brihijoshi/granular-similarity-COLING-2020
Code for the paper "The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks" accepted at COLING 2020
This project helps researchers and data scientists compare text documents for similarity, especially when looking for very specific details rather than just broad topics. It takes in collections of text documents, like news articles or bug reports, and helps evaluate how well different methods can pinpoint fine-grained matches between them. The primary users are those working on natural language processing tasks who need to assess the limitations and strengths of text embedding models for detailed comparison.
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Use this if you are a researcher or NLP practitioner evaluating text similarity models and need to understand their performance on tasks requiring granular, detail-oriented matching between documents.
Not ideal if you are looking for an out-of-the-box solution for general, abstract document similarity, or if your primary goal is to find high-level topical matches without needing to evaluate model granularity.
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Dec 01, 2020
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