---
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: 0.0952
- Precision: 0.7390
- Recall: 0.7504
- F1: 0.7447
- Accuracy: 0.9701

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5183        | 0.96  | 25   | 0.2791          | 0.0       | 0.0    | 0.0    | 0.9291   |
| 0.1921        | 1.92  | 50   | 0.1466          | 0.5556    | 0.0430 | 0.0799 | 0.9310   |
| 0.1093        | 2.88  | 75   | 0.0965          | 0.7052    | 0.5559 | 0.6218 | 0.9638   |
| 0.073         | 3.85  | 100  | 0.0931          | 0.6361    | 0.8485 | 0.7271 | 0.9625   |
| 0.0605        | 4.81  | 125  | 0.0812          | 0.7513    | 0.7539 | 0.7526 | 0.9693   |
| 0.0397        | 5.77  | 150  | 0.0967          | 0.6809    | 0.7126 | 0.6964 | 0.9685   |
| 0.0339        | 6.73  | 175  | 0.0952          | 0.7390    | 0.7504 | 0.7447 | 0.9701   |


### Framework versions

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