| --- |
| 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 |
|
|
