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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_2e-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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# tmvar_2e-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: 0.0325
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- Precision: 0.7972
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- Recall: 0.8782
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- F1: 0.8357
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- Accuracy: 0.9909
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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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| 0.5778 | 0.49 | 25 | 0.2034 | 0.0 | 0.0 | 0.0 | 0.9555 |
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| 0.1384 | 0.98 | 50 | 0.0953 | 0.0 | 0.0 | 0.0 | 0.9705 |
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| 0.0778 | 1.47 | 75 | 0.0841 | 0.0 | 0.0 | 0.0 | 0.9734 |
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| 0.064 | 1.96 | 100 | 0.0506 | 0.6818 | 0.2284 | 0.3422 | 0.9827 |
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| 0.0368 | 2.45 | 125 | 0.0424 | 0.6318 | 0.6447 | 0.6382 | 0.9882 |
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| 0.0212 | 2.94 | 150 | 0.0360 | 0.7478 | 0.8579 | 0.7991 | 0.9899 |
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| 0.0138 | 3.43 | 175 | 0.0398 | 0.7629 | 0.8985 | 0.8252 | 0.9899 |
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| 0.013 | 3.92 | 200 | 0.0250 | 0.8502 | 0.8934 | 0.8713 | 0.9932 |
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| 0.0079 | 4.41 | 225 | 0.0293 | 0.8579 | 0.8579 | 0.8579 | 0.9925 |
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| 0.0055 | 4.9 | 250 | 0.0325 | 0.7972 | 0.8782 | 0.8357 | 0.9909 |
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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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