---
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_FULL
  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_FULL

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: 0.5701
- Accuracy: 0.8552
- F1: 0.8469
- Precision: 0.8499
- Recall: 0.8466

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.8254        | 0.14  | 100  | 2.1467          | 0.4831   | 0.4014 | 0.4638    | 0.4628 |
| 1.7026        | 0.28  | 200  | 1.0972          | 0.7123   | 0.6713 | 0.6981    | 0.6939 |
| 1.207         | 0.42  | 300  | 0.9107          | 0.7524   | 0.7200 | 0.7407    | 0.7315 |
| 1.0468        | 0.56  | 400  | 0.7801          | 0.7861   | 0.7586 | 0.7829    | 0.7676 |
| 1.0953        | 0.71  | 500  | 0.8004          | 0.7852   | 0.7649 | 0.7778    | 0.7685 |
| 1.0857        | 0.85  | 600  | 0.8224          | 0.7678   | 0.7367 | 0.7775    | 0.7507 |
| 0.9787        | 0.99  | 700  | 0.6742          | 0.8111   | 0.7968 | 0.8069    | 0.7976 |
| 0.7944        | 1.13  | 800  | 0.6627          | 0.8222   | 0.8068 | 0.8162    | 0.8077 |
| 0.8078        | 1.27  | 900  | 0.6222          | 0.8306   | 0.8194 | 0.8260    | 0.8187 |
| 0.8101        | 1.41  | 1000 | 0.6620          | 0.8279   | 0.8158 | 0.8276    | 0.8155 |
| 0.7668        | 1.55  | 1100 | 0.5905          | 0.8439   | 0.8361 | 0.8400    | 0.8356 |
| 0.7133        | 1.69  | 1200 | 0.6085          | 0.8328   | 0.8231 | 0.8297    | 0.8225 |
| 0.7621        | 1.84  | 1300 | 0.6039          | 0.8375   | 0.8293 | 0.8368    | 0.8283 |
| 0.6461        | 1.98  | 1400 | 0.6176          | 0.8392   | 0.8298 | 0.8356    | 0.8292 |
| 0.5791        | 2.12  | 1500 | 0.5925          | 0.8524   | 0.8439 | 0.8497    | 0.8440 |
| 0.602         | 2.26  | 1600 | 0.5877          | 0.8473   | 0.8389 | 0.8441    | 0.8382 |
| 0.5459        | 2.4   | 1700 | 0.5888          | 0.8534   | 0.8460 | 0.8528    | 0.8459 |
| 0.569         | 2.54  | 1800 | 0.6118          | 0.8467   | 0.8393 | 0.8448    | 0.8387 |
| 0.5138        | 2.68  | 1900 | 0.5797          | 0.8561   | 0.8474 | 0.8520    | 0.8474 |
| 0.5427        | 2.82  | 2000 | 0.5678          | 0.8578   | 0.8498 | 0.8531    | 0.8493 |
| 0.5437        | 2.97  | 2100 | 0.5701          | 0.8552   | 0.8469 | 0.8499    | 0.8466 |


### Framework versions

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