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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_2e-05_0404_ES6_strict_tok1 |
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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_2e-05_0404_ES6_strict_tok1 |
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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: 0.0330 |
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- Precision: 0.8213 |
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- Recall: 0.8629 |
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- F1: 0.8416 |
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- Accuracy: 0.9916 |
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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: 2e-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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| 0.7315 | 0.49 | 25 | 0.2102 | 0.0 | 0.0 | 0.0 | 0.9555 | |
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| 0.1371 | 0.98 | 50 | 0.1021 | 0.0 | 0.0 | 0.0 | 0.9698 | |
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| 0.0836 | 1.47 | 75 | 0.0960 | 0.0 | 0.0 | 0.0 | 0.9725 | |
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| 0.0666 | 1.96 | 100 | 0.0526 | 0.0 | 0.0 | 0.0 | 0.9804 | |
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| 0.0391 | 2.45 | 125 | 0.0521 | 0.7294 | 0.3147 | 0.4397 | 0.9843 | |
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| 0.0252 | 2.94 | 150 | 0.0382 | 0.8630 | 0.6396 | 0.7347 | 0.9899 | |
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| 0.016 | 3.43 | 175 | 0.0452 | 0.6496 | 0.7716 | 0.7053 | 0.9872 | |
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| 0.0145 | 3.92 | 200 | 0.0272 | 0.8730 | 0.8376 | 0.8549 | 0.9923 | |
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| 0.0082 | 4.41 | 225 | 0.0301 | 0.8804 | 0.8223 | 0.8504 | 0.9920 | |
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| 0.0058 | 4.9 | 250 | 0.0330 | 0.8213 | 0.8629 | 0.8416 | 0.9916 | |
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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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