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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: multiCorp_5e-05_250 |
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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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# multiCorp_5e-05_250 |
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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: 0.0691 |
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- Precision: 0.6768 |
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- Recall: 0.5971 |
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- F1: 0.6344 |
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- Accuracy: 0.9855 |
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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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- training_steps: 1500 |
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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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| 0.4757 | 0.34 | 50 | 0.1963 | 0.0 | 0.0 | 0.0 | 0.9740 | |
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| 0.1585 | 0.68 | 100 | 0.1299 | 0.3375 | 0.1049 | 0.1600 | 0.9758 | |
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| 0.1224 | 1.01 | 150 | 0.1121 | 0.3719 | 0.3094 | 0.3377 | 0.9750 | |
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| 0.1003 | 1.35 | 200 | 0.0954 | 0.4297 | 0.3167 | 0.3647 | 0.9791 | |
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| 0.0903 | 1.69 | 250 | 0.0920 | 0.4213 | 0.3063 | 0.3547 | 0.9786 | |
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| 0.0735 | 2.03 | 300 | 0.0795 | 0.4882 | 0.4575 | 0.4724 | 0.9814 | |
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| 0.0636 | 2.36 | 350 | 0.0769 | 0.5188 | 0.4718 | 0.4942 | 0.9820 | |
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| 0.0633 | 2.7 | 400 | 0.0737 | 0.5296 | 0.4926 | 0.5104 | 0.9823 | |
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| 0.0598 | 3.04 | 450 | 0.0735 | 0.5844 | 0.4320 | 0.4968 | 0.9827 | |
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| 0.0479 | 3.38 | 500 | 0.0730 | 0.5797 | 0.5264 | 0.5518 | 0.9831 | |
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| 0.0492 | 3.72 | 550 | 0.0680 | 0.6086 | 0.4978 | 0.5477 | 0.9838 | |
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| 0.041 | 4.05 | 600 | 0.0672 | 0.6190 | 0.5667 | 0.5917 | 0.9842 | |
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| 0.0371 | 4.39 | 650 | 0.0672 | 0.6616 | 0.5693 | 0.6120 | 0.9851 | |
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| 0.0362 | 4.73 | 700 | 0.0665 | 0.6670 | 0.5711 | 0.6153 | 0.9852 | |
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| 0.0334 | 5.07 | 750 | 0.0700 | 0.6532 | 0.5468 | 0.5953 | 0.9848 | |
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| 0.0288 | 5.41 | 800 | 0.0670 | 0.6482 | 0.5628 | 0.6025 | 0.9849 | |
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| 0.0288 | 5.74 | 850 | 0.0698 | 0.6643 | 0.5745 | 0.6162 | 0.9851 | |
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| 0.0263 | 6.08 | 900 | 0.0717 | 0.6827 | 0.5845 | 0.6298 | 0.9856 | |
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| 0.0231 | 6.42 | 950 | 0.0712 | 0.6826 | 0.5702 | 0.6213 | 0.9852 | |
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| 0.0238 | 6.76 | 1000 | 0.0691 | 0.6768 | 0.5971 | 0.6344 | 0.9855 | |
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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 2.11.0 |
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- Tokenizers 0.13.2 |
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