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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_1e-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_1e-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: 0.1042
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- Precision: 0.6583
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- Recall: 0.8623
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- F1: 0.7466
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- Accuracy: 0.9675
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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: 1e-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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| 0.9667 | 0.96 | 25 | 0.3537 | 0.0 | 0.0 | 0.0 | 0.9293 |
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| 0.2692 | 1.92 | 50 | 0.1917 | 0.0 | 0.0 | 0.0 | 0.9308 |
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| 0.148 | 2.88 | 75 | 0.1300 | 0.5833 | 0.0843 | 0.1474 | 0.9504 |
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| 0.1085 | 3.85 | 100 | 0.1147 | 0.6699 | 0.4819 | 0.5606 | 0.9578 |
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| 0.0998 | 4.81 | 125 | 0.1047 | 0.6534 | 0.6231 | 0.6379 | 0.9607 |
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| 0.0745 | 5.77 | 150 | 0.0901 | 0.6798 | 0.7711 | 0.7226 | 0.9677 |
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| 0.0709 | 6.73 | 175 | 0.0889 | 0.6657 | 0.8296 | 0.7387 | 0.9676 |
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| 0.0614 | 7.69 | 200 | 0.0867 | 0.6753 | 0.8485 | 0.7521 | 0.9681 |
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| 0.0532 | 8.65 | 225 | 0.0851 | 0.6830 | 0.8158 | 0.7435 | 0.9685 |
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| 0.0496 | 9.62 | 250 | 0.0956 | 0.6585 | 0.8296 | 0.7342 | 0.9668 |
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| 0.0429 | 10.58 | 275 | 0.1042 | 0.6583 | 0.8623 | 0.7466 | 0.9675 |
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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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