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license: mit |
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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: Variome_2e-05_29_03 |
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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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# Variome_2e-05_29_03 |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0928 |
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- Precision: 0.5437 |
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- Recall: 0.4211 |
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- F1: 0.4746 |
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- Accuracy: 0.9852 |
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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: 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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- training_steps: 500 |
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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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| 1.1552 | 5.0 | 25 | 0.1636 | 0.0 | 0.0 | 0.0 | 0.9794 | |
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| 0.1671 | 10.0 | 50 | 0.1345 | 0.0 | 0.0 | 0.0 | 0.9794 | |
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| 0.1297 | 15.0 | 75 | 0.1076 | 0.2683 | 0.0827 | 0.1264 | 0.9806 | |
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| 0.0995 | 20.0 | 100 | 0.1047 | 0.24 | 0.1353 | 0.1731 | 0.9810 | |
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| 0.0845 | 25.0 | 125 | 0.0987 | 0.2289 | 0.1429 | 0.1759 | 0.9813 | |
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| 0.0722 | 30.0 | 150 | 0.1001 | 0.2558 | 0.1654 | 0.2009 | 0.9816 | |
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| 0.0642 | 35.0 | 175 | 0.0994 | 0.3117 | 0.1805 | 0.2286 | 0.9821 | |
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| 0.0564 | 40.0 | 200 | 0.0938 | 0.3204 | 0.2481 | 0.2797 | 0.9817 | |
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| 0.0481 | 45.0 | 225 | 0.0935 | 0.4070 | 0.2632 | 0.3196 | 0.9833 | |
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| 0.0416 | 50.0 | 250 | 0.0913 | 0.4167 | 0.3383 | 0.3734 | 0.9836 | |
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| 0.0363 | 55.0 | 275 | 0.0911 | 0.4653 | 0.3534 | 0.4017 | 0.9847 | |
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| 0.0321 | 60.0 | 300 | 0.0909 | 0.4495 | 0.3684 | 0.4050 | 0.9842 | |
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| 0.0293 | 65.0 | 325 | 0.0918 | 0.5361 | 0.3910 | 0.4522 | 0.9852 | |
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| 0.0269 | 70.0 | 350 | 0.0936 | 0.5444 | 0.3684 | 0.4395 | 0.9853 | |
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| 0.0251 | 75.0 | 375 | 0.0936 | 0.5833 | 0.4211 | 0.4891 | 0.9858 | |
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| 0.0242 | 80.0 | 400 | 0.0920 | 0.5534 | 0.4286 | 0.4831 | 0.9854 | |
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| 0.0232 | 85.0 | 425 | 0.0928 | 0.5612 | 0.4135 | 0.4762 | 0.9855 | |
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| 0.0216 | 90.0 | 450 | 0.0928 | 0.5437 | 0.4211 | 0.4746 | 0.9852 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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