基于“Seq2Seq+前缀树”的知识图谱问答

Last update: Dec 12, 2022

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Text Data & NLP KgCLUE-bert4keras

Overview

KgCLUE-bert4keras

基于“Seq2Seq+前缀树”的知识图谱问答

简介

博客：https://kexue.fm/archives/8802

环境

软件：bert4keras>=0.10.8
硬件：目前的结果是用一张Titan RTX（24G）跑出来的。

运行

第一次运行的时候，会给知识库构建前缀树，然后保存下来，这个过程大概需要30分钟左右；
如果是第二次运行，那么就会自动加载保存好的前缀树，这个过程大概需要5分钟左右；
保存下载的前缀树文件大概1.8G，加载到运行环境中，大概需要30G内存；
每个epoch的训练时间是很快的，反而是验证效果时间比较长，跑完训练和测试，大概需要1～2小时。

交流

QQ交流群：808623966，微信群请加机器人微信号spaces_ac_cn

Owner

苏剑林(Jianlin Su)

科学爱好者

GitHub Repository

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基于“Seq2Seq+前缀树”的知识图谱问答

Related tags

Overview

KgCLUE-bert4keras

简介

环境

运行

交流

Owner

苏剑林(Jianlin Su)

This library is testing the ethics of language models by using natural adversarial texts.

A text augmentation tool for named entity recognition.

基于Transformer的单模型、多尺度的VAE模型

In this project, we compared Spanish BERT and Multilingual BERT in the Sentiment Analysis task.

PyTorch code for EMNLP 2019 paper "LXMERT: Learning Cross-Modality Encoder Representations from Transformers".

PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

Baseline code for Korean open domain question answering(ODQA)

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Main repository for the chatbot Bobotinho.

DVC-NLP-Simple-usecase

Contains links to publicly available datasets for modeling health outcomes using speech and language.

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