---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_bert-base-uncased
  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_bert-base-uncased

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: 0.1808
- Accuracy: 0.9261
- Precision: 0.9343
- Recall: 0.9443
- F1: 0.9393

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5507        | 0.09  | 50   | 0.3325          | 0.8642   | 0.9461    | 0.8224 | 0.8799 |
| 0.3193        | 0.18  | 100  | 0.3217          | 0.8701   | 0.9665    | 0.8135 | 0.8835 |
| 0.2726        | 0.28  | 150  | 0.2532          | 0.8918   | 0.9091    | 0.9125 | 0.9108 |
| 0.2143        | 0.37  | 200  | 0.2203          | 0.9146   | 0.9281    | 0.9310 | 0.9295 |
| 0.2134        | 0.46  | 250  | 0.2371          | 0.9162   | 0.9035    | 0.9645 | 0.9330 |
| 0.2234        | 0.55  | 300  | 0.2027          | 0.9178   | 0.9130    | 0.9552 | 0.9336 |
| 0.2139        | 0.64  | 350  | 0.1986          | 0.9194   | 0.9125    | 0.9587 | 0.9351 |
| 0.2062        | 0.74  | 400  | 0.1853          | 0.9222   | 0.9469    | 0.9232 | 0.9349 |
| 0.1793        | 0.83  | 450  | 0.1953          | 0.9244   | 0.9213    | 0.9567 | 0.9387 |
| 0.1771        | 0.92  | 500  | 0.1808          | 0.9261   | 0.9343    | 0.9443 | 0.9393 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2