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Applied NLPLive
Verity
Verity takes a piece of text and returns a credibility signal in real time. It was built to explore how far a disciplined, classical NLP pipeline — not a large model — could go on a well-defined classification task, deployed end to end as a real product rather than a notebook.
Core capabilities
- TF-IDF vectorization tuned for short-form text
- Logistic regression classifier trained on labeled news data
- Flask API with request validation and structured responses
- Static frontend calling the API directly, no server-rendered coupling
Frequently asked
Does Verity use a large language model?+
No. It deliberately uses a classical TF-IDF plus logistic regression pipeline, to see how far disciplined feature engineering goes before reaching for a larger model.
Is the source available?+
Yes, the backend is open source. The link is on this page.
Use cases
- Quick credibility check before sharing an article
- Reference implementation for lightweight text classification
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