bert-model-disaster-tweets-classification
This model is a fine-tuned version of 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
