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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_0404_ES6_strict_tok |
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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_0404_ES6_strict_tok |
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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: 0.0707 |
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- Precision: 0.5783 |
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- Recall: 0.4750 |
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- F1: 0.5216 |
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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: 2000 |
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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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| 1.1357 | 0.13 | 25 | 0.1875 | 0.0 | 0.0 | 0.0 | 0.9759 | |
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| 0.1839 | 0.26 | 50 | 0.1827 | 0.0 | 0.0 | 0.0 | 0.9759 | |
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| 0.1925 | 0.39 | 75 | 0.1841 | 0.0 | 0.0 | 0.0 | 0.9759 | |
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| 0.1804 | 0.52 | 100 | 0.1797 | 0.0 | 0.0 | 0.0 | 0.9759 | |
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| 0.1677 | 0.65 | 125 | 0.1727 | 0.0 | 0.0 | 0.0 | 0.9759 | |
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| 0.1486 | 0.79 | 150 | 0.1293 | 0.0 | 0.0 | 0.0 | 0.9759 | |
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| 0.1231 | 0.92 | 175 | 0.1203 | 0.1706 | 0.0758 | 0.1050 | 0.9766 | |
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| 0.1011 | 1.05 | 200 | 0.1162 | 0.1591 | 0.0403 | 0.0643 | 0.9766 | |
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| 0.1206 | 1.18 | 225 | 0.1142 | 0.2467 | 0.1420 | 0.1803 | 0.9770 | |
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| 0.1189 | 1.31 | 250 | 0.1085 | 0.2264 | 0.0921 | 0.1310 | 0.9778 | |
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| 0.1086 | 1.44 | 275 | 0.1015 | 0.25 | 0.1958 | 0.2196 | 0.9790 | |
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| 0.0977 | 1.57 | 300 | 0.0948 | 0.2849 | 0.2505 | 0.2666 | 0.9800 | |
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| 0.0901 | 1.7 | 325 | 0.0944 | 0.2966 | 0.2534 | 0.2733 | 0.9796 | |
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| 0.0888 | 1.83 | 350 | 0.0891 | 0.3162 | 0.2543 | 0.2819 | 0.9811 | |
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| 0.0724 | 1.96 | 375 | 0.0920 | 0.4200 | 0.2495 | 0.3131 | 0.9812 | |
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| 0.0773 | 2.09 | 400 | 0.0850 | 0.4561 | 0.3090 | 0.3684 | 0.9826 | |
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| 0.0679 | 2.23 | 425 | 0.0803 | 0.4373 | 0.3378 | 0.3812 | 0.9825 | |
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| 0.0809 | 2.36 | 450 | 0.0871 | 0.4580 | 0.2562 | 0.3286 | 0.9814 | |
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| 0.0667 | 2.49 | 475 | 0.0769 | 0.4281 | 0.3656 | 0.3944 | 0.9835 | |
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| 0.0731 | 2.62 | 500 | 0.0742 | 0.5111 | 0.3752 | 0.4328 | 0.9841 | |
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| 0.0713 | 2.75 | 525 | 0.0724 | 0.5571 | 0.4165 | 0.4767 | 0.9848 | |
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| 0.063 | 2.88 | 550 | 0.0706 | 0.5687 | 0.4367 | 0.4940 | 0.9849 | |
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| 0.0714 | 3.01 | 575 | 0.0733 | 0.5448 | 0.4319 | 0.4818 | 0.9848 | |
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| 0.0572 | 3.14 | 600 | 0.0707 | 0.5783 | 0.4750 | 0.5216 | 0.9852 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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