Offensive Speech Detection

🚀 Model Card: Fine-Tuned DistilBERT for Offensive/Hate Speech Detection

This fine-tuned DistilBERT model is designed to efficiently detect offensive and hate speech in text data, maintaining high accuracy.

🚀 Quick Start

To use this model for offensive/hate speech detection, you can follow these steps:

from transformers import pipeline

classifier = pipeline("text-classification", model="Falconsai/offensive_speech_detection")
text = "Your text to classify here."
result = classifier(text)

✨ Features

• Efficient Performance: The Fine-Tuned DistilBERT is a distilled variant of the BERT transformer model, offering efficient performance while maintaining high accuracy.
• Task-Specific Adaptation: It has been specifically fine - tuned for offensive/hate speech detection in text data.
• Optimal Hyperparameters: Meticulous hyperparameter settings, such as batch size and learning rate, ensure optimal performance for the detection task.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="Falconsai/offensive_speech_detection")
text = "Your text to classify here."
result = classifier(text)

📚 Documentation

Model Description

The Fine-Tuned DistilBERT is a variant of the BERT transformer model, distilled for efficient performance while maintaining high accuracy. It has been adapted and fine - tuned for the specific task of offensive/hate speech detection in text data.

The model, named "distilbert - base - uncased," is pre - trained on a substantial amount of text data, which allows it to capture semantic nuances and contextual information present in natural language text. It has been fine - tuned with meticulous attention to hyperparameter settings, including batch size and learning rate, to ensure optimal model performance for the offensive/hate speech detection task.

During the fine - tuning process, a batch size of 16 for efficient computation and learning was chosen. Additionally, a learning rate (2e - 5) was selected to strike a balance between rapid convergence and steady optimization, ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.

This model has been trained on a proprietary dataset < 100k, specifically designed for offensive/hate speech detection. The dataset consists of text samples, each labeled as "non - offensive" or "offensive." The diversity within the dataset allowed the model to learn to identify offensive content accurately. This dataset was combined from research datasets on this topic as well as news headlines. All duplicates were removed and meticulous effort was taken to ensure the dataset quality.

The goal of this meticulous training process is to equip the model with the ability to detect offensive and hate speech in text data effectively. The result is a model ready to contribute significantly to content moderation and safety, while maintaining high standards of accuracy and reliability.

Intended Uses & Limitations

Intended Uses

• Offensive/Hate Speech Detection: The primary intended use of this model is to detect offensive or hate speech in text data. It is well - suited for filtering and identifying inappropriate content in various applications.
• Of Special Note: The data suggests the word "like" is most often used as a comparative statement in the derogatory. These have numerous instances within the "Offensive Speech Dataset". "You look like X" or "He smells like X" are quite common. Also of note, the ABSENCE/LACK OF of punctuation lends itself heavily to the "Offensive" dataset. Accordingly the model will identify these as well, based on their prominence in the training data.

Limitations

• Specialized Task Fine - Tuning: While the model is adept at offensive/hate speech detection, its performance may vary when applied to other natural language processing tasks. Users interested in employing this model for different tasks should explore fine - tuned versions available in the model hub for optimal results.

Training Data

The model's training data includes a proprietary dataset designed for offensive/hate speech detection. This dataset comprises a diverse collection of text samples, categorized into "non - offensive" and "offensive" classes. The training process aimed to equip the model with the ability to distinguish between offensive and non - offensive content effectively.

Training Stats

Responsible Usage

It is essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real - world applications, particularly those involving potentially sensitive content.

References

• Hugging Face Model Hub
• DistilBERT Paper

⚠️ Important Note

The model's performance may be influenced by the quality and representativeness of the data it was fine - tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.

📄 License

This model is licensed under the Apache 2.0 license.