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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: tmVar_5e-05_30_03 |
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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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# tmVar_5e-05_30_03 |
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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: 0.0230 |
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- Precision: 0.8677 |
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- Recall: 0.8865 |
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- F1: 0.8770 |
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- Accuracy: 0.9964 |
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## Model description |
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Trained on Token set with max_length=475 |
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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: 500 |
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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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| 0.3602 | 1.39 | 25 | 0.0547 | 0.4823 | 0.3676 | 0.4172 | 0.9851 | |
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| 0.0498 | 2.78 | 50 | 0.0305 | 0.4518 | 0.5568 | 0.4988 | 0.9912 | |
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| 0.0237 | 4.17 | 75 | 0.0198 | 0.6338 | 0.7297 | 0.6784 | 0.9942 | |
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| 0.0089 | 5.56 | 100 | 0.0164 | 0.7895 | 0.8919 | 0.8376 | 0.9960 | |
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| 0.0036 | 6.94 | 125 | 0.0138 | 0.7826 | 0.8757 | 0.8265 | 0.9967 | |
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| 0.0023 | 8.33 | 150 | 0.0148 | 0.8462 | 0.8919 | 0.8684 | 0.9969 | |
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| 0.0012 | 9.72 | 175 | 0.0159 | 0.7890 | 0.9297 | 0.8536 | 0.9966 | |
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| 0.0012 | 11.11 | 200 | 0.0163 | 0.845 | 0.9135 | 0.8779 | 0.9970 | |
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| 0.001 | 12.5 | 225 | 0.0165 | 0.8534 | 0.8811 | 0.8670 | 0.9967 | |
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| 0.0012 | 13.89 | 250 | 0.0215 | 0.8020 | 0.8757 | 0.8372 | 0.9961 | |
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| 0.0008 | 15.28 | 275 | 0.0192 | 0.875 | 0.9081 | 0.8912 | 0.9970 | |
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| 0.0007 | 16.67 | 300 | 0.0192 | 0.875 | 0.9081 | 0.8912 | 0.9970 | |
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| 0.0005 | 18.06 | 325 | 0.0192 | 0.875 | 0.9081 | 0.8912 | 0.9970 | |
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| 0.0009 | 19.44 | 350 | 0.0230 | 0.8677 | 0.8865 | 0.8770 | 0.9964 | |
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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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