•1 min read•from Machine Learning
Measure Embedding Relevance: A New Approach to Retrieval Benchmarking
Retrieval benchmarks sometimes feel benchmaxxed by models, so we wanted to find a way to tie it as close as possible to my objective: finding the article that answers a product question right.
We worked on a new metric which seems more proportional to document relevance, and built up a benchmarking dataset to measure embedding models.
Here is an article on the approach: https://medium.com/qonto-way/qontofaq-benchmarking-information-retrieval-acd89600ebe1
and the associated code: https://github.com/qonto/qonto-faq-benchmark
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Tagged with
#information retrieval
#benchmarking
#QontoFAQ
#embedding models
#document relevance
#product question
#metric
#dataset
#models
#article
#code
#machine learning
#relevance
#evaluation
#performance
#question answering
#search
#accuracy
#espadrine