---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7799
- Accuracy: 0.391
- F1: 0.2680
- Precision: 0.4970
- Recall: 0.391
- Mse: 11.53
- Mae: 2.086

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Mse   | Mae   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:-----:|
| 2.0415        | 1.0   | 157  | 1.7799          | 0.391    | 0.2680 | 0.4970    | 0.391  | 11.53 | 2.086 |
| 1.6256        | 2.0   | 314  | 1.6337          | 0.42     | 0.3217 | 0.4871    | 0.42   | 8.176 | 1.694 |
| 1.4552        | 3.0   | 471  | 1.6218          | 0.432    | 0.3467 | 0.4754    | 0.432  | 6.967 | 1.529 |
| 1.2905        | 4.0   | 628  | 1.6459          | 0.437    | 0.3596 | 0.3645    | 0.437  | 6.628 | 1.494 |
| 1.2211        | 5.0   | 785  | 1.6601          | 0.432    | 0.3684 | 0.3584    | 0.432  | 6.314 | 1.458 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3