bert-base-uncased-Abusive_Or_Threatening_Speech

This model is a fine-tuned version of bert-base-uncased. It achieves the following results on the evaluation set:

• Loss: 0.0874
• Accuracy: 0.9720
• F1: 0.7590
• Recall: 0.8406
• Precision: 0.6918

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/Malignant%20Comments/Malignant%20Comments%20-%20BERT-Base.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/surekharamireddy/malignant-comment-classification

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.1008	1.0	1531	0.0874	0.9720	0.7590	0.8406	0.6918
0.0673	2.0	3062	0.0981	0.9719	0.7591	0.8450	0.6891

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0
• Datasets 2.8.0
• Tokenizers 0.12.1

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.