---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_5e-05_0404_ES6_strict_tok
  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_tok

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: 0.0824
- Precision: 0.7891
- Recall: 0.7470
- F1: 0.7675
- Accuracy: 0.9741

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4311        | 0.96  | 25   | 0.1785          | 0.7       | 0.0120 | 0.0237 | 0.9354   |
| 0.1235        | 1.92  | 50   | 0.0961          | 0.6732    | 0.7091 | 0.6907 | 0.9655   |
| 0.0749        | 2.88  | 75   | 0.0858          | 0.6801    | 0.8417 | 0.7523 | 0.9692   |
| 0.063         | 3.85  | 100  | 0.0857          | 0.6764    | 0.8744 | 0.7628 | 0.9666   |
| 0.0521        | 4.81  | 125  | 0.0757          | 0.7419    | 0.7522 | 0.7470 | 0.9723   |
| 0.0336        | 5.77  | 150  | 0.0829          | 0.7170    | 0.7935 | 0.7533 | 0.9714   |
| 0.0287        | 6.73  | 175  | 0.0824          | 0.7891    | 0.7470 | 0.7675 | 0.9741   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3