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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" lang="en">
<head>
<meta http-equiv="content-type" content="application/xhtml+xml; charset=UTF-8" />
<meta name="description" content="Lei Li" />
<meta name="keywords" content="machine learning deep learning natural language processing probabilistic programming" />
<meta name="author" content="Lei Li" />
<link rel="stylesheet" type="text/css" href="style/origo.css" media="all" />
<!--link rel="stylesheet" type="text/css" href="style/style.css" media="all" /-->
<title>Lei LI's Resource</title>
</head>
<body class="blue light">
<div id="layout">
<ul class="menu">
<li><a href="index.html">Home</a></li>
<li><a href="student.html">Student</a></li>
<li><a href="research.html">Research</a></li>
<li><a href="teaching.html">Teaching</a></li>
<li><a href="pubs.html">Publication</a></li>
<li class="active"><a href="resource.html">Resource</a></li>
<li><a href="media.html">Media</a></li>
<li><a href="leili-bio.html">Bio</a></li>
<li><a href="https://lileicc.github.io/blog/">Blog</a></li>
</ul>
<div class="row">
<div class="main col">
<h1> Software and Toolbox </h1>
<ul>
<li><a href="https://github.com/bytedance/lightseq">LightSeq</a>: A
High Performance Training and Inference Library for Transformer models.
It is widely used for machine translation, text generation, visual recognition, and more.
With the custom CUDA
implementation, it achieves 10x speed-up over the original
tensorflow seq2seq package, and faster than other implementations.
</li>
<li> <a href="https://github.com/bytedance/neurst"> NeurST </a>: A
toolbok with readily available models for neural machine
translation and speech-to-text translation. </li>
<li><a href="https://bayesianlogic.github.io/">BLOG</a>: a
probabilistic programming language for machine learning</li>
<li><a href="https://github.com/lileicc/swift">Swift</a>: a compiler
for the probabilistic programming language BLOG.</li>
<li><a href="software/dynammo-r346.zip">DynaMMo</a>: learning
toolbox for multi-dimensional co-evolving time series. <a href="https://github.com/lileicc/dynammo">github
page</a></li>
<li><a href="software/clds-r347.zip">CLDS</a>: complex-valued linear
dynamical system</li>
<li><a href="software/plif-r345.zip">PLiF</a>: time-shift-invariant
feature extraction for time series </li>
<li><a href="software/bolero-r349.zip">BoLeRO</a>: human motion
capture occlution recovering</li>
<li><a href="paralearn/index.html">paralearn</a>: a parallel
algorithm for learning Markov models and linear dynamical systems
(i.e. Kalman filter) </li>
<li><a href="software/mlds-r662.zip">MLDS</a>: learning dynamical
model for tensor time series </li>
</ul>
<h1>Dataset</h1>
<ul>
<li> TTNews: a dataset for Chinese document summarization. 50,000
news articles with summary for training, and 4,000 news articles
for testing. [<a href="http://tcci.ccf.org.cn/conference/2018/dldoc/taskgline03.pdf">Task
description</a>] [<a href="https://pan.baidu.com/s/1bppQ4z1">Training
data</a>] [<a href="https://www.dropbox.com/s/luizl5rftml05nc/nlpcc_summarization_2017-2018_evaluation.zip?dl=0">Testing
data and evaluation script</a>] [Reports from <a href="pubs/hua2017overview.pdf">NLPCC2017</a>
and <a href="pubs/li2018overview.pdf">NLPCC2018</a>] </li>
<li> CNewSum: an extended version of TTNews for Chinese document
summarization. It includes 304,307 documents and human-written
summaries. It includes additional adequacy-level and
deducibility-level labels. [<a href="https://dqwang122.github.io/projects/CNewSum/">Project
URL</a>] </li>
<li>MLGSum: a multilingual text summarization corpus with 1.2
million articles in 12 languages. Average length per article is
570 words. [<a href="https://dqwang122.github.io/projects/CALMS/">Project
URL] </a> [<a href="https://drive.google.com/file/d/1i9xfOkQ60kixj0rZ-kCo8UCo2fZ51fCY/view?usp=sharing">Data</a>]</li>
</ul>
<p> Please send me email if you find bugs or have comments! </p>
</div>
</div>
</div>
</body>
</html>