[Doc] v0.3.0 release (#200)

README.md

@@ -21,7 +21,7 @@ We summarize the contributions of CogDL as follows:
 
 ## ❗ News
 
-- We release the first version of **[CogDL paper](https://arxiv.org/abs/2103.00959)** in arXiv. You can join [our slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg) for discussion.🎉
+- The new **v0.3.0 release** provides a fast spmm operator to speed up GNN training. We also release the first version of **[CogDL paper](https://arxiv.org/abs/2103.00959)** in arXiv. You can join [our slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg) for discussion. 🎉🎉🎉
 
 - The new **v0.2.0 release** includes easy-to-use `experiment` and `pipeline` APIs for all experiments and applications. The `experiment` API supports automl features of searching hyper-parameters. This release also provides `OAGBert` API for model inference (`OAGBert` is trained on large-scale academic corpus by our lab). Some features and models are added by the open source community (thanks to all the contributors 🎉).
 

README_CN.md

@@ -1,6 +1,7 @@
 ![CogDL](docs/source/_static/cogdl-logo.png)
 ===
 
+[![PyPI Latest Release](https://badge.fury.io/py/cogdl.svg)](https://pypi.org/project/cogdl/)
 [![Build Status](https://travis-ci.org/THUDM/cogdl.svg?branch=master)](https://travis-ci.org/THUDM/cogdl)
 [![Coverage Status](https://coveralls.io/repos/github/THUDM/cogdl/badge.svg?branch=master)](https://coveralls.io/github/THUDM/cogdl?branch=master)
 [![Documentation Status](https://readthedocs.org/projects/cogdl/badge/?version=latest)](https://cogdl.readthedocs.io/en/latest/?badge=latest)
@@ -20,7 +21,7 @@ CogDL的特性包括：
 
 ## ❗ 最新
 
-- 我们在arXiv上发布了 **[CogDL paper](https://arxiv.org/abs/2103.00959)** 的初版. 你可以加入[我们的slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg)来讨论CogDL相关的内容。🎉
+- 最新的 **v0.3.0版本** 提供了快速的稀疏矩阵乘操作来加速图神经网络模型的训练。我们在arXiv上发布了 **[CogDL paper](https://arxiv.org/abs/2103.00959)** 的初版. 你可以加入[我们的slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg)来讨论CogDL相关的内容。🎉
 
 - 最新的 **v0.2.0版本** 包含了非常易用的`experiment`和`pipeline`接口，其中`experiment`接口还支持超参搜索。这个版本还提供了`OAGBert`模型的接口（`OAGBert`是我们实验室推出的在大规模学术语料下训练的模型）。这个版本的很多内容是由开源社区的小伙伴们提供的，感谢大家的支持！🎉
 

docs/source/index.rst

@@ -17,7 +17,7 @@ We summarize the contributions of CogDL as follows:
 ❗ News
 ------------
 
-- We release the first version of `CogDL paper <https://arxiv.org/abs/2103.00959>`_ in arXiv. You can join `our slack <https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg>`_ for discussion.🎉
+- The new **v0.3.0 release** provides a fast spmm operator to speed up GNN training. We also release the first version of `CogDL paper <https://arxiv.org/abs/2103.00959>`_ in arXiv. You can join `our slack <https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg>`_ for discussion. 🎉🎉🎉
 - The new **v0.2.0 release** includes easy-to-use ``experiment`` and ``pipeline`` APIs for all experiments and applications. The ``experiment`` API supports automl features of searching hyper-parameters. This release also provides ``OAGBert`` API for model inference (``OAGBert`` is trained on large-scale academic corpus by our lab). Some features and models are added by the open source community (thanks to all the contributors 🎉).
 - The new **v0.1.2 release** includes a pre-training task, many examples, OGB datasets, some knowledge graph embedding methods, and some graph neural network models. The coverage of CogDL is increased to 80%. Some new APIs, such as ``Trainer`` and ``Sampler``, are developed and being tested. 
 - The new **v0.1.1 release** includes the knowledge link prediction task, many state-of-the-art models, and ``optuna`` support. We also have a `Chinese WeChat post <https://mp.weixin.qq.com/s/IUh-ctQwtSXGvdTij5eDDg>`_ about the CogDL release.