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

# BERT_Text_classification_noisy

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: 0.4823
- Accuracy: 0.8880
- F1: 0.8776
- Precision: 0.8835
- Recall: 0.8779

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.7338        | 0.24  | 50   | 1.4138          | 0.7076   | 0.6300 | 0.5892    | 0.6828 |
| 0.9336        | 0.48  | 100  | 0.5276          | 0.8361   | 0.8305 | 0.8407    | 0.8303 |
| 0.5885        | 0.71  | 150  | 0.4815          | 0.8603   | 0.8541 | 0.8583    | 0.8525 |
| 0.6172        | 0.95  | 200  | 0.5176          | 0.8777   | 0.8648 | 0.8712    | 0.8664 |
| 0.5229        | 1.19  | 250  | 0.4818          | 0.8809   | 0.8709 | 0.8769    | 0.8707 |
| 0.4757        | 1.43  | 300  | 0.4845          | 0.8827   | 0.8720 | 0.8786    | 0.8722 |
| 0.4286        | 1.67  | 350  | 0.4231          | 0.8854   | 0.8759 | 0.8776    | 0.8758 |
| 0.4837        | 1.9   | 400  | 0.4763          | 0.8907   | 0.8794 | 0.8864    | 0.8799 |
| 0.4031        | 2.14  | 450  | 0.4539          | 0.8880   | 0.8766 | 0.8833    | 0.8773 |
| 0.4305        | 2.38  | 500  | 0.4775          | 0.8858   | 0.8752 | 0.8806    | 0.8755 |
| 0.3538        | 2.62  | 550  | 0.4863          | 0.8880   | 0.8771 | 0.8853    | 0.8776 |
| 0.325         | 2.86  | 600  | 0.4823          | 0.8880   | 0.8776 | 0.8835    | 0.8779 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2