Text data posts

Explore the latest in our mission to build a better world using data science and AI.

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tutorial

Getting started with LLMs: a benchmark for the 'What's Up, Docs?' challenge

An introduction to using large language models via the benchmark to a document summarization challenge.

winners

Meet the Winners of the Youth Mental Health Narratives Challenge

Learn about the winning solutions from the Youth Mental Health Challenge Automated Abstraction and Novel Variables Tracks

tutorial

Youth Mental Health: Automated Abstraction Benchmark

Learn how to process text narratives using open-source LLMs for the Youth Mental Health: Automated Abstraction challenge

winners

Meet the winners of the SNOMED CT Entity Linking Challenge

Meet the winners with the best systems for detecting clinical terms in medical notes.

tutorial

SNOMED CT Entity Linking Challenge - Benchmark

In this guest post from Veratai, we'll help you get started with the SNOMED CT Entity Linking Challenge!

winners

Meet the winners of the Unsupervised Wisdom Challenge!

Introducing the winners of the Unsupervised Wisdom Challenge! Meet the top teams who explored emergency department narratives about older adult falls.

winners

Announcing the results of our Keeping It Fresh competition

See who kept it the freshest — meet the winners of the Keeping It Fresh competition.

tutorial

From raw Yelp reviews to a model of hygiene violations (in 3 easy steps)

We were so excited about our new civic innovation competition we couldn't help but get started ourselves! The goal for this competition is to use data from Yelp restaurant reviews to narrow the search for health code violations in Boston. Competitors will have access to historical hygiene violation records from the City of Boston — a leader in open government data — and Yelp's consumer reviews. The challenge: Figure out the words, phrases, ratings, and patterns that predict violations, and help public health inspectors do their job better.

competition

Help cities keep it fresh

Can you use the patterns, words, and phrases in Yelp reviews of restaurants to predict the number of hygeine violations that city health inspectors uncover?

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