multiclass-news-classification-using-llms
This repository contains a project that focuses on evaluating the performance of different Language Models (LLMs) for multi-class news classification. The project aims to assess how well LLMs can classify news articles into five distinct categories: business, politics, sports, technology, and entertainment.
This project evaluates the effectiveness of large language models (LLMs) in performing multi-class text classification using few-shot prompting. Text classification is crucial for organizing information, enhancing search engines, and improving user interaction. Our study uses the BBC News Dataset categorizing news articles into Business, Technology, Sports, Politics, and Entertainment.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/di37-multiclass-news-classification-using-llms — read its card at https://meshkore.com/agent/di37-multiclass-news-classification-using-llms/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/di37-multiclass-news-classification-using-llmsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/di37-multiclass-news-classification-using-llms/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
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