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dustalov · Aug 31, 2024 · 49c6427 · 49c6427
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 [codspeed_badge]: https://img.shields.io/endpoint?url=https://codspeed.io/badge.json
 [codspeed_link]: https://codspeed.io/dustalov/evalica
 
-**Evalica** is a Python library that transforms pairwise comparisons into ranked lists of items. It offers convenient high-performant Rust implementations of the corresponding methods via [PyO3](https://pyo3.rs/), and additionally provides naïve Python code for most of them. Evalica is fully compatible with [NumPy](https://numpy.org/) arrays and [pandas](https://pandas.pydata.org/) data frames.
+**Evalica** [&#x025b;&#x02c8;&#x028b;alitsa] (eh-val-lee-tsah) is a Python library that transforms pairwise comparisons into ranked lists of items. It offers convenient high-performant Rust implementations of the corresponding methods via [PyO3](https://pyo3.rs/), and additionally provides naïve Python code for most of them. Evalica is fully compatible with [NumPy](https://numpy.org/) arrays and [pandas](https://pandas.pydata.org/) data frames.
 
 - [Tutorial](https://dustalov.github.io/evalica/) (and [Tutorial.ipynb](Tutorial.ipynb))
 - [Chatbot-Arena.ipynb](Chatbot-Arena.ipynb) [![Open in Colab][colab_badge]][colab_link] [![Binder][binder_badge]][binder_link]

diff --git a/docs/index.md b/docs/index.md
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 # Evalica
 
-**Evalica** is a Python library that transforms pairwise comparisons into ranked lists of items. It offers convenient high-performant Rust implementations of the corresponding methods via [PyO3](https://pyo3.rs/), and additionally provides naïve Python code for most of them. Evalica is fully compatible with [NumPy](https://numpy.org/) arrays and [pandas](https://pandas.pydata.org/) data frames.
+**Evalica** [&#x025b;&#x02c8;&#x028b;alitsa] (eh-val-lee-tsah) is a Python library that transforms pairwise comparisons into ranked lists of items. It offers convenient high-performant Rust implementations of the corresponding methods via [PyO3](https://pyo3.rs/), and additionally provides naïve Python code for most of them. Evalica is fully compatible with [NumPy](https://numpy.org/) arrays and [pandas](https://pandas.pydata.org/) data frames.
 
 [![Evalica](https://raw.githubusercontent.com/dustalov/evalica/master/Evalica.svg)](https://github.com/dustalov/evalica)