AI Rank
← 开源
NTMC-Community

MatchZoo

Facilitating the design, comparison and sharing of deep text matching models.

Model DevelopmentDeep Learning FrameworksPython
在 GitHub 打开
增长势头
+024 小时新增 Star0.0%
3.85k
Star
888
Fork
+0
本周
26
贡献者
创建于 2017-06-08 · 更新于 2026-09-30 · 今日第 7087 名
主要开发者
udusefaneshionpl8787
README

logo

MatchZoo Tweet

Facilitating the design, comparison and sharing of deep text matching models.

MatchZoo 是一个通用的文本匹配工具包，它旨在方便大家快速的实现、比较、以及分享最新的深度文本匹配模型。

Python 3.6 Pypi Downloads Documentation Status Build Status codecov License Requirements Status

🔥News: MatchZoo-py (PyTorch version of MatchZoo) is ready now.

The goal of MatchZoo is to provide a high-quality codebase for deep text matching research, such as document retrieval, question answering, conversational response ranking, and paraphrase identification. With the unified data processing pipeline, simplified model configuration and automatic hyper-parameters tunning features equipped, MatchZoo is flexible and easy to use.

Tasks

Text 1

Text 2

Objective

Paraphrase Identification

string 1

string 2

classification

Textual Entailment

text

hypothesis

classification

Question Answer

question

answer

classification/ranking

Conversation

dialog

response

classification/ranking

Information Retrieval

query

document

ranking

Get Started in 60 Seconds

To train a Deep Semantic Structured Model, import matchzoo and prepare input data.

import matchzoo as mz

train_pack = mz.datasets.wiki_qa.load_data('train', task='ranking')
valid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking')

Preprocess your input data in three lines of code, keep track parameters to be passed into the model.

preprocessor = mz.preprocessors.DSSMPreprocessor()
train_processed = preprocessor.fit_transform(train_pack)
valid_processed = preprocessor.transform(valid_pack)

Make use of MatchZoo customized loss functions and evaluation metrics:

ranking_task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=4))
ranking_task.metrics = [
    mz.metrics.NormalizedDiscountedCumulativeGain(k=3),
    mz.metrics.MeanAveragePrecision()
]

Initialize the model, fine-tune the hyper-parameters.

model = mz.models.DSSM()
model.params['input_shapes'] = preprocessor.context['input_shapes']
model.params['task'] = ranking_task
model.guess_and_fill_missing_params()
model.build()
model.compile()

Generate pair-wise training data on-the-fly, evaluate model performance using customized callbacks on validation data.

train_generator = mz.PairDataGenerator(train_processed, num_dup=1, num_neg=4, batch_size=64, shuffle=True)
valid_x, valid_y = valid_processed.unpack()
evaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=len(valid_x))
history = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=5, use_multiprocessing=False)

References

Tutorials

English Documentation

中文文档

If you're interested in the cutting-edge research progress, please take a look at awaresome neural models for semantic match.

Install

MatchZoo is dependent on Keras and Tensorflow. Two ways to install MatchZoo:

Install MatchZoo from Pypi:

pip install matchzoo

Install MatchZoo from the Github source:

git clone https://github.com/NTMC-Community/MatchZoo.git
cd MatchZoo
python setup.py install

Models

  1. DRMM: this model is an implementation of A Deep Relevance Matching Model for Ad-hoc Retrieval.

  2. MatchPyramid: this model is an implementation of Text Matching as Image Recognition

  3. ARC-I: this model is an implementation of Convolutional Neural Network Architectures for Matching Natural Language Sentences

  4. DSSM: this model is an implementation of Learning Deep Structured Semantic Models for Web Search using Clickthrough Data

  5. CDSSM: this model is an implementation of Learning Semantic Representations Using Convolutional Neural Networks for Web Search

  6. ARC-II: this model is an implementation of Convolutional Neural Network Architectures for Matching Natural Language Sentences

  7. MV-LSTM:this model is an implementation of A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

  8. aNMM: this model is an implementation of aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model

  9. DUET: this model is an implementation of Learning to Match Using Local and Distributed Representations of Text for Web Search

  10. K-NRM: this model is an implementation of End-to-End Neural Ad-hoc Ranking with Kernel Pooling

  11. CONV-KNRM: this model is an implementation of Convolutional neural networks for soft-matching n-grams in ad-hoc search

  12. models under development: Match-SRNN, DeepRank, BiMPM ....

Citation

If you use MatchZoo in your research, please use the following BibTex entry.

@inproceedings{Guo:2019:MLP:3331184.3331403,
 author = {Guo, Jiafeng and Fan, Yixing and Ji, Xiang and Cheng, Xueqi},
 title = {MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching},
 booktitle = {Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
 series = {SIGIR'19},
 year = {2019},
 isbn = {978-1-4503-6172-9},
 location = {Paris, France},
 pages = {1297--1300},
 numpages = {4},
 url = {http://doi.acm.org/10.1145/3331184.3331403},
 doi = {10.1145/3331184.3331403},
 acmid = {3331403},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {matchzoo, neural network, text matching},
}

Development Team

​ ​ ​ ​

    ​ [![faneshion](https://github.com/faneshion.png?s=40)](https://github.com/faneshion)  

    ​ [Fan Yixing](http://www.bigdatalab.ac.cn/~fanyixing/) ​

Core Dev

    ASST PROF, ICT

​

     [![bwanglzu](https://github.com/bwanglzu.png?s=40)](https://github.com/bwanglzu)  

     [Wang Bo](https://github.com/bwanglzu) ​

Core Dev
M.S. TU Delft ​

    ​ [![uduse](https://github.com/uduse.png?s=36)](https://github.com/uduse)  

     [Wang Zeyi](https://github.com/uduse)

Core Dev
B.S. UC Davis ​

    ​ [![pl8787](https://github.com/pl8787.png?s=40)](https://github.com/pl8787)  

    ​ [Pang Liang](https://github.com/pl8787)

Core Dev

    ASST PROF, ICT

​

    ​ [![yangliuy](https://github.com/yangliuy.png?s=40)](https://github.com/yangliuy)  

    ​ [Yang Liu](https://github.com/yangliuy)

Core Dev

    PhD. UMASS

​

    ​ [![wqh17101](https://github.com/wqh17101.png?s=40)](https://github.com/wqh17101)  

    ​ [Wang Qinghua](https://github.com/wqh17101) ​

Documentation

    B.S. Shandong Univ.

​

    ​ [![ZizhenWang](https://github.com/ZizhenWang.png?s=40)](https://github.com/ZizhenWang)  

    ​ [Wang Zizhen](https://github.com/ZizhenWang) ​

Dev

    M.S. UCAS

​

    ​ [![lixinsu](https://github.com/lixinsu.png?s=40)](https://github.com/lixinsu)  

    ​ [Su Lixin](https://github.com/lixinsu)

Dev

    PhD. UCAS

​

    ​ [![zhouzhouyang520](https://github.com/zhouzhouyang520.png?s=40)](https://github.com/zhouzhouyang520)  

    ​ [Yang Zhou](https://github.com/zhouzhouyang520) ​

Dev

    M.S. CQUT

​

    ​ [![rgtjf](https://github.com/rgtjf.png?s=36)](https://github.com/rgtjf)  

    ​ [Tian Junfeng](https://github.com/rgtjf) ​

Dev

    M.S. ECNU

​

Contribution

Please make sure to read the Contributing Guide before creating a pull request. If you have a MatchZoo-related paper/project/compnent/tool, send a pull request to this awesome list!

Thank you to all the people who already contributed to MatchZoo!

Jianpeng Hou, Lijuan Chen, Yukun Zheng, Niuguo Cheng, Dai Zhuyun, Aneesh Joshi, Zeno Gantner, Kai Huang, stanpcf, ChangQF, Mike Kellogg

Project Organizers

  • Jiafeng Guo
    • Institute of Computing Technology, Chinese Academy of Sciences
    • Homepage
  • Yanyan Lan
    • Institute of Computing Technology, Chinese Academy of Sciences
    • Homepage
  • Xueqi Cheng
    • Institute of Computing Technology, Chinese Academy of Sciences
    • Homepage

License

Apache-2.0

Copyright (c) 2015-present, Yixing Fan (faneshion)