deepke-main.rar

<p align="center"> <a href="https://github.com/zjunlp/deepke"> <img src="pics/logo.png" width="400"/></a> <p> <p align="center"> <a href="http://deepke.zjukg.cn"> <img alt="Documentation" src="https://img.shields.io/badge/demo-website-blue"> </a> <a href="https://pypi.org/project/deepke/#files"> <img alt="PyPI" src="https://img.shields.io/pypi/v/deepke"> </a> <a href="https://github.com/zjunlp/DeepKE/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/zjunlp/deepke"> </a> <a href="http://zjunlp.github.io/DeepKE"> <img alt="Documentation" src="https://img.shields.io/badge/doc-website-red"> </a> <a href="https://colab.research.google.com/drive/1vS8YJhJltzw3hpJczPt24O0Azcs3ZpRi?usp=sharing"> <img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"> </a> </p> <p align="center"> <b> English | <a href="https://github.com/zjunlp/DeepKE/blob/main/README_CN.md">简体中文</a> </b> </p> <h1 align="center"> <p>A Deep Learning Based Knowledge Extraction Toolkit<br>for Knowledge Graph Construction</p> </h1> [DeepKE](https://arxiv.org/pdf/2201.03335.pdf) is a knowledge extraction toolkit for knowledge graph construction supporting **cnSchema**，**low-resource**, **document-level** and **multimodal** scenarios for *entity*, *relation* and *attribute* extraction. We provide [documents](https://zjunlp.github.io/DeepKE/), [Google Colab tutorials](https://colab.research.google.com/drive/1vS8YJhJltzw3hpJczPt24O0Azcs3ZpRi?usp=sharing), [online demo](http://deepke.zjukg.cn/), [paper](https://arxiv.org/pdf/2201.03335.pdf), [slides](https://drive.google.com/file/d/1IIeIZAbVduemqXc4zD40FUMoPHCJinLy/view?usp=sharing) and [poster](https://drive.google.com/file/d/1vd7xVHlWzoAxivN4T5qKrcqIGDcSM1_7/view?usp=sharing) for beginners. **Reading Materials**: Data-Efficient Knowledge Graph Construction, 高效知识图谱构建 ([Tutorial on CCKS 2022](http://sigkg.cn/ccks2022/?page_id=24)) \[[slides](https://drive.google.com/drive/folders/1xqeREw3dSiw-Y1rxLDx77r0hGUvHnuuE)\] Efficient and Robust Knowledge Graph Construction ([Tutorial on AACL-IJCNLP 2022](https://www.aacl2022.org/Program/tutorials)) \[[slides](https://github.com/NLP-Tutorials/AACL-IJCNLP2022-KGC-Tutorial)\] PromptKG Family: a Gallery of Prompt Learning & KG-related Research Works, Toolkits, and Paper-list [[Resources](https://github.com/zjunlp/PromptKG)\] Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective \[[Survey](https://arxiv.org/abs/2202.08063)\]\[[Paper-list](https://github.com/zjunlp/Low-resource-KEPapers)\] Reasoning with Language Model Prompting \[[Survey](https://arxiv.org/abs/2212.09597)\]\[[Paper-list](https://github.com/zjunlp/Prompt4ReasoningPapers)\]\[[ppt](https://github.com/zjunlp/Prompt4ReasoningPapers/blob/main/tutorial.pdf)\] **Related Toolkit**: [Doccano](https://github.com/doccano/doccano)、[MarkTool](https://github.com/FXLP/MarkTool)、[LabelStudio](https://labelstud.io/ ): Data Annotation Toolkits [LambdaKG](https://github.com/zjunlp/PromptKG/tree/main/lambdaKG): A library and benchmark for PLM-based KG embeddings [EasyInstruct](https://github.com/zjunlp/EasyInstruct): An easy-to-use framework to instruct Large Language Models # Table of Contents * [What's New](#whats-new) * [Prediction Demo](#prediction-demo) * [Model Framework](#model-framework) * [Quick Start](#quick-start) * [Requirements](#requirements) * [Introduction of Three Functions](#introduction-of-three-functions) * [1. Named Entity Recognition](#1-named-entity-recognition) * [2. Relation Extraction](#2-relation-extraction) * [3. Attribute Extraction](#3-attribute-extraction) * [Notebook Tutorial](#notebook-tutorial) * [Tips](#tips) * [To do](#to-do) * [Citation](#citation) * [Contributors](#contributors) * [Other Knowledge Extraction Open-Source Projects](#other-knowledge-extraction-open-source-projects) <br> # What's New ## Apr, 2023 * We have added new models, including [CP-NER](https://github.com/zjunlp/DeepKE/blob/main/example/ner/cross), [ASP(EMNLP'22)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/ASP), [PRGC(ACL'21)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/PRGC), [PURE(NAACL'21)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/PURE), provided [event extraction](https://github.com/zjunlp/DeepKE/tree/main/example/ee/standard) capabilities (Chinese and English), and offered compatibility with higher versions of Python packages (e.g., Transformers). ## Feb, 2023 * We have supported using [LLM](https://github.com/zjunlp/DeepKE/tree/main/example/llm) (GPT-3) with in-context learning (based on [EasyInstruct](https://github.com/zjunlp/EasyInstruct)) & data generation, added a NER model [W2NER(AAAI'22)](https://github.com/zjunlp/DeepKE/tree/main/example/ner/standard/w2ner). ## Nov, 2022 * Add data [annotation instructions](https://github.com/zjunlp/DeepKE/blob/main/README_TAG.md) for entity recognition and relation extraction, automatic labelling of weakly supervised data ([entity extraction](https://github.com/zjunlp/DeepKE/tree/main/example/ner/prepare-data) and [relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/prepare-data)), and optimize [multi-GPU training](https://github.com/zjunlp/DeepKE/tree/main/example/re/standard). ## Sept, 2022 * The paper [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://arxiv.org/abs/2201.03335) has been accepted by the EMNLP 2022 System Demonstration Track. ## Aug, 2022 * We have added [data augmentation](https://github.com/zjunlp/DeepKE/tree/main/example/re/few-shot/DA) (Chinese, English) support for [low-resource relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/few-shot). ## June, 2022 * We have added multimodal support for [entity](https://github.com/zjunlp/DeepKE/tree/main/example/ner/multimodal) and [relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/multimodal). ## May, 2022 * We have released [DeepKE-cnschema](https://github.com/zjunlp/DeepKE/blob/main/README_CNSCHEMA.md) with off-the-shelf knowledge extraction models. ## Jan, 2022 * We have released a paper [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://arxiv.org/abs/2201.03335) ## Dec, 2021 * We have added `dockerfile` to create the enviroment automatically. ## Nov, 2021 * The demo of DeepKE, supporting real-time extration without deploying and training, has been released. * The documentation of DeepKE, containing the details of DeepKE such as source codes and datasets, has been released. ## Oct, 2021 * `pip install deepke` * The codes of deepke-v2.0 have been released. ## Aug, 2019 * The codes of deepke-v1.0 have been released. ## Aug, 2018 * The project DeepKE startup and codes of deepke-v0.1 have been released. <br> # Prediction Demo There is a demonstration of prediction. The GIF file is created by [Terminalizer](https://github.com/faressoft/terminalizer). Get the [code](https://drive.google.com/file/d/1r4tWfAkpvynH3CBSgd-XG79rf-pB-KR3/view?usp=share_link). <img src="pics/demo.gif" width="636" height="494" align=center> <br> # Model Framework <h3 align="center"> <img src="pics/architectures.png"> </h3> - DeepKE contains a unified framework for **named entity recognition**, **relation extraction** and **attribute extraction**, the three knowledge extraction functions. - Each task can be implemented in different scenarios. For example, we can achieve relation extraction in **standard**, **low-resource (few-shot)**, **document-level** and **multimodal** settings. - Each application scenario comprises of three components: **Data** including Tokenizer, Preprocessor and Loader, **Model** including Module, Encoder and Forwarder, **Core** including Training, Evaluation and Prediction. <br> #