Merge branch 'ksaur/readmeedits' of https://github.com/microsoft/hummingbird into mainterl-updatedoc

README.md

@@ -7,10 +7,10 @@
 [![Gitter](https://badges.gitter.im/hummingbird-ml/community.svg)](https://gitter.im/hummingbird-ml/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
 
 ## Introduction
-*Hummingbird* converts trained traditional Machine Learning models into [PyTorch](https://pytorch.org/). Once in the PyTorch format, <!--you can further convert to [ONNX](https://github.com/onnx/onnx) or [TorchScript](https://pytorch.org/docs/stable/jit.html), and --> you can run the models on GPU for high performance native scoring. For full details, see [our papers](#documentation).
+*Hummingbird* is a library for accelerating inference (scoring/prediction) in traditional machine learning (ML) models. It  compiles traditional ML pipelines into tensor computations, which allows users to seamlessly leverage hardware acceleration without having to re-engineer their models. 
 
-Currently we support [these](https://github.com/microsoft/hummingbird/blob/develop/hummingbird/_supported_operators.py#L26) tree-based classifiers and regressors.  These models include
-[scikit-learn](https://scikit-learn.org/stable/) models such as  Decision Trees and Random Forest, and also [LightGBM](https://github.com/Microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost) Classifiers/Regressors.
+You can use *Hummingbird* to convert your trained traditional ML models into [PyTorch](https://pytorch.org/). Currently we support a variety of tree-based classifiers and regressors (list [here](https://github.com/microsoft/hummingbird/wiki/Supported-Operators)).  These models include
+[scikit-learn](https://scikit-learn.org/stable/) Decision Trees and Random Forest, and also [LightGBM](https://github.com/Microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost) Classifiers/Regressors.
 
 ## Installation
 
@@ -44,11 +44,11 @@ X = np.array(np.random.rand(100000, 28), dtype=np.float32)
 y = np.random.randint(num_classes, size=100000)
 
 # Create and train a model (LightGBM in this case)
-model = lgb.LGBMClassifier()
-model.fit(X, y)
+lgb_model = lgb.LGBMClassifier()
+lgb_model.fit(X, y)
 
-# Use Hummingbird to convert the model to pytorch
-model = model.to('pytorch')
+# Use Hummingbird to convert the model to PyTorch
+model = lgb_model.to('pytorch')
 
 # Run predictions on CPU
 model.predict(X)