模型介绍
中文整理雅意IE大模型/YAYI UIE
[🤗Github Repo]
[🔗网页端]
介绍/Introduction
雅意信息抽取统一大模型 (YAYI-UIE)在百万级人工构造的高质量信息抽取数据上进行指令微调，统一训练信息抽取任务包括命名实体识别（NER），关系抽取（RE）和事件抽取（EE），实现通用、安全、金融、生物、医疗、商业、
个人、车辆、电影、工业、餐厅、科学等场景下结构化抽取。
通过雅意IE大模型的开源为促进中文预训练大模型开源社区的发展，贡献自己的一份力量，通过开源，与每一位合作伙伴共建雅意大模型生态。如果您想了解更多关于 YAYI UIE 模型的细节，我们建议您参阅 GitHub 仓库。更多技术细节，欢迎阅读我们的技术报告🔥YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction。
The YAYI Unified Information Extraction Large Language Model (YAYI UIE), fine-tuned on millions of high-quality data, integrates training across tasks such as Named Entity
Recognition (NER), Relation Extraction (RE), and Event Extraction (EE). The model is able to extract structured outputs across diverse fields including general, security,
finance, biology, medicine, business, personal, automotive, film, industry, restaurant, and science.
The open-source of YAYI-UIE aims to foster the growth of the Chinese PLM open-source community. We can't wait to collaborate with our partners to develop the YAYI Large Models ecosystem! For more details about the YAYI UIE, please refer to our GitHub repository. For more technical details, please read our technical report 🔥YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction.
数据集地址
数据集名称 大小 魔搭模型标识 下载地址
:---------- :----------: :----------: ----------:
YAYI-UIE Data 3.4G wenge-research/yayi uie sft data 数据集下载
模型推理/Model Inference
指令样例/Sample Prompts
- 实体抽取任务/NER tasks
- 关系抽取任务/RE tasks
- 事件抽取任务/EE tasks
模型zero-shot评测/Zero-shot Evaluation
- NER任务/NER tasks
AI，Literature，Music，Politics，Science为英文数据集，boson，clue，weibo为中文数据集
AI，Literature,Music,Politics and Science are English datasets; boson，clue and weibo are Chinese datasets
Model AI Literature Music Politics Science **EN** Average boson clue weibo **ZH** Average
- ----- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
davinci 2.97 9.87 13.83 18.42 10.04 11.03 - - - 31.09
ChatGPT 3.5 **54.4** **54.07** **61.24** **59.12** **63** **58.37** 38.53 25.44 29.3
UIE 31.14 38.97 33.91 46.28 41.56 38.37 40.64 34.91 40.79 38.78
USM 28.18 56 44.93 36.1 44.09 41.86 - - - -
InstructUIE 49 47.21 53.16 48.15 49.3 49.36 - - - -
DeepKE-LLM 13.76 20.18 14.78 33.86 9.19 18.35 25.96 4.44 25.2 18.53
YAYI-UIE 52.4 45.99 51.2 51.82 50.53 50.39 **49.25** **36.46** 36.78 **40.83**
- RE任务/RE Tasks
FewRe，Wiki-ZSL为英文数据集， SKE 2020，COAE2016，IPRE为中文数据集
FewRe and Wiki-ZSL are English datasets; SKE 2020, COAE2016 and IPRE are Chinese datasets
Model FewRel Wiki-ZSL **EN** Average SKE 2020 COAE2016 IPRE **ZH** Average
- ----- ------ ------ ------ ------ ------ ------ ------
ChatGPT 3.5 9.96 13.14 11.55 24.47 19.31 6.73 16.84
ZETT(T5-small) 30.53 31.74 31.14 - - - -
ZETT(T5-base) 33.71 31.17 32.44 - - - -
InstructUIE **39.55** 35.2 37.38 - - - -
DeepKE-LLM 17.46 15.33 16.40 0.4 6.56 9.75 5.57
YAYI-UIE 36.09 **41.07** **38.58** **70.8** **19.97** **22.97** **37.91**
- EE任务/EE Tasks
commodity news为英文数据集，FewFC，ccf law为中文数据集
commodity news is a English dataset, FewFC and ccf law are Chinese datasets
EET（事件类型判别 Event Type Extraction）
模型 commodity news FewFC ccf law **ZH** Average
- ----- ------ ------ ------ ------
ChatGPT 3.5 1.41 16.15 0 8.08
UIE - 50.23 2.16 26.20
InstructUIE **23.26** - - -
YAYI-UIE 12.45 **81.28** **12.87** **47.08**
EEA（事件论元抽取 Event Arguments Extraction）
模型 commodity news FewFC ccf law **ZH** Average
- ----- ------ ------ ------ ------
ChatGPT 3.5 8.6 44.4 44.57 44.49
UIE - 43.02 **60.85** 51.94
InstructUIE **21.78** - - -
YAYI-UIE 19.74 **63.06** 59.42 **61.24**
The chart illustrates the performance of our model on Chinese IE tasks in zero-shot setting.
相关协议/Terms and Conditions
局限性/Limitations
基于当前数据和基础模型训练得到的SFT模型，在效果上仍存在以下问题：
- 抽取的信息可能会产生违背事实的错误回答。
- 对于具备危害性的指令无法很好的鉴别，可能会产生危害性言论。
- 在一些涉及段落级长文本的场景下模型的抽取能力仍有待提高。
The SFT model, trained using the data and the base model, still faces the following issues:
- The information extracted may lead to factually incorrect answers.
- It struggles to effectively discern harmful instructions, potentially resulting in hazardous statements.
- The model's extraction capability needs improvement in scenarios involving paragraph-level texts.
开源协议/Open Source License
本项目中的代码和数据依照 Apache-2.0 协议开源，社区使用YAYI UIE模型或其衍生品请遵循Baichuan2的社区协议和商用协议。
The code and data in this project is open-sourced under the Apache-2.0 license. The use of YAYI-UIE model or its derivatives must adhere to Baichuan2's community and commercial Model License.
免责声明/Disclaimer
基于以上模型局限性，我们要求开发者仅将我们开源的代码、数据、模型及后续用此项目生成的衍生物用于研究目的，不得用于商业用途，以及其他会对社会带来危害的用途。请谨慎鉴别和使用雅意大模型生成的内容，请勿将生成的有害内容传播至互联网。若产生不良后果，由传播者自负。
本项目仅可应用于研究目的，项目开发者不承担任何因使用本项目（包含但不限于数据、模型、代码等）导致的危害或损失。详细请参考免责声明。
Given the limitations of the model outlined above,we require developers to use the code, data, models, and any derivatives generated from this project solely for research
purposes. They must not be used for commercial purposes or other applications that could harm society. Users should be careful in discerning and utilizing content generated
by the YAYI UIE, and avoid distributing harmful content on the internet. The spreader bears sole responsibility for any adverse consequences.
This project is intended only for research purposes. The project developers are not liable for any harm or loss resulting from the use of this project, including but not
limited to data, models, and code. For more details, please refer to the disclaimer.
引用/Citation
如果您在工作中使用了我们的模型，请引用我们的论文:
If you are using the resource for your work, please cite our paper.
优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。
你将获得什么
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完整仓库可能包含多个权重格式，因此需要的磁盘空间可能大于单一格式的模型大小。下载文件不等于完成模型部署。
使用前须知
运行模型需要的设备、工具与依赖，以原作者说明为准。
是否允许商用、修改和分发，请查看模型许可证。
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