address comments

Author: Vahid Tavanashad

README.md

@@ -6,7 +6,7 @@
 
 # Introduction
 `mkl_fft` started as a part of Intel® Distribution for Python* optimizations to NumPy, and is now being released
-as a stand-alone package. It offers a thin layered interface for the Intel® oneAPI Math Kernel Library (OneMKL) FFT functionality that allows efficient access to native FFT optimizations from a range of NumPy and SciPy functions. As a result, its performance close to performance of native C/Intel OneMKL. The optimizations are provided for real and complex data types in both single and double precision for in-place and out-of-place modes of operation. As a result, its performance is close to the performance of native C/Intel® OneMKL. For analyzing the performance use [FFT benchmarks](https://github.com/intelpython/fft_benchmark).
+as a stand-alone package. It offers a thin layered interface for the Intel® oneAPI Math Kernel Library (OneMKL) FFT functionality that allows efficient access to native FFT optimizations from a range of NumPy and SciPy functions. As a result, its performance is close to the performance of native C/Intel® OneMKL. The optimizations are provided for real and complex data types in both single and double precisions for in-place and out-of-place modes of operation. For analyzing the performance use [FFT benchmarks](https://github.com/intelpython/fft_benchmark).
 
 Thanks to Intel® OneMKL’s flexibility in its supports for arbitrarily strided input and output arrays both one-dimensional and multi-dimensional Fast Fourier Transforms along distinct axes can be performed directly, without the need to copy the input into a contiguous array first. Furthermore, input strides can be arbitrary, including negative or zero, as long as strides remain an integer multiple of array’s item size, otherwise a copy will be made.