Fear Mongering Detection

🚀 Model Card: Finetuned DistilBERT for Fear Mongering Detection

The Fine-Tuned DistilBERT is a specialized model designed to detect fear mongering in text data. It leverages the DistilBERT architecture, offering efficient performance while maintaining high accuracy for this specific classification task.

✨ Features

• Semantic Understanding: Pre - trained on a large amount of text data, it can capture semantic nuances and contextual information in natural language.
• Optimized Training: Meticulously fine - tuned with specific hyperparameters like batch size and learning rate for optimal performance in fear mongering classification.
• Versatile Applications: Suitable for various applications such as social media monitoring, news article analysis, and content moderation.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

To use this model for user Fear Monger classification, you can follow these steps:

from transformers import pipeline

statement = "The rise of smart cities is part of a covert plan to create a global surveillance network, where every move and action is monitored and controlled."
classifier = pipeline("text - classification", model="Falconsai/fear_mongering_detection")
classifier(statement)

📚 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 classifying user intent in text data.

Definition

Fear Monger: /ˈfɪrˌmʌŋ.ɡɚ/ to intentionally try to make people afraid of something when this is not necessary or reasonable.

The model, named "Falconsai/fear_mongering_detection" 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 user intent classification 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 rather small dataset of under 50k, 100 epochs, specifically designed for "Fear Mongering Identification".

The goal of this meticulous training process is to equip the model with the ability to identify instances of Fear Mongering in text data effectively, making it ready to contribute to a wide range of applications involving human speech, text and generated content applications.

Model Details

Use Cases

Social Media Monitoring

• Description: The model can be applied to analyze social media posts and comments to identify instances of fear mongering. This can be useful for social media platforms to monitor and moderate content that may spread fear or misinformation.

News Article Analysis

• Description: The model can be utilized to analyze news articles and identify sections containing fear - mongering language. This can help media outlets and fact - checking organizations to assess the tone and potential bias in news reporting.

Content Moderation in Online Platforms

• Description: Online platforms and forums can deploy the model to automatically flag or filter out content that may be perceived as fear - mongering. This helps maintain a more positive and constructive online environment.

Limitations

• Domain Specificity: The model's performance will be limited to the Identification of fear Mongering as this was the intent and may not generalize well to other contexts.
• False Positives: The model may occasionally misclassify non - fear - mongering text as fear - mongering. Users should be aware of this limitation.

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

Disclaimer: 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.

🔧 Technical Details

The model has been fine - tuned with a batch size of 16 for efficient computation and learning. A learning rate of 2e - 5 was selected to balance rapid convergence and steady optimization. It was trained on a dataset of under 50k for 100 epochs for the specific task of fear mongering identification.

📄 License

This model is licensed under the Apache 2.0 license.