | 1452 | 0.5803 | 0.6579 | 0.7064 | 0.6813 | 0.8276 | |
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| 0.276 | 5.0 | 1815 | 0.6461 | 0.6598 | 0.7105 | 0.6842 | 0.8238 | |
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| 0.1944 | 6.0 | 2178 | 0.6995 | 0.6616 | 0.7120 | 0.6859 | 0.8237 | |
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| 0.1505 | 7.0 | 2541 | 0.7337 | 0.6563 | 0.7195 | 0.6865 | 0.8253 | |
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| 0.1505 | 8.0 | 2904 | 0.7710 | 0.6664 | 0.7120 | 0.6884 | 0.8255 | |
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| 0.1178 | 9.0 | 3267 | 0.8030 | 0.6541 | 0.7165 | 0.6838 | 0.8233 | |
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| 0.1006 | 10.0 | 3630 | 0.8095 | 0.6591 | 0.7116 | 0.6843 | 0.8236 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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