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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: SETH_5e-05_0404_ES6_strict_2 |
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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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# SETH_5e-05_0404_ES6_strict_2 |
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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: 0.0578 |
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- Precision: 0.7121 |
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- Recall: 0.8812 |
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- F1: 0.7877 |
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- Accuracy: 0.9827 |
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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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| 0.38 | 0.96 | 25 | 0.1107 | 0.4376 | 0.4768 | 0.4563 | 0.9653 | |
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| 0.0752 | 1.92 | 50 | 0.0615 | 0.6796 | 0.8468 | 0.7540 | 0.9797 | |
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| 0.0437 | 2.88 | 75 | 0.0502 | 0.7317 | 0.8589 | 0.7902 | 0.9820 | |
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| 0.0334 | 3.85 | 100 | 0.0523 | 0.7228 | 0.8933 | 0.7991 | 0.9820 | |
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| 0.0273 | 4.81 | 125 | 0.0486 | 0.7668 | 0.8657 | 0.8133 | 0.9838 | |
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| 0.0223 | 5.77 | 150 | 0.0474 | 0.7949 | 0.8606 | 0.8264 | 0.9855 | |
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| 0.0152 | 6.73 | 175 | 0.0524 | 0.8569 | 0.7831 | 0.8183 | 0.9855 | |
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| 0.0143 | 7.69 | 200 | 0.0578 | 0.7121 | 0.8812 | 0.7877 | 0.9827 | |
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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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