Fix NuGet README

src/NetFabric.Numerics.Tensors/README.md

@@ -8,8 +8,6 @@ Within the library, you'll find pre-defined operations such as `Sqrt()`, `Sin()`
 
 For custom operations, the library allows the definition of operators through interfaces like `IUnaryOperator<T>`, `IBinaryOperator<T>`, `ITernaryOperator<T>`, or `IAggregationOperator<T>`. These operators can be applied seamlessly using the `Apply()` or `Aggregate()` methods.
 
-Documentation for this library is available at [NetFabric.Numerics.Tensors Documentation](https://netfabric.github.io/NetFabric.Numerics.Tensors/).
-
 ## Usage
 
 The library includes methods tailored for operations involving one, two, or three `ReadOnlySpan<T>`. Results are provided in a `Span<T>`, with the condition that the destination `Span<T>` must be of the same size or larger than the sources. Inplace operations are supported when the destination parameter matches any of the sources.
@@ -49,12 +47,6 @@ var indexOfLessThan = IndexOfLessThan(values, 0);
 var indexOfLessThanOrEqual = IndexOfLessThanOrEqual(values, 0);
 ```
 
-Please consult the [documentation](https://netfabric.github.io/NetFabric.Numerics.Tensors/api/NetFabric.Numerics.TensorOperations.html) to get the complete list of pre-defined operations.
-
 ## Custom Operations
 
-While `NetFabric.Numerics.Tensors` provides various pre-defined operations, combining them might not be efficient. Custom operators can be implemented, allowing the definition of specific operations for each element of the source, while still benefiting from high-performance reusable iteration code. Please refer to the [documentation](https://netfabric.github.io/NetFabric.Numerics.Tensors/articles/Extending-the-library.html) for instructions on how to do it.
-
-## Benchmarks
-
-Please consult the [documentation](https://netfabric.github.io/NetFabric.Numerics.Tensors/articles/Benchmarks.html) to obtain benchmarking results.
+While `NetFabric.Numerics.Tensors` provides various pre-defined operations, combining them might not be efficient. Custom operators can be implemented, allowing the definition of specific operations for each element of the source, while still benefiting from high-performance reusable iteration code.