Dataset Collection: 
* The hatespeech dataset is collected from Kaggle, containing 25k Twits.
* The dataset has the three classes 0-Hatespeech 1-Offensive Language 2-Neither
https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offensive-language-dataset

Dataset Preparation:
* Change all text to lowercase
* Eliminate URLs
* Use TextBlob for spelling correction
* Convert emojis to text and keep sentiment
* Keep hashtags
* Eliminate @mentions
* Balancing

distilbert-base-uncased model fine-tuned for Hate Speech Detection
* The model is fine-tuned on the dataset, used PyTorch for tokenization
* This model can be used to create the labels for academic purposes or for industrial purposes.
* This model can be used for the inference purpose as well.

Data Fields: 24.783
Label: 0-Hatespeech 1-Offensive Language 2-Neither

Application:
* The model is useful for tweet hatespeech detection
* The model can be used to infer labels, classifying the tweet between the three classes
* You can fine-tune this model for your particular use cases

Model Implementation
* Best Hyperparameters: {'batch_size': 32, 'epochs': 7, 'learning_rate': 1e-05}
    * Accuracy: 0.97
* Model Results:
    * Accuracy: 0.84
    * Precision: 0.83
    * Recall: 0.84
    * F1 Score: 0.83
* Steps to Implement Model:
{}
transformers pytorch tensorboard safetensors distilbert text-classification autotrain_compatible endpoints_compatible region:us