Update README.md

Author: prasanthpul

README.md

@@ -102,7 +102,7 @@ By default we preserve the image format of inputs (nchw or nhwc) as given in the
 ### --opset
 By default we use the opset 7 to generate the graph. By specifying ```--opset``` the user can override the default to generate a graph with the desired opset. For example ```--opset 5``` would create a onnx graph that uses only ops available in opset 5. Because older opsets have in most cases fewer ops, some models might not convert on a older opset.
 ### --target 
-Some models require special handling, for example they don't support all data types. We'll workaround it in some cases by generating a different graph. Those workarounds are activated with ```--target TARGET```. Currently supported values are listed on this [wiki](https://github.com/onnx/tensorflow-onnx/wiki/target). If your model will be run on Windows ML, you should specify the appropriate target value.
+Some models require special handling to run on some runtimes. In particular, the model may use unsupported data types. Workarounds are activated with ```--target TARGET```. Currently supported values are listed on this [wiki](https://github.com/onnx/tensorflow-onnx/wiki/target). If your model will be run on Windows ML, you should specify the appropriate target value.
 ### --custom-ops
 the runtime may support custom ops that are not defined in onnx. A user can asked the converter to map to custom ops by listing them with the --custom-ops option. Tensorflow ops listed here will be mapped to a custom op with the same name as the tensorflow op but in the onnx domain ai.onnx.converters.tensorflow. For example: ```--custom-ops Print``` will insert a op ```Print``` in the onnx domain ```ai.onnx.converters.tensorflow``` into the graph. We also support a python api for custom ops documented later in this readme. 
 ### --fold_const