[no ci] docs: v1 to v2, fix typo, add configurator desc

Author: vpratz

examples/From_BayesFlow_1.1_to_2.0.ipynb

@@ -67,7 +67,7 @@
     "\n",
     "Previously users would define a prior function and pass it to a `Prior1.1` object to sample prior values. The likelihood would also be specified via a function and passed to a `Simulator1.1` wrapper to produce observations for given parameter values. These were then combined in the `GenerativeModel1.1`.  \n",
     "\n",
-    "In 2.0 we no longer make use of the `Prior1.1`, `Simulator1.1` or `GenerativeModel1.1` objects. Instead, the `Simulator` class comprises the whole functionality, taking the role of the `GenerativeModel1.1`. It directly produces joint samples from prior and likelihood, without creating separate `Prior1.1` and `Simulator1.1` objects first. The `bf.simulator.make_simulator` offers a convencient wrapper to create the appropriate simulator for different settings."
+    "In 2.0 we no longer make use of the `Prior1.1`, `Simulator1.1` or `GenerativeModel1.1` objects. Instead, the `Simulator` class comprises the whole functionality, taking the role of the `GenerativeModel1.1`. It directly produces joint samples from prior and likelihood, without creating separate `Prior1.1` and `Simulator1.1` objects first. The `bf.simulator.make_simulator` offers a convenient wrapper to create the appropriate simulator for different settings."
    ]
   },
   {
@@ -158,7 +158,7 @@
    "source": [
     "### Adapter and Data Configuration\n",
     "\n",
-    "In _BayesFlow 2.0_ we now need to specify the data configuration. To make this as easy as possible, we offer a (semi-)invertible processing pipeline, the `bf.adapters.Adapter` class. It can be used to perform operations like standardization, but also for renaming and grouping variables into the format required by BayesFlow.\n",
+    "In _BayesFlow 1.1_, data configuration and pre-processing had to be handled manually in a user-specified `configurator` function. In _BayesFlow 2.0_ we also need to specify the data configuration, but this has become much simpler, as we now offer a (semi-)invertible processing pipeline. The `bf.adapters.Adapter` class can be used to perform operations like standardization, but also for renaming and grouping variables into the format required by BayesFlow.\n",
     "\n",
     "In general, BayesFlow expects three different kinds of inputs. The `summary_variables` are variables that will be summarized by a summary network. The `inference_variables` are variables we want to learn the approximate distributions for. In the posterior approximation setting, we would provide the parameter values drawn from the prior distribution, as we want to approximate a conditional distribution $p(\\theta|x)$ of the parameters $\\theta$. The `inference_conditions` are variables which are directly passed as conditions, without going through a summary network. Examples for this are hand-crafted summary statistics, as well as context variables like the number of observations. Previously these things were inferred from the type of network used, but now they need to be defined explictly using the `adapter`. The new approach is much more explicit and extensible. It also makes it easier to change individual settings, while keeping other settings at their defaults.\n",
     "\n",