---
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__

![Histogram of Word Counts of Reviews](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Binary%20Classification/Fake%20Reviews/Images/Histogram%20of%20Review%20Word%20Counts.png)

__Class Distribution__

![Class Distribution](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Binary%20Classification/Fake%20Reviews/Images/Class%20Distribution.png)

## 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