Update README.md

README.md

@@ -19,7 +19,7 @@ There are two tutorial notebooks:
 
 1. Model_based_curation_tutorial.ipynb
 
-   This notebook helps you apply pretrained models to new recordings. Simply load the models and use them to label your spike-sorted data.
+   This notebook helps you apply pre-trained models to new recordings. Simply load the models and use them to label your spike-sorted data.
 
    
    We provide "noise_neuron_model.skops" which is used to identify noise, and "sua_mua_model.skops" which is used to isolate SUA. These models
@@ -27,11 +27,11 @@ There are two tutorial notebooks:
 
    Steps:
 
-   1. load your recording depending on the acquisition software you used to create the 'recording' object
-   2. load your sorting depending on the spike sorter you used to create the 'sorting' object
+   1. load your recording depending on the acquisition software you used to create the 'recording' object.
+   2. load your sorting depending on the spike sorter you used to create the 'sorting' object.
    3. Then you can create a Sorting_Analyzer object and you compute quality metrics.
 
-  These steps are explained in more details in the jupyter notebook in files folder.
+  These steps are explained in more detail in the Jupyter notebook in the files folder.
   
   auto_label_units is the main in this notebook. API link to know the parameters:
   (https://spikeinterface--2918.org.readthedocs.build/en/2918/api.html#spikeinterface.curation.auto_label_units)