---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned_bert_model
  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. -->

# finetuned_bert_model

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0747
- Precision: 0.6614
- Recall: 0.7090
- F1: 0.6844
- Accuracy: 0.9700

## 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: 2e-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
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0571        | 1.0   | 2185 | 0.0775          | 0.6470    | 0.7164 | 0.6800 | 0.9689   |
| 0.0573        | 2.0   | 4370 | 0.0747          | 0.6614    | 0.7090 | 0.6844 | 0.9700   |
| 0.0476        | 3.0   | 6555 | 0.0809          | 0.6638    | 0.7208 | 0.6911 | 0.9690   |
| 0.0405        | 4.0   | 8740 | 0.0870          | 0.6628    | 0.7297 | 0.6946 | 0.9691   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1