Wang Yijun (Receiling)

Receiling

Geek Repo

Company:Shanghai Jiao Tong University

Location:Shanghai

Home Page:https://receiling.github.io/

Github PK Tool:Github PK Tool


Organizations
Thinklab-SJTU

Wang Yijun's repositories

UniRE

Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021. It is based on our NERE toolkit (https://github.com/Receiling/NERE).

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DP3

Deep Point Process by PyTorch

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PSPE

Pretrained Span and span Pair Encoder, code for "Pre-training Entity Relation Encoder with Intra-span and Inter-spanInformation.", EMNLP2020. It is based on our NERE toolkit (https://github.com/Receiling/NERE).

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NERE

Universal Neural Entity Relation Extraction Toolkit.

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ENPAR

Code for "ENPAR: Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction.", EACL2021. It is based on our NERE toolkit (https://github.com/Receiling/NERE).

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ecnu_compiler

ecnu compiler project

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AlgorithmVisualizer

Algorithm Visualizer

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AntNRE

AntNLP Neural Relation Extraction

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antu

Universal data IO and neural network modules in NLP tasks.

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cinema_ticketing

ecnu database project: a cinema ticketing system(customer + employee)

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Code_Runner

这是是一个在线的运行提交的代码的小型网站,现在只是一个demo。。。

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chinese_correct_wsd

简易的中文纠错和消歧

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ecnu_ailab

ecnu aritificial intelligence experiment

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ecnu_oslab

east china normal university operating system lab

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ImageRecognition

Image Recognition Project, Machine Learning Practice

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InstructGLM

ChatGLM-6B 指令学习|指令数据|Instruct

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OnlineJudge

Open source online judge based on Python, Django and Docker. | 开源 Online Judge | 如果您在使用这个系统,请watch或者加入QQ群,这样可以收到最新的更新 | 2.0重构版正在开发

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PPDS

Point Process Data Set

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python-scraping

Code samples from the book Web Scraping with Python http://shop.oreilly.com/product/0636920034391.do

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seminar

antnlp seminar materials

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task2-cilin

基于同义词词林实现上下位关系自动抽取

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transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

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trl

Train transformer language models with reinforcement learning.

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vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

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