update proto version and documentation

README.md

@@ -24,6 +24,7 @@ Interesting values (such as a high proportion of missing data, or very different
 Features can be sorted by values of interest such as the number of missing values or the skew between the different datasets.
 
 The python code to generate the statistics for visualization can be installed through `pip install facets-overview`.
+As of version 1.1.0, the `facets-overview` package requires a version of `protobuf` at version 3.20.0 or later.
 
 Details about Overview usage can be found in its [README](./facets_overview/README.md).
 

facets_overview/README.md

@@ -27,6 +27,7 @@ The feature statistics protocol buffer can be created for datasets by the python
 This code can be installed through `pip install facets-overview`. TensorFlow should also be installed but is not included as a
 pip dependency, so as to allow a user to depend on either the tensorflow or tensorflow-gpu package as necessary.
 Datasets can be analyzed either from a TfRecord files of tensorflow Example protocol buffers, or from pandas DataFrames.
+As of version 1.1.0, the `facets-overview` package requires a version of `protobuf` at version 3.20.0 or later.
 
 To create the proto from a pandas DataFrame, use the `ProtoFromDataFrames` method of the [GenericFeatureStatisticsGenerator class](./python/generic_feature_statistics_generator.py).
 To create the proto from a TfRecord file, use the `ProtoFromTfRecordFiles` method of the [FeatureStatisticsGenerator class](./python/feature_statistics_generator.py).