fake news detection using distilbert trained on liar dataset
Check text for moderation flags
Provide feedback on text content
Generate answers by querying text in uploaded documents
Determine emotion from text
Upload a PDF or TXT, ask questions about it
Aligns the tokens of two sentences
Compare AI models by voting on responses
Encode and decode Hindi text using BPE
Generate topics from text data with BERTopic
Generate Shark Tank India Analysis
Experiment with and compare different tokenizers
This is for learning purpose, don't take it seriously :)
Fakenewsdetection is a text analysis tool designed to identify and classify news content as either Real or Fake. Leveraging the power of advanced AI technology, specifically DistilBERT fine-tuned on the Liar dataset, this tool provides reliable and efficient fake news detection. Its primary goal is to help users verify the authenticity of news articles and combat misinformation.
• Advanced NLP Model: Utilizes DistilBERT, a state-of-the-art language model optimized for performance and efficiency.
• Trained on Liar Dataset: The model is fine-tuned on the Liar dataset, containing a wide range of labeled news articles to ensure high accuracy.
• Real-Time Analysis: Quickly analyze and classify news content, providing instant results.
• User-Friendly Interface: Easy to use, with a straightforward input and output process.
• Scalability: Can handle large volumes of text, making it suitable for both individual and organizational use.
What makes Fakenewsdetection accurate?
Fakenewsdetection uses DistilBERT, a robust NLP model, and is trained on the Liar dataset, which contains a diverse collection of labeled news articles. This ensures high accuracy in detecting fake news.
Can I use Fakenewsdetection for real-time analysis?
Yes, Fakenewsdetection is designed for real-time analysis, allowing users to quickly verify the authenticity of news content as they encounter it.
Is Fakenewsdetection customizable?
While Fakenewsdetection is pre-trained on the Liar dataset, users can further fine-tune the model for specific use cases or integrate it into custom applications via its API.