---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: BERT-political_bias-finetune
  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. -->

# BERT-political_bias-finetune

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0945
- Accuracy: 0.9890
- Precision: 0.9962
- Recall: 0.9875
- F1: 0.9918

## 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: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2651        | 0.1834 | 500  | 0.5210          | 0.8885   | 0.9981    | 0.8363 | 0.9101 |
| 0.1807        | 0.3668 | 1000 | 0.1013          | 0.9846   | 0.9913    | 0.9859 | 0.9885 |
| 0.0872        | 0.5503 | 1500 | 0.0693          | 0.9879   | 0.9860    | 0.9962 | 0.9911 |
| 0.0775        | 0.7337 | 2000 | 0.1155          | 0.9787   | 0.9961    | 0.9723 | 0.9840 |
| 0.0751        | 0.9171 | 2500 | 0.0530          | 0.9901   | 0.9945    | 0.9908 | 0.9926 |
| 0.033         | 1.1005 | 3000 | 0.0505          | 0.9930   | 0.9956    | 0.9940 | 0.9948 |
| 0.0403        | 1.2839 | 3500 | 0.0447          | 0.9905   | 0.9908    | 0.9951 | 0.9929 |
| 0.0395        | 1.4674 | 4000 | 0.0857          | 0.9868   | 0.9967    | 0.9837 | 0.9901 |
| 0.0077        | 1.6508 | 4500 | 0.0708          | 0.9912   | 0.9903    | 0.9967 | 0.9935 |
| 0.0342        | 1.8342 | 5000 | 0.0945          | 0.9890   | 0.9962    | 0.9875 | 0.9918 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1