---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_0.0001_0404_ES6
  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_0404_ES6

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: 0.0645
- Precision: 0.8013
- Recall: 0.8537
- F1: 0.8267
- Accuracy: 0.9861

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2589        | 0.96  | 25   | 0.0930          | 0.84      | 0.2530 | 0.3889 | 0.9670   |
| 0.0625        | 1.92  | 50   | 0.0580          | 0.7024    | 0.8003 | 0.7482 | 0.9813   |
| 0.041         | 2.88  | 75   | 0.0554          | 0.7890    | 0.7659 | 0.7773 | 0.9814   |
| 0.0318        | 3.85  | 100  | 0.0528          | 0.6951    | 0.8709 | 0.7731 | 0.9814   |
| 0.0254        | 4.81  | 125  | 0.0488          | 0.7601    | 0.8778 | 0.8147 | 0.9846   |
| 0.0174        | 5.77  | 150  | 0.0571          | 0.7669    | 0.7814 | 0.7741 | 0.9833   |
| 0.0144        | 6.73  | 175  | 0.0598          | 0.7744    | 0.8451 | 0.8082 | 0.9838   |
| 0.0135        | 7.69  | 200  | 0.0587          | 0.7530    | 0.8657 | 0.8054 | 0.9848   |
| 0.0074        | 8.65  | 225  | 0.0695          | 0.8162    | 0.8176 | 0.8169 | 0.9859   |
| 0.0087        | 9.62  | 250  | 0.0606          | 0.7746    | 0.8279 | 0.8003 | 0.9848   |
| 0.0063        | 10.58 | 275  | 0.0645          | 0.8013    | 0.8537 | 0.8267 | 0.9861   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2