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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_0.0001_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SETH_0.0001_250
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.
It achieves the following results on the evaluation set:
- Loss: 0.0681
- Precision: 0.7818
- Recall: 0.7945
- F1: 0.7881
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2912 | 0.76 | 25 | 0.1275 | 0.8475 | 0.0909 | 0.1642 | 0.9647 |
| 0.0752 | 1.52 | 50 | 0.0588 | 0.6884 | 0.7873 | 0.7345 | 0.9799 |
| 0.0433 | 2.27 | 75 | 0.0603 | 0.6623 | 0.8309 | 0.7371 | 0.9803 |
| 0.0394 | 3.03 | 100 | 0.0516 | 0.6761 | 0.8727 | 0.7619 | 0.9822 |
| 0.0292 | 3.79 | 125 | 0.0534 | 0.7430 | 0.8145 | 0.7771 | 0.9836 |
| 0.0249 | 4.55 | 150 | 0.0520 | 0.7384 | 0.8109 | 0.7730 | 0.9828 |
| 0.0196 | 5.3 | 175 | 0.0618 | 0.7442 | 0.8145 | 0.7778 | 0.9833 |
| 0.0165 | 6.06 | 200 | 0.0604 | 0.7538 | 0.8182 | 0.7847 | 0.9846 |
| 0.0131 | 6.82 | 225 | 0.0613 | 0.7788 | 0.7745 | 0.7767 | 0.9843 |
| 0.0095 | 7.58 | 250 | 0.0681 | 0.7818 | 0.7945 | 0.7881 | 0.9850 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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