{"id":13595577,"url":"https://github.com/NTMC-Community/MatchZoo","last_synced_at":"2025-04-09T13:32:31.650Z","repository":{"id":41165385,"uuid":"93728104","full_name":"NTMC-Community/MatchZoo","owner":"NTMC-Community","description":"Facilitating the design, comparison and sharing of deep text matching 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Resources"],"sub_categories":["Some Retrieval Toolkits"],"readme":"\u003cdiv align='center'\u003e\n\u003cimg src=\"https://github.com/NTMC-Community/MatchZoo/blob/master/artworks/matchzoo-logo.png?raw=true\" width = \"400\"  alt=\"logo\" align=\"center\" /\u003e\n\u003c/div\u003e\n\n# MatchZoo [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=MatchZoo:%20deep%20learning%20for%20semantic%20matching\u0026url=https://github.com/NTMC-Community/MatchZoo)\n\n\u003e Facilitating the design, comparison and sharing of deep text matching models.\u003cbr/\u003e\n\u003e 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。\n\n[![Python 3.6](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg)](https://www.python.org/downloads/release/python-360/)\n[![Pypi Downloads](https://img.shields.io/pypi/dm/matchzoo.svg?label=pypi)](https://pypi.org/project/MatchZoo/)\n[![Documentation Status](https://readthedocs.org/projects/matchzoo/badge/?version=master)](https://matchzoo.readthedocs.io/en/master/?badge=master)\n[![Build Status](https://travis-ci.org/NTMC-Community/MatchZoo.svg?branch=master)](https://travis-ci.org/NTMC-Community/MatchZoo/)\n[![codecov](https://codecov.io/gh/NTMC-Community/MatchZoo/branch/master/graph/badge.svg)](https://codecov.io/gh/NTMC-Community/MatchZoo)\n[![License](https://img.shields.io/badge/License-Apache%202.0-yellowgreen.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Requirements Status](https://requires.io/github/NTMC-Community/MatchZoo/requirements.svg?branch=master)](https://requires.io/github/NTMC-Community/MatchZoo/requirements/?branch=master)\n---\n🔥**News: [MatchZoo-py](https://github.com/NTMC-Community/MatchZoo-py) (PyTorch version of MatchZoo) is ready now.**\n\nThe 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.\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003cth width=30%, bgcolor=#999999 \u003eTasks\u003c/th\u003e \n    \u003cth width=20%, bgcolor=#999999\u003eText 1\u003c/th\u003e\n    \u003cth width=\"20%\", bgcolor=#999999\u003eText 2\u003c/th\u003e\n    \u003cth width=\"20%\", bgcolor=#999999\u003eObjective\u003c/th\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e Paraphrase Identification \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e string 1 \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e string 2 \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e classification \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e Textual Entailment \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e text \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e hypothesis \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e classification \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e Question Answer \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e question \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e answer \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e classification/ranking \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e Conversation \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e dialog \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e response \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e classification/ranking \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e Information Retrieval \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e query \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e document \u003c/td\u003e\n    \u003ctd align=\"center\", bgcolor=#eeeeee\u003e ranking \u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n## Get Started in 60 Seconds\n\nTo train a [Deep Semantic Structured Model](https://www.microsoft.com/en-us/research/project/dssm/), import matchzoo and prepare input data.\n\n```python\nimport matchzoo as mz\n\ntrain_pack = mz.datasets.wiki_qa.load_data('train', task='ranking')\nvalid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking')\n```\n\nPreprocess your input data in three lines of code, keep track parameters to be passed into the model.\n\n```python\npreprocessor = mz.preprocessors.DSSMPreprocessor()\ntrain_processed = preprocessor.fit_transform(train_pack)\nvalid_processed = preprocessor.transform(valid_pack)\n```\n\nMake use of MatchZoo customized loss functions and evaluation metrics:\n\n```python\nranking_task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=4))\nranking_task.metrics = [\n    mz.metrics.NormalizedDiscountedCumulativeGain(k=3),\n    mz.metrics.MeanAveragePrecision()\n]\n```\n\nInitialize the model, fine-tune the hyper-parameters.\n\n```python\nmodel = mz.models.DSSM()\nmodel.params['input_shapes'] = preprocessor.context['input_shapes']\nmodel.params['task'] = ranking_task\nmodel.guess_and_fill_missing_params()\nmodel.build()\nmodel.compile()\n```\n\nGenerate pair-wise training data on-the-fly, evaluate model performance using customized callbacks on validation data.\n\n```python\ntrain_generator = mz.PairDataGenerator(train_processed, num_dup=1, num_neg=4, batch_size=64, shuffle=True)\nvalid_x, valid_y = valid_processed.unpack()\nevaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=len(valid_x))\nhistory = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=5, use_multiprocessing=False)\n```\n\n## References\n[Tutorials](https://github.com/NTMC-Community/MatchZoo/tree/master/tutorials)\n\n[English Documentation](https://matchzoo.readthedocs.io/en/master/)\n\n[中文文档](https://matchzoo.readthedocs.io/zh/latest/)\n\nIf you're interested in the cutting-edge research progress, please take a look at [awaresome neural models for semantic match](https://github.com/NTMC-Community/awaresome-neural-models-for-semantic-match).\n\n## Install\n\nMatchZoo is dependent on [Keras](https://github.com/keras-team/keras) and [Tensorflow](https://github.com/tensorflow/tensorflow). Two ways to install MatchZoo:\n\n**Install MatchZoo from Pypi:**\n\n```python\npip install matchzoo\n```\n\n**Install MatchZoo from the Github source:**\n\n```\ngit clone https://github.com/NTMC-Community/MatchZoo.git\ncd MatchZoo\npython setup.py install\n```\n\n\n## Models\n\n1. [DRMM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/drmm.py): this model is an implementation of \u003ca href=\"http://www.bigdatalab.ac.cn/~gjf/papers/2016/CIKM2016a_guo.pdf\"\u003eA Deep Relevance Matching Model for Ad-hoc Retrieval\u003c/a\u003e.\n\n2. [MatchPyramid](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/match_pyramid.py): this model is an implementation of \u003ca href=\"https://arxiv.org/abs/1602.06359\"\u003e Text Matching as Image Recognition\u003c/a\u003e\n\n3. [ARC-I](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/arci.py): this model is an implementation of \u003ca href=\"https://arxiv.org/abs/1503.03244\"\u003eConvolutional Neural Network Architectures for Matching Natural Language Sentences\u003c/a\u003e\n\n4. [DSSM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/dssm.py): this model is an implementation of \u003ca href=\"https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf\"\u003eLearning Deep Structured Semantic Models for Web Search using Clickthrough Data\u003c/a\u003e\n\n5. [CDSSM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/cdssm.py): this model is an implementation of \u003ca href=\"https://www.microsoft.com/en-us/research/publication/learning-semantic-representations-using-convolutional-neural-networks-for-web-search/\"\u003eLearning Semantic Representations Using Convolutional Neural Networks for Web Search\u003c/a\u003e\n\n6. [ARC-II](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/arcii.py): this model is an implementation of \u003ca href=\"https://arxiv.org/abs/1503.03244\"\u003eConvolutional Neural Network Architectures for Matching Natural Language Sentences\u003c/a\u003e\n\n7. [MV-LSTM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/mvlstm.py):this model is an implementation of \u003ca href=\"https://arxiv.org/abs/1511.08277\"\u003eA Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations\u003c/a\u003e\n\n8. [aNMM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/anmm.py): this model is an implementation of \u003ca href=\"http://maroo.cs.umass.edu/pub/web/getpdf.php?id=1240\"\u003eaNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model\u003c/a\u003e\n\n9. [DUET](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/duet.py): this model is an implementation of \u003ca href=\"https://dl.acm.org/citation.cfm?id=3052579\"\u003eLearning to Match Using Local and Distributed Representations of Text for Web Search\u003c/a\u003e\n\n10. [K-NRM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/knrm.py): this model is an implementation of \u003ca href=\"https://arxiv.org/abs/1706.06613\"\u003eEnd-to-End Neural Ad-hoc Ranking with Kernel Pooling\u003c/a\u003e\n\n11. [CONV-KNRM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/conv_knrm.py): this model is an implementation of \u003ca href=\"http://www.cs.cmu.edu/~zhuyund/papers/WSDM_2018_Dai.pdf\"\u003eConvolutional neural networks for soft-matching n-grams in ad-hoc search\u003c/a\u003e\n\n12. models under development: \u003ca href=\"https://arxiv.org/abs/1604.04378\"\u003eMatch-SRNN\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1710.05649\"\u003eDeepRank\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1702.03814\"\u003eBiMPM\u003c/a\u003e .... \n\n\n## Citation\n\nIf you use MatchZoo in your research, please use the following BibTex entry.\n\n```\n@inproceedings{Guo:2019:MLP:3331184.3331403,\n author = {Guo, Jiafeng and Fan, Yixing and Ji, Xiang and Cheng, Xueqi},\n title = {MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching},\n booktitle = {Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval},\n series = {SIGIR'19},\n year = {2019},\n isbn = {978-1-4503-6172-9},\n location = {Paris, France},\n pages = {1297--1300},\n numpages = {4},\n url = {http://doi.acm.org/10.1145/3331184.3331403},\n doi = {10.1145/3331184.3331403},\n acmid = {3331403},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords = {matchzoo, neural network, text matching},\n} \n```\n\n\n## Development Team\n\n ​ ​ ​ ​\n\u003ctable border=\"0\"\u003e\n  \u003ctbody\u003e\n    \u003ctr align=\"center\"\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/faneshion\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/faneshion.png?s=40\" alt=\"faneshion\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"http://www.bigdatalab.ac.cn/~fanyixing/\"\u003eFan Yixing\u003c/a\u003e ​\n        \u003cp\u003eCore Dev\u003cbr\u003e\n        ASST PROF, ICT\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n         \u003ca href=\"https://github.com/bwanglzu\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/bwanglzu.png?s=40\" alt=\"bwanglzu\"\u003e\u003c/a\u003e\u003cbr\u003e\n         \u003ca href=\"https://github.com/bwanglzu\"\u003eWang Bo\u003c/a\u003e ​\n        \u003cp\u003eCore Dev\u003cbr\u003e M.S. TU Delft\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/uduse\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/uduse.png?s=36\" alt=\"uduse\"\u003e\u003c/a\u003e\u003cbr\u003e\n         \u003ca href=\"https://github.com/uduse\"\u003eWang Zeyi\u003c/a\u003e\n         \u003cp\u003eCore Dev\u003cbr\u003e B.S. UC Davis\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/pl8787\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/pl8787.png?s=40\" alt=\"pl8787\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/pl8787\"\u003ePang Liang\u003c/a\u003e\n        \u003cp\u003eCore Dev\u003cbr\u003e\n        ASST PROF, ICT\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/yangliuy\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/yangliuy.png?s=40\" alt=\"yangliuy\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/yangliuy\"\u003eYang Liu\u003c/a\u003e\n        \u003cp\u003eCore Dev\u003cbr\u003e\n        PhD. UMASS\u003c/p\u003e​\n      \u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr align=\"center\"\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/wqh17101\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/wqh17101.png?s=40\" alt=\"wqh17101\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/wqh17101\"\u003eWang Qinghua\u003c/a\u003e ​\n        \u003cp\u003eDocumentation\u003cbr\u003e\n        B.S. Shandong Univ.\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/ZizhenWang\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/ZizhenWang.png?s=40\" alt=\"ZizhenWang\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/ZizhenWang\"\u003eWang Zizhen\u003c/a\u003e ​\n        \u003cp\u003eDev\u003cbr\u003e\n        M.S. UCAS\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/lixinsu\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/lixinsu.png?s=40\" alt=\"lixinsu\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/lixinsu\"\u003eSu Lixin\u003c/a\u003e\n        \u003cp\u003eDev\u003cbr\u003e\n        PhD. UCAS\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/zhouzhouyang520\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/zhouzhouyang520.png?s=40\" alt=\"zhouzhouyang520\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/zhouzhouyang520\"\u003eYang Zhou\u003c/a\u003e ​\n        \u003cp\u003eDev\u003cbr\u003e\n        M.S. CQUT\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/rgtjf\"\u003e\u003cimg width=\"40\" height=\"40\" src=\"https://github.com/rgtjf.png?s=36\" alt=\"rgtjf\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/rgtjf\"\u003eTian Junfeng\u003c/a\u003e ​\n        \u003cp\u003eDev\u003cbr\u003e\n        M.S. ECNU\u003c/p\u003e​\n      \u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\n\n\n## Contribution\n\nPlease make sure to read the [Contributing Guide](./CONTRIBUTING.md) before creating a pull request. If you have a MatchZoo-related paper/project/compnent/tool, send a pull request to [this awesome list](https://github.com/NTMC-Community/awaresome-neural-models-for-semantic-match)!\n\nThank you to all the people who already contributed to MatchZoo!\n\n[Jianpeng Hou](https://github.com/HouJP), [Lijuan Chen](https://github.com/githubclj), [Yukun Zheng](https://github.com/zhengyk11), [Niuguo Cheng](https://github.com/niuox), [Dai Zhuyun](https://github.com/AdeDZY), [Aneesh Joshi](https://github.com/aneesh-joshi), [Zeno Gantner](https://github.com/zenogantner), [Kai Huang](https://github.com/hkvision), [stanpcf](https://github.com/stanpcf), [ChangQF](https://github.com/ChangQF), [Mike Kellogg\n](https://github.com/wordreference)\n\n\n\n\n## Project Organizers\n\n- Jiafeng Guo\n  * Institute of Computing Technology, Chinese Academy of Sciences\n  * [Homepage](http://www.bigdatalab.ac.cn/~gjf/)\n- Yanyan Lan\n  * Institute of Computing Technology, Chinese Academy of Sciences\n  * [Homepage](http://www.bigdatalab.ac.cn/~lanyanyan/)\n- Xueqi Cheng\n  * Institute of Computing Technology, Chinese Academy of Sciences\n  * [Homepage](http://www.bigdatalab.ac.cn/~cxq/)\n\n\n## License\n\n[Apache-2.0](https://opensource.org/licenses/Apache-2.0)\n\nCopyright (c) 2015-present, Yixing Fan (faneshion)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNTMC-Community%2FMatchZoo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FNTMC-Community%2FMatchZoo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNTMC-Community%2FMatchZoo/lists"}