---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: bert-base-uncased-Abusive_Or_Threatening_Speech
  results: []
language:
- en
pipeline_tag: text-classification
---

# bert-base-uncased-Abusive_Or_Threatening_Speech

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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.