README.md

metadata

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.

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