From e82ec43b3790bd003a9b6303b7aa28043a5d9664 Mon Sep 17 00:00:00 2001 From: Coet Date: Sun, 2 Sep 2018 15:23:13 +0800 Subject: [PATCH] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index a38771ee..8ad0f018 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,9 @@ [![Build Status](https://travis-ci.org/fastnlp/fastNLP.svg?branch=master)](https://travis-ci.org/fastnlp/fastNLP) [![codecov](https://codecov.io/gh/fastnlp/fastNLP/branch/master/graph/badge.svg)](https://codecov.io/gh/fastnlp/fastNLP) +[![PyPI version](https://badge.fury.io/py/fastNLP.svg)](https://badge.fury.io/py/fastNLP) +![Hex.pm](https://img.shields.io/hexpm/l/plug.svg) +[![Documentation Status](https://readthedocs.org/projects/fastnlp/badge/?version=latest)](http://fastnlp.readthedocs.io/?badge=latest) fastNLP is a modular Natural Language Processing system based on PyTorch, for fast development of NLP tools. It divides the NLP model based on deep learning into different modules. These modules fall into 4 categories: encoder, interaction, aggregation and decoder, while each category contains different implemented modules. Encoder modules encode the input into some abstract representation, interaction modules make the information in the representation interact with each other, aggregation modules aggregate and reduce information, and decoder modules decode the representation into the output. Most current NLP models could be built on these modules, which vastly simplifies the process of developing NLP models. The architecture of fastNLP is as the figure below: