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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_0.0001 |
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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_0.0001 |
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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: 0.0162 |
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- Precision: 0.8877 |
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- Recall: 0.8973 |
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- F1: 0.8925 |
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- Accuracy: 0.9971 |
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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: 0.0001 |
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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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| 0.2263 | 1.47 | 25 | 0.0776 | 0.0 | 0.0 | 0.0 | 0.9843 | |
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| 0.05 | 2.94 | 50 | 0.0400 | 0.2868 | 0.4216 | 0.3414 | 0.9872 | |
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| 0.0271 | 4.41 | 75 | 0.0219 | 0.5381 | 0.6486 | 0.5882 | 0.9925 | |
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| 0.0108 | 5.88 | 100 | 0.0132 | 0.8324 | 0.8324 | 0.8324 | 0.9965 | |
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| 0.0029 | 7.35 | 125 | 0.0107 | 0.8934 | 0.9514 | 0.9215 | 0.9979 | |
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| 0.0025 | 8.82 | 150 | 0.0123 | 0.8691 | 0.8973 | 0.8830 | 0.9972 | |
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| 0.0011 | 10.29 | 175 | 0.0127 | 0.8579 | 0.9135 | 0.8848 | 0.9969 | |
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| 0.0006 | 11.76 | 200 | 0.0102 | 0.8969 | 0.9405 | 0.9182 | 0.9981 | |
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| 0.0005 | 13.24 | 225 | 0.0118 | 0.8942 | 0.9135 | 0.9037 | 0.9978 | |
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| 0.0005 | 14.71 | 250 | 0.0106 | 0.8768 | 0.9622 | 0.9175 | 0.9981 | |
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| 0.0015 | 16.18 | 275 | 0.0119 | 0.855 | 0.9243 | 0.8883 | 0.9976 | |
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| 0.0006 | 17.65 | 300 | 0.0134 | 0.8814 | 0.9243 | 0.9024 | 0.9977 | |
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| 0.0004 | 19.12 | 325 | 0.0177 | 0.8617 | 0.8757 | 0.8686 | 0.9969 | |
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| 0.0003 | 20.59 | 350 | 0.0162 | 0.8877 | 0.8973 | 0.8925 | 0.9971 | |
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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 0.13.2 |
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