1 citations · 1 across the 5 of their papers we have counts for
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HUME: Measuring the Human-Model Performance Gap in Text Embedding Tasks
Adnan El Assadi, Isaac Chung, Roman Solomatin +2
Comparing human and model performance offers a valuable perspective for understanding the strengths and limitations of embedding models, highlighting where they succeed and where t…
AutoIntent: AutoML for Text Classification
Ilya Alekseev, Roman Solomatin, Darina Rustamova +1
AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, c…
Maintaining MTEB: Towards Long Term Usability and Reproducibility of Embedding Benchmarks
Isaac Chung, Imene Kerboua, Marton Kardos +2
The Massive Text Embedding Benchmark (MTEB) has become a standard evaluation platform for text embedding models. While previous work has established the core benchmark methodology,…