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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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- accuracy |
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model-index: |
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- name: biobert |
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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 |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4906 |
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- Accuracy: 0.9444 |
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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: 32 |
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- eval_batch_size: 256 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| No log | 1.0 | 791 | 0.2279 | 0.9384 | |
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| 0.1997 | 2.0 | 1582 | 0.3086 | 0.9326 | |
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| 0.0772 | 3.0 | 2373 | 0.3142 | 0.9305 | |
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| 0.0504 | 4.0 | 3164 | 0.3149 | 0.9417 | |
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| 0.0504 | 5.0 | 3955 | 0.3344 | 0.9414 | |
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| 0.0367 | 6.0 | 4746 | 0.3333 | 0.9430 | |
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| 0.0245 | 7.0 | 5537 | 0.3671 | 0.9409 | |
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| 0.0204 | 8.0 | 6328 | 0.4249 | 0.9395 | |
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| 0.0134 | 9.0 | 7119 | 0.3557 | 0.9456 | |
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| 0.0134 | 10.0 | 7910 | 0.4586 | 0.9384 | |
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| 0.0109 | 11.0 | 8701 | 0.5423 | 0.9374 | |
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| 0.0087 | 12.0 | 9492 | 0.4680 | 0.9458 | |
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| 0.0052 | 13.0 | 10283 | 0.4594 | 0.9458 | |
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| 0.0071 | 14.0 | 11074 | 0.5178 | 0.9389 | |
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| 0.0071 | 15.0 | 11865 | 0.4706 | 0.9421 | |
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| 0.0056 | 16.0 | 12656 | 0.4917 | 0.9435 | |
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| 0.0034 | 17.0 | 13447 | 0.4678 | 0.9447 | |
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| 0.0026 | 18.0 | 14238 | 0.4793 | 0.9447 | |
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| 0.0023 | 19.0 | 15029 | 0.4869 | 0.9458 | |
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| 0.0023 | 20.0 | 15820 | 0.4906 | 0.9444 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu118 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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