improve readme

README.md

@@ -89,18 +89,20 @@ julia> dict_sigma = Parquet.diagdict_parquet(Parquet.SigmaDiag, renormalization_
 The Back End architecture enables the compiler to output source code in a range of other programming languages and machine learning frameworks. The example code below demonstrates how to use the `Compilers` to generate the source code for the self-energy diagrams in Julia, C, and Python.
 
 ```julia
-julia> g_o211 = dict_sigma[(2,1,1)] # select the self-energy with 2nd-order Green's function counterterms and 1st-order interaction counterterms.
+#Access the self-energy data for the configuration with 2nd-order Green's function counterterms and 1st-order interaction counterterms.
+julia> g_o211 = dict_sigma[(2,1,1)] 
 
-# Compile the self-energy to Julia RuntimeGeneratedFunction `func` and the `leafmap`, which maps the indices in the vector of leaf values to the corresponding leafs (propagators and interactions). 
+# Compile the selected self-energy into a Julia RuntimeGeneratedFunction `func` and a `leafmap`.
+# The `leafmap` associates vector indices of leaf values with their corresponding leaves (propagators and interactions). 
 julia> func, leafmap = Compilers.compile(g_o211);
 
-# Generate the source code for the self-energy in Julia and save it to a file.
+# Export the self-energy's source code to a Julia file.
 julia> Compilers.compile_Julia(g_o211, "func_g211.jl");
 
-# Generate the source code for the self-energy in C-language and save it to a file.
+# Export the self-energy's source code to a C file.
 julia> Compilers.compile_C(g_o211, "func_g211.c");
 
-# Generate the source code for the self-energy in Python and JAX machine learning framework and save it to a file.
+# Export the self-energy's source code to a Python file for use with the JAX machine learning framework.
 julia> Compilers.compile_Python(g_o211, :jax, "func_g211.py");
 ```