---
library_name: transformers
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: 0.0488
- Accuracy: 0.8207
- Precision: 0.9268
- Recall: 0.8840
- F1: 0.9030

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0242        | 0.9971 | 173  | 0.0552          | 0.8452   | 0.8964    | 0.8905 | 0.8883 |
| 0.0298        | 2.0    | 347  | 0.0488          | 0.8207   | 0.9268    | 0.8840 | 0.9030 |
| 0.0236        | 2.9971 | 520  | 0.0484          | 0.8214   | 0.9338    | 0.8680 | 0.8971 |
| 0.0298        | 4.0    | 694  | 0.0498          | 0.8251   | 0.9357    | 0.8719 | 0.9004 |
| 0.0232        | 4.9971 | 867  | 0.0477          | 0.8281   | 0.9381    | 0.8732 | 0.9020 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1