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

# output

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: 0.0837
- Accuracy: 0.9865
- Precision: 0.9937
- Recall: 0.9809
- F1: 0.9873

## 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: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0168        | 0.1380 | 250  | 0.0997          | 0.9851   | 0.9841    | 0.9881 | 0.9861 |
| 0.1           | 0.2759 | 500  | 0.0789          | 0.9873   | 0.9897    | 0.9866 | 0.9881 |
| 0.0522        | 0.4139 | 750  | 0.0811          | 0.9870   | 0.9861    | 0.9897 | 0.9879 |
| 0.1133        | 0.5519 | 1000 | 0.0837          | 0.9865   | 0.9937    | 0.9809 | 0.9873 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1