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README.md

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 `fast-stats` is designed and tested against the `scikit-learn` library and provides formatted results to be a drop-in replacement in most cases. There are both generic functions like `fast_stats.precision` that provide significant speedup and specialized binary functions such as `fast_stats.binary_precision` that provide even faster optimal performance for binary data. `fast-stats` was specifically created to provide a limited alternative to `scikit-learn.metrics` for calculating statistics quickly on large multi-dimensional arrays or tensors such as those produced by computer vision models.
 
 [Benchmarks](`examples/benchmarks.ipynb`) show approximately: 
-- 100 times improvement in binary calculations
-- 10 times improvement in multiclass calculations
-- 2 times improvement in computing confusion matrices
+- 100x improvement in binary calculations
+- 10x improvement in multiclass calculations
+- 2x improvement in computing confusion matrices
 - 15% speed-up over an equivalent `numpy` binary calculation
 
 The project was developed using the [maturin](https://maturin.rs) framework.