Update README.md

README.md

@@ -752,7 +752,7 @@ In this way, first-class dims are a way of adapting the nicer syntax of these ar
 
 Performance Expectations
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-First-class dimensions are not a compiler. They provide syntax for existing PyTorch operations such as advanced indexing that is easier to read and write. For large sized tensors, the performance of any statements including them will be the same as using the already existing operations. An important exception is the pattern matching of products and summation, where performance will be improved by issuing to a matrix-multiply kernel. The C++ implementation of dimensions adds a small overhead of around 2us on top of PyTorch's normal overhead of 8us to each function that uses them. In the future, the implementation can encorporate more fusion optimization to further improve performance of this style of code.
+First-class dimensions are not a compiler. They provide syntax for existing PyTorch operations such as advanced indexing that is easier to read and write. For large sized tensors, the performance of any statements including them will be the same as using the already existing operations. An important exception is the pattern matching of products and summation, where performance will be improved by issuing to a matrix-multiply kernel. The C++ implementation of dimensions adds a small overhead of around 2us on top of PyTorch's normal overhead of 8us to each function that uses them. In the future, the implementation can incorporate more fusion optimization to further improve performance of this style of code.
 
 
 ## License