fBERT-Hate_Offensive_or_Normal_Speech
This model is a fine-tuned version of diptanu/fBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1282
- Accuracy: 0.9607
- Weighted f1: 0.9605
- Micro f1: 0.9607
- Macro f1: 0.9581
- Weighted recall: 0.9607
- Micro recall: 0.9607
- Macro recall: 0.9571
- Weighted precision: 0.9609
- Micro precision: 0.9607
- Macro precision: 0.9596
Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiclass%20Classification/Transformer%20Comparison/Hate%20%26%20Offensive%20Speech%20-%20fBERT.ipynb
Associated Models
This project is part of a comparison that included the following models:
- https://huggingface.co/DunnBC22/bert-large-uncased-Hate_Offensive_or_Normal_Speech
- https://huggingface.co/DunnBC22/bert-base-uncased-Hate_Offensive_or_Normal_Speech
- https://huggingface.co/DunnBC22/distilbert-base-uncased-Hate_Offensive_or_Normal_Speech
- https://huggingface.co/DunnBC22/hateBERT-Hate_Offensive_or_Normal_Speech
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
The main limitation is the quality of the data source.
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/subhajournal/normal-hate-and-offensive-speeches
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: 3
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8316 | 1.0 | 39 | 0.5146 | 0.6754 | 0.5655 | 0.6754 | 0.5312 | 0.6754 | 0.6754 | 0.6324 | 0.4902 | 0.6754 | 0.4616 |
| 0.3628 | 2.0 | 78 | 0.2042 | 0.8820 | 0.8786 | 0.8820 | 0.8706 | 0.8820 | 0.8820 | 0.8685 | 0.8930 | 0.8820 | 0.8922 |
| 0.1767 | 3.0 | 117 | 0.1282 | 0.9607 | 0.9605 | 0.9607 | 0.9581 | 0.9607 | 0.9607 | 0.9571 | 0.9609 | 0.9607 | 0.9596 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
