Remove copy-paste mistake

Author: jmarin

recipes_source/quantization.rst

@@ -81,7 +81,7 @@ The full documentation of the `quantize_dynamic` API call is `here <https://pyto
 3. Post Training Static Quantization
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-This method converts both the weights and the activations to 8-bit integers beforehand so there won’t be on-the-fly conversion on the activations during the inference, as the dynamic quantization does. While post-training static quantization can significantly enhance inference speed and reduce model size, this method may degrade the original model's performance. While post-training static quantization can significantly enhance inference speed and reduce model size, this method may degrade the original model's accuracy. Converting weights and activation functions to 8-bit integers can slightly alter the network's behavior and activation responses, leading to a reduction in the model's original effectiveness.
+This method converts both the weights and the activations to 8-bit integers beforehand so there won’t be on-the-fly conversion on the activations during the inference, as the dynamic quantization does. While post-training static quantization can significantly enhance inference speed and reduce model size, this method may degrade the original model's accuracy. Converting weights and activation functions to 8-bit integers can slightly alter the network's behavior and activation responses, leading to a reduction in the model's original effectiveness.
 
 To apply static quantization on a model, run the following code: