FakeNewsChallenge/fnc-1-baseline
A baseline implementation for FNC-1
This project helps data scientists and researchers working on content analysis to evaluate how well their systems can identify the relationship between a news headline and its article body. You provide a dataset of headlines and article bodies, along with their known relationships (e.g., 'agree', 'disagree', 'discuss', 'unrelated'). The project outputs a score and a confusion matrix, showing how accurately your system categorized these relationships.
139 stars. No commits in the last 6 months.
Use this if you are developing or testing a system for automatically classifying the stance of news articles and need a standard way to evaluate its performance against a known dataset.
Not ideal if you are looking for a complete, production-ready fake news detection application or a tool that generates classifications directly without custom model integration.
Stars
139
Forks
101
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 03, 2022
Commits (30d)
0
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