---
license: afl-3.0
metrics:
- accuracy
- code_eval
library_name: sklearn
pipeline_tag: text-classification
tags:
- clim
---


# bert-model-disaster-tweets-classification

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the Natural-Language-Processing-with-Disaster-Tweets dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.82
- F1 Score: 0.82

## Model description

Load BertForSequenceClassification, the pretrained BERT model with a single linear classification layer on top, using an optimizer : incorporates weight decay, which is a regularization technique that helps prevent overfitting during training.

## Intended uses & limitations

Use to classify if a tweet represents a disaster or not.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with epsilon = 1e-8.
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Epoch | Average training loss | Training epoch  | Accuracy | F1   |
|:-----:|:---------------------:|:---------------:|:--------:|:----:|
| 1.0   | 0.47                  | 0:00:49         | 0.82     | 0.82 |
| 2.0   | 0.36                  | 0:00:36         | 0.82     | 0.82 |
| 3.0   | 0.29                  | 0:00:51         | 0.82     | 0.82 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3