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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_5e-05_0404_ES6_strict
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_5e-05_0404_ES6_strict
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: 0.0633
- Precision: 0.7953
- Recall: 0.8692
- F1: 0.8306
- Accuracy: 0.9864
## 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: 5e-05
- 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: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3171 | 0.96 | 25 | 0.0921 | 0.6399 | 0.7676 | 0.6980 | 0.9759 |
| 0.0656 | 1.92 | 50 | 0.0588 | 0.7528 | 0.8227 | 0.7862 | 0.9796 |
| 0.04 | 2.88 | 75 | 0.0456 | 0.7641 | 0.8640 | 0.8110 | 0.9837 |
| 0.031 | 3.85 | 100 | 0.0481 | 0.7647 | 0.8726 | 0.8151 | 0.9840 |
| 0.0241 | 4.81 | 125 | 0.0443 | 0.7915 | 0.8623 | 0.8254 | 0.9857 |
| 0.0162 | 5.77 | 150 | 0.0469 | 0.8443 | 0.8399 | 0.8421 | 0.9868 |
| 0.0132 | 6.73 | 175 | 0.0487 | 0.8310 | 0.8296 | 0.8303 | 0.9865 |
| 0.013 | 7.69 | 200 | 0.0545 | 0.7692 | 0.8778 | 0.8199 | 0.9854 |
| 0.0091 | 8.65 | 225 | 0.0539 | 0.8093 | 0.8399 | 0.8243 | 0.9865 |
| 0.0071 | 9.62 | 250 | 0.0691 | 0.7820 | 0.8520 | 0.8155 | 0.9855 |
| 0.0049 | 10.58 | 275 | 0.0633 | 0.7953 | 0.8692 | 0.8306 | 0.9864 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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