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

# results

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3788
- Accuracy: 0.9531
- Precision: 0.9573
- Recall: 0.9531
- F1: 0.9529

## 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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3597        | 1.0   | 16   | 1.0019          | 0.8906   | 0.9092    | 0.8906 | 0.8899 |
| 0.6676        | 2.0   | 32   | 0.5230          | 0.9219   | 0.9342    | 0.9219 | 0.9206 |
| 0.4248        | 3.0   | 48   | 0.3788          | 0.9531   | 0.9573    | 0.9531 | 0.9529 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.15.2