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--- |
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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_LabelNorm_0404 |
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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_LabelNorm_0404 |
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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: 0.0340 |
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- Precision: 0.6966 |
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- Recall: 0.6039 |
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- F1: 0.6469 |
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- Accuracy: 0.9907 |
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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: 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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| 0.2699 | 0.08 | 25 | 0.1037 | 0.0 | 0.0 | 0.0 | 0.9838 | |
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| 0.0844 | 0.15 | 50 | 0.0821 | 0.0 | 0.0 | 0.0 | 0.9838 | |
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| 0.0776 | 0.23 | 75 | 0.0761 | 0.0 | 0.0 | 0.0 | 0.9838 | |
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| 0.0767 | 0.31 | 100 | 0.0680 | 0.0 | 0.0 | 0.0 | 0.9838 | |
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| 0.0541 | 0.39 | 125 | 0.0602 | 0.6392 | 0.2054 | 0.3110 | 0.9856 | |
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| 0.0586 | 0.46 | 150 | 0.0553 | 0.7209 | 0.1447 | 0.2411 | 0.9851 | |
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| 0.051 | 0.54 | 175 | 0.0508 | 0.3651 | 0.4265 | 0.3934 | 0.9837 | |
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| 0.0325 | 0.62 | 200 | 0.0449 | 0.4337 | 0.3307 | 0.3753 | 0.9858 | |
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| 0.0429 | 0.7 | 225 | 0.0454 | 0.5562 | 0.4272 | 0.4833 | 0.9874 | |
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| 0.0357 | 0.77 | 250 | 0.0448 | 0.5351 | 0.3502 | 0.4233 | 0.9855 | |
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| 0.0542 | 0.85 | 275 | 0.0420 | 0.6611 | 0.2778 | 0.3912 | 0.9872 | |
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| 0.0396 | 0.93 | 300 | 0.0379 | 0.5781 | 0.4755 | 0.5218 | 0.9882 | |
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| 0.0354 | 1.01 | 325 | 0.0417 | 0.5083 | 0.6195 | 0.5584 | 0.9877 | |
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| 0.0326 | 1.08 | 350 | 0.0378 | 0.5295 | 0.4677 | 0.4967 | 0.9875 | |
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| 0.0277 | 1.16 | 375 | 0.0413 | 0.7018 | 0.3424 | 0.4603 | 0.9882 | |
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| 0.0429 | 1.24 | 400 | 0.0431 | 0.4924 | 0.5767 | 0.5312 | 0.9869 | |
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| 0.025 | 1.32 | 425 | 0.0368 | 0.6256 | 0.5331 | 0.5756 | 0.9891 | |
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| 0.0364 | 1.39 | 450 | 0.0353 | 0.6193 | 0.5354 | 0.5743 | 0.9882 | |
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| 0.0321 | 1.47 | 475 | 0.0366 | 0.6695 | 0.4949 | 0.5691 | 0.9895 | |
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| 0.0238 | 1.55 | 500 | 0.0340 | 0.5968 | 0.5230 | 0.5574 | 0.9895 | |
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| 0.0289 | 1.63 | 525 | 0.0320 | 0.6191 | 0.5907 | 0.6045 | 0.9900 | |
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| 0.0272 | 1.7 | 550 | 0.0325 | 0.5938 | 0.6257 | 0.6093 | 0.9898 | |
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| 0.028 | 1.78 | 575 | 0.0316 | 0.6309 | 0.6 | 0.6151 | 0.9903 | |
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| 0.025 | 1.86 | 600 | 0.0364 | 0.6781 | 0.3852 | 0.4913 | 0.9891 | |
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| 0.0235 | 1.93 | 625 | 0.0323 | 0.6543 | 0.6467 | 0.6505 | 0.9903 | |
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| 0.0256 | 2.01 | 650 | 0.0320 | 0.7196 | 0.5292 | 0.6099 | 0.9904 | |
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| 0.0182 | 2.09 | 675 | 0.0337 | 0.6773 | 0.6436 | 0.6600 | 0.9910 | |
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| 0.0179 | 2.17 | 700 | 0.0319 | 0.6592 | 0.6218 | 0.6400 | 0.9905 | |
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| 0.0171 | 2.24 | 725 | 0.0340 | 0.6966 | 0.6039 | 0.6469 | 0.9907 | |
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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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