fix descr in setup; bump version

setup.py

@@ -14,7 +14,7 @@
 
 setup(
     name='dtreeviz',
-    version='2.1.0',
+    version='2.1.1',
     url='https://github.com/parrt/dtreeviz',
     license='MIT',
     packages=find_packages(),
@@ -37,8 +37,8 @@
     python_requires='>=3.6',
     author='Terence Parr, Tudor Lapusan, and Prince Grover',
     author_email='parrt@antlr.org',
-    description='''A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization',
-    long_description='A python library for decision tree visualization and model interpretation.  Decision trees are the fundamental building block of [gradient boosting machines](http://explained.ai/gradient-boosting/index.html) and [Random Forests](https://en.wikipedia.org/wiki/Random_forest)(tm), probably the two most popular machine learning models for structured data.  Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by [R2D3](http://www.r2d3.us/); [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/). Please see [How to visualize decision trees](http://explained.ai/decision-tree-viz/index.html) for deeper discussion of our decision tree visualization library and the visual design decisions we made.
+    description='''A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization''',
+    long_description='''A python library for decision tree visualization and model interpretation.  Decision trees are the fundamental building block of [gradient boosting machines](http://explained.ai/gradient-boosting/index.html) and [Random Forests](https://en.wikipedia.org/wiki/Random_forest)(tm), probably the two most popular machine learning models for structured data.  Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by [R2D3](http://www.r2d3.us/); [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/). Please see [How to visualize decision trees](http://explained.ai/decision-tree-viz/index.html) for deeper discussion of our decision tree visualization library and the visual design decisions we made.
 
 Currently dtreeviz supports: [scikit-learn](https://scikit-learn.org/stable), [XGBoost](https://xgboost.readthedocs.io/en/latest), [Spark MLlib](https://spark.apache.org/mllib/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [Tensorflow](https://www.tensorflow.org/decision_forests).  See [Installation instructions](README.md#Installation).''',
     keywords='machine-learning data structures trees visualization',