Classify Turkish news into categories
Determine emotion from text
Compare LLMs by role stability
Open LLM(CohereForAI/c4ai-command-r7b-12-2024) and RAG
Choose to summarize text or answer questions from context
Classify text into categories
Compare different tokenizers in char-level and byte-level.
Detect AI-generated texts with precision
Track, rank and evaluate open Arabic LLMs and chatbots
Test your attribute inference skills with comments
Encode and decode Hindi text using BPE
Detect emotions in text sentences
Generative Tasks Evaluation of Arabic LLMs
Turkish News Classification is an AI-powered text analysis tool designed to categorize Turkish news articles into predefined categories. This tool leverages advanced natural language processing (NLP) and machine learning algorithms to accurately classify news content, enabling efficient organization and analysis of large volumes of text data.
• Multilingual Support: Primarily focused on Turkish, with capabilities to handle other languages to a limited extent. • High Accuracy: Utilizes state-of-the-art models to ensure precise classification. • Customizable Categories: Allows users to define their own categories for specific use cases. • Real-Time Processing: Enables quick classification of news articles as they are published or ingested. • Integration Ready: Can be easily integrated into existing news aggregation and analysis platforms. • Scalability: Designed to handle large datasets and high-throughput environments.
What languages does Turkish News Classification support?
The tool primarily supports Turkish, but it can also handle other languages to a limited extent with reduced accuracy.
Can I customize the classification categories?
Yes, users can define their own categories to suit specific needs, allowing for flexible and tailored classification.
How accurate is the classification?
The accuracy depends on the quality of the model and the clarity of the input text. State-of-the-art models ensure high precision, but results can vary based on context and complexity.