Audience
Academic researchers and developers searching for a tool to implement efficient and scalable passage re-ranking and expansion techniques
About TILDE
TILDE (Term Independent Likelihood moDEl) is a passage re-ranking and expansion framework built on BERT, designed to enhance retrieval performance by combining sparse term matching with deep contextual representations. The original TILDE model pre-computes term weights across the entire BERT vocabulary, which can lead to large index sizes. To address this, TILDEv2 introduces a more efficient approach by computing term weights only for terms present in expanded passages, resulting in indexes that are 99% smaller than those of the original TILDE. This efficiency is achieved by leveraging TILDE as a passage expansion model, where passages are expanded using top-k terms (e.g., top 200) to enrich their content. It provides scripts for indexing collections, re-ranking BM25 results, and training models using datasets like MS MARCO.
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Integrations
Company Information
ielab
United States
github.com/ielab/TILDE/tree/main
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Product Details
Platforms Supported
Cloud
Training
Documentation
Support
Online
TILDE Frequently Asked Questions
Q: What kinds of users and organization types does TILDE work with?
TILDE works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does TILDE support in their product?
TILDE supports these languages: English.
Q: What kind of support options does TILDE offer?
TILDE offers support via online.
Q: What other applications or services does TILDE integrate with?
TILDE integrates with: Hugging Face and Python.
Q: What type of training does TILDE provide?
TILDE provides training in the form of documentation.
