[doc] add paddlepaddle example to readme
@@ -94,7 +94,7 @@ Standarlize model serving and deployment workflow for teams:
* Transformers - [Docs](https://docs.bentoml.org/en/latest/frameworks.html#transformers)
* Gluon - [Docs](https://docs.bentoml.org/en/latest/frameworks.html#gluon)
* Detectron - [Docs](https://docs.bentoml.org/en/latest/frameworks.html#detectron)
-* Paddle - [Docs](https://docs.bentoml.org/en/latest/frameworks.html#paddle)
+* PaddlePaddle - [Docs](https://docs.bentoml.org/en/latest/frameworks.html#paddle) | [Example](https://github.com/bentoml/gallery#paddlepaddle)
* EvalML - [Docs](https://docs.bentoml.org/en/latest/frameworks.html#evalml)
* EasyOCR -[Docs](https://docs.bentoml.org/en/latest/frameworks.html#easyocr)
* ONNX-MLIR - [Docs](https://docs.bentoml.org/en/latest/frameworks.html#onnx-mlir)
@@ -130,7 +130,7 @@ to understand which deployment option is best suited for your use case.
## Introduction
BentoML provides APIs for defining a prediction service, a servable model so to speak,
-which includes the traind ML model itself, plus its pre-processing, post-processing
+which includes the trained ML model itself, plus its pre-processing, post-processing
code, input/output specifications and dependencies. Here's what a simple
prediction service look like in BentoML:
