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README.md
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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: 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: 0.0952
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- Precision: 0.7390
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- Recall: 0.7504
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- F1: 0.7447
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- Accuracy: 0.9701
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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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| 0.5183 | 0.96 | 25 | 0.2791 | 0.0 | 0.0 | 0.0 | 0.9291 |
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| 0.1921 | 1.92 | 50 | 0.1466 | 0.5556 | 0.0430 | 0.0799 | 0.9310 |
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| 0.1093 | 2.88 | 75 | 0.0965 | 0.7052 | 0.5559 | 0.6218 | 0.9638 |
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| 0.073 | 3.85 | 100 | 0.0931 | 0.6361 | 0.8485 | 0.7271 | 0.9625 |
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| 0.0605 | 4.81 | 125 | 0.0812 | 0.7513 | 0.7539 | 0.7526 | 0.9693 |
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| 0.0397 | 5.77 | 150 | 0.0967 | 0.6809 | 0.7126 | 0.6964 | 0.9685 |
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| 0.0339 | 6.73 | 175 | 0.0952 | 0.7390 | 0.7504 | 0.7447 | 0.9701 |
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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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