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colincen's Projects

advanced-java icon advanced-java

😮 互联网 Java 工程师进阶知识完全扫盲:涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识,后端同学必看,前端同学也可学习

chineseglue icon chineseglue

Language Understanding Evaluation benchmark for Chinese: datasets, baselines, pre-trained models,corpus and leaderboard

coach icon coach

Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling (ACL-2020)

convlab icon convlab

DSTC8 Track 1 Task 1 End-to-End Multi-Domain Dialog Challenge Result:

cppprimer icon cppprimer

:books: Solutions for C++ Primer 5th exercises.

crosswoz icon crosswoz

A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

entity-recognition-datasets icon entity-recognition-datasets

A collection of corpora for named entity recognition (NER) and entity recognition tasks. These annotated datasets cover a variety of languages, domains and entity types.

fewshottagging icon fewshottagging

Code for ACL2020 paper: Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network

hscrf-pytorch icon hscrf-pytorch

ACL 2018: Hybrid semi-Markov CRF for Neural Sequence Labeling (http://aclweb.org/anthology/P18-2038)

java icon java

存放JAVA开发的设计**、算法:《剑指Offer》、《编程珠玑》、《深入理解Java虚拟机:JVM高级特性与最佳实践》、《重构-改善既有代码的设计 中文版》、《clean_code(中文完整版)》、《Java编程**(第4版)》、《Java核心技术 卷I (第8版)》、《Quartz_Job+Scheduling_Framework》;一些大的上传不上来的文件在README

metadialog icon metadialog

Platform for few-shot natural language processing: Text Classification, Sequene Labeling.

miaosha icon miaosha

⭐⭐⭐⭐秒杀系统设计与实现.互联网工程师进阶与分析🙋🐓

mine_pytorch icon mine_pytorch

MINE: Mutual Information Neural Estimation in pytorch (unofficial)

nlp-projects icon nlp-projects

word2vec, sentence2vec, machine reading comprehension, dialog system, text classification, pretrained language model (i.e., XLNet, BERT, ELMo, GPT), sequence labeling, information retrieval, information extraction (i.e., entity, relation and event extraction), knowledge graph, text generation, network embedding

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