Classify Turkish news into categories
Ask questions about air quality data with pre-built prompts or your own queries
Use title and abstract to predict future academic impact
Detect harms and risks with Granite Guardian 3.1 8B
Explore and filter language model benchmark results
Generative Tasks Evaluation of Arabic LLMs
Load documents and answer questions from them
Humanize AI-generated text to sound like it was written by a human
Test SEO effectiveness of your content
Upload a table to predict basalt source lithology, temperature, and pressure
Calculate love compatibility using names
Upload a PDF or TXT, ask questions about it
Extract bibliographical metadata from PDFs
Turkish News Classification is an AI-powered tool designed for text analysis. It specializes in categorizing Turkish news articles into predefined categories based on their content. This tool leverages advanced natural language processing (NLP) to accurately classify news into relevant categories, making it useful for media monitoring, content organization, and research purposes.
• Support for Turkish Language: Built specifically for Turkish text, ensuring high accuracy in understanding linguistic nuances. • Multiple Categories: Classifies news into a variety of categories such as politics, sports, technology, economy, and more. • Integration Ready: Can be seamlessly integrated into larger applications or pipelines for automated workflows. • API Access: Provides an easy-to-use API for developers to incorporate the classification functionality. • Customizable Categories: Allows users to define custom categories based on their specific needs.
What formats does Turkish News Classification support?
Turkish News Classification supports plain text inputs. Ensure your news articles are provided in a clean, unformatted structure for optimal accuracy.
Can I customize the categories?
Yes, you can define custom categories to suit your specific needs. Contact the support team for guidelines on implementing custom category models.
How accurate is the classification?
The accuracy depends on the quality of the input text and the model's training data. Under ideal conditions, it achieves high accuracy, but results may vary with ambiguous or unclear content.