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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_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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# SETH_5e-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: 0.0824 |
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- Precision: 0.7891 |
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- Recall: 0.7470 |
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- F1: 0.7675 |
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- Accuracy: 0.9741 |
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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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| 0.4311 | 0.96 | 25 | 0.1785 | 0.7 | 0.0120 | 0.0237 | 0.9354 | |
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| 0.1235 | 1.92 | 50 | 0.0961 | 0.6732 | 0.7091 | 0.6907 | 0.9655 | |
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| 0.0749 | 2.88 | 75 | 0.0858 | 0.6801 | 0.8417 | 0.7523 | 0.9692 | |
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| 0.063 | 3.85 | 100 | 0.0857 | 0.6764 | 0.8744 | 0.7628 | 0.9666 | |
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| 0.0521 | 4.81 | 125 | 0.0757 | 0.7419 | 0.7522 | 0.7470 | 0.9723 | |
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| 0.0336 | 5.77 | 150 | 0.0829 | 0.7170 | 0.7935 | 0.7533 | 0.9714 | |
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| 0.0287 | 6.73 | 175 | 0.0824 | 0.7891 | 0.7470 | 0.7675 | 0.9741 | |
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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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