| --- |
| license: apache-2.0 |
| base_model: bert-large-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - recall |
| - precision |
| model-index: |
| - name: bert-large-uncased-Fake_Reviews_Classifier |
| results: [] |
| --- |
| |
| # bert-large-uncased-Fake_Reviews_Classifier |
|
|
| This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased). |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.5336 |
| - Accuracy: 0.8381 |
| - F1 |
| - Weighted: 0.8142 |
| - Micro: 0.8381 |
| - Macro: 0.6308 |
| - Recall |
| - Weighted: 0.8381 |
| - Micro: 0.8381 |
| - Macro: 0.6090 |
| - Precision |
| - Weighted: 0.8101 |
| - Micro: 0.8381 |
| - Macro: 0.7029 |
|
|
| ## Model description |
|
|
| For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Fake%20Reviews/Fake%20Reviews%20Classification%20-%20BERT-Large%20With%20PEFT.ipynb |
| |
| ## Intended uses & limitations |
| |
| This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. |
| |
| ## Training and evaluation data |
| |
| Dataset Source: https://www.kaggle.com/datasets/razamukhtar007/fake-reviews |
| |
| __Histogram of Word Counts of Reviews__ |
| |
|  |
| |
| __Class Distribution__ |
| |
|  |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 0.001 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
| | 0.633 | 1.0 | 10438 | 0.5608 | 0.8261 | 0.7914 | 0.8261 | __0.5745__ | 0.8261 | 0.8261 | 0.5643 | 0.7844 | 0.8261 | 0.6542 | |
| | 0.6029 | 2.0 | 20876 | 0.6490 | 0.8331 | 0.7724 | 0.8331 | __0.5060__ | 0.8331 | 0.8331 | 0.5239 | 0.7892 | 0.8331 | 0.6929 | |
| | 0.5478 | 3.0 | 31314 | 0.5508 | 0.8305 | 0.8071 | 0.8305 | __0.6189__ | 0.8305 | 0.8305 | 0.6003 | 0.8002 | 0.8305 | 0.6784 | |
| | 0.513 | 4.0 | 41752 | 0.5459 | 0.8347 | 0.8101 | 0.8347 | __0.6224__ | 0.8347 | 0.8347 | 0.6023 | 0.8049 | 0.8347 | 0.6916 | |
| | 0.5288 | 5.0 | 52190 | 0.5336 | 0.8381 | 0.8142 | 0.8381 | __0.6308__ | 0.8381 | 0.8381 | 0.6090 | 0.8101 | 0.8381 | 0.7029 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.31.0 |
| - Pytorch 2.0.1 |
| - Datasets 2.13.1 |
| - Tokenizers 0.13.3 |
| |
| |
| ## License Notice |
| This model is a fine-tuned derivative of a pretrained model. |
| Users must comply with the original model license. |
| |
| |
| ## Dataset Notice |
| This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions. |
