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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_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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# SETH_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: 0.0910
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- Precision: 0.8062
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- Recall: 0.7659
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- F1: 0.7855
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- Accuracy: 0.9765
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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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| 0.7275 | 0.96 | 25 | 0.2746 | 0.0 | 0.0 | 0.0 | 0.9293 |
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| 0.1794 | 1.92 | 50 | 0.1296 | 0.6835 | 0.3270 | 0.4424 | 0.9572 |
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| 0.1018 | 2.88 | 75 | 0.0915 | 0.7093 | 0.7349 | 0.7219 | 0.9691 |
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| 0.0769 | 3.85 | 100 | 0.0881 | 0.6844 | 0.8434 | 0.7556 | 0.9671 |
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| 0.0674 | 4.81 | 125 | 0.0875 | 0.6478 | 0.8675 | 0.7417 | 0.9678 |
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| 0.0497 | 5.77 | 150 | 0.0814 | 0.7543 | 0.7504 | 0.7524 | 0.9716 |
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| 0.0441 | 6.73 | 175 | 0.0801 | 0.7756 | 0.8090 | 0.7919 | 0.9746 |
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| 0.0369 | 7.69 | 200 | 0.0818 | 0.7989 | 0.7728 | 0.7857 | 0.9767 |
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| 0.0266 | 8.65 | 225 | 0.0910 | 0.8062 | 0.7659 | 0.7855 | 0.9765 |
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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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