1 Star 26 Fork 5

科大讯飞 / CINO

加入 Gitee
与超过 1200万 开发者一起发现、参与优秀开源项目,私有仓库也完全免费 :)
免费加入
克隆/下载
贡献代码
同步代码
取消
提示: 由于 Git 不支持空文件夾,创建文件夹后会生成空的 .keep 文件
Loading...
README
Apache-2.0

中文说明 | English



GitHub

在自然语言处理领域中,预训练语言模型(Pre-trained Language Model, PLM)已成为重要的基础技术,在多语言的研究中,预训练模型的使用也愈加普遍。为了促进中国少数民族语言信息处理的研究与发展,哈工大讯飞联合实验室(HFL)发布少数民族语言预训练模型CINO (Chinese mINOrity PLM)。


中文LERT | 中英文PERT | 中文MacBERT | 中文ELECTRA | 中文XLNet | 中文BERT | 知识蒸馏工具TextBrewer | 模型裁剪工具TextPruner

查看更多哈工大讯飞联合实验室发布的资源:https://github.com/ymcui/HFL-Anthology

新闻

2022/10/29 我们提出了一种融合语言学信息的预训练模型LERT。查看:https://github.com/ymcui/LERT

2022/8/23 CINO被国际重要会议COLING 2022录用为长文。camera-ready结束后,我们将更新论文最终版并发布相应资源。

2022/02/21 更新CINO-small模型,6层transformer结构,参数量148M。

2022/01/25 更新CINO-v2模型与WCM-v2数据集,少数民族语言分类任务效果提升。

2021/12/17 哈工大讯飞联合实验室全新推出模型裁剪工具包TextPruner,欢迎试用。

2021/10/25 CINO-large模型、少数民族语言分类任务数据集Wiki-Chinese-Minority(WCM)数据集已开放下载使用。

内容导引

章节 描述
简介 介绍少数民族语言预训练模型与相关数据集
模型下载 模型下载地址与使用说明
快速加载 介绍了如何使用🤗Transformers快速加载模型
少数民族语言分类数据集 介绍少数民族语言分类数据集
实验结果 列举了模型在NLU任务上的效果
引用 技术报告与引用

简介

多语言预训练模型(Multilingual Pre-trained Language Model),如mBERT、XLM-R等,通过在预训练阶段增加语言数量、采用MLM自监督训练等方式,使预训练模型具备了多语言(multilingual)和跨语言(cross-lingual)理解的能力。然而,由于国内少数民族语言语料的稀缺以及国际上研究的忽视,现有的多语言模型无法很好地处理国内少数民族语言文字。

本项工作的主要贡献:

  • CINO (Chinese mINOrity PLM) 基于多语言预训练模型XLM-R,在多种国内少数民族语言语料上进行了二次预训练。该模型提供了藏语、蒙语(回鹘体)、维吾尔语、哈萨克语(阿拉伯体)、朝鲜语、壮语、粤语等少数民族语言与方言的理解能力。

  • 为了便于评价包括CINO在内的各个多语言预训练模型性能,我们构建了基于维基百科的少数民族语言分类任务数据集Wiki-Chinese-Minority(WCM)。具体见少数民族语言分类数据集

  • 通过实验证明,CINO在Wiki-Chinese-Minority(WCM)以及其他少数民族语言数据集:藏语新闻分类 Tibetan News Classification Corpus (TNCC) 、朝鲜语新闻分类 KLUE-TC (YNAT) 上获得了最好的效果。相关结果详见实验结果

该模型涵盖:

  • Chinese,中文(zh)
  • Tibetan,藏语(bo)
  • Mongolian (Uighur form),蒙语(mn)
  • Uyghur,维吾尔语(ug)
  • Kazakh (Arabic form),哈萨克语(kk)
  • Korean,朝鲜语(ko)
  • Zhuang,壮语
  • Cantonese,粤语(yue)



模型下载

直接下载

目前提供PyTorch版本的CINO-small、CINO-base和CINO-large模型的下载(推荐使用v2版本),后续将陆续更新其他规模与版本的模型。

  • CINO-large-v2:24-layer, 1024-hidden, 16-heads, vocabulary size 136K, 442M parameters
  • CINO-base-v2 12-layer, 768-hidden, 12-heads, vocabulary size 136K, 190M parameters
  • CINO-small-v2 6-layer, 768-hidden, 12-heads, vocabulary size 136K, 148M parameters
  • CINO-large:24-layer, 1024-hidden, 16-heads, vocabulary size 275K, 585M parameters

注意:

  • v1模型(CINO-large)支持XLM-R中的所有语言再加上少数民族语言;
  • v2模型(CINO-large-v2,CINO-base-v2和CINO-small-v2)的词表针对预训练数据做了裁剪,仅支持中文与少数民族语言。
模型简称 模型文件大小 Google下载 百度网盘下载
CINO-large-v2 1.6GB PyTorch模型 PyTorch模型(密码3fjt)
CINO-base-v2 705MB PyTorch模型 PyTorch模型(密码qnvc)
CINO-small-v2 564MB PyTorch模型 PyTorch模型(密码9mc8)
CINO-large 2.2GB PyTorch模型 PyTorch模型(密码wpyh)

通过🤗transformers下载

通过🤗transformers模型库可以下载TensorFlow (v2)和PyTorch版本模型。

下载方法:点击任意需要下载的模型 → 选择"Files and versions"选项卡 → 下载对应的模型文件。

模型简称 模型文件大小 transformers模型库地址
CINO-large-v2 1.6GB https://huggingface.co/hfl/cino-large-v2
CINO-base-v2 705MB https://huggingface.co/hfl/cino-base-v2
CINO-small-v2 564MB https://huggingface.co/hfl/cino-small-v2
CINO-large 2.2GB https://huggingface.co/hfl/cino-large

模型使用

PyTorch版本包含3个文件:

pytorch_model.bin        # 模型权重
config.json              # 模型参数
sentencepiece.bpe.model  # 词表

CINO的结构与XLM-R相同,可直接使用Transformers中的XLMRobertaModel模型进行加载:

from transformers import XLMRobertaTokenizer, XLMRobertaModel
tokenizer = XLMRobertaTokenizer.from_pretrained("PATH_TO_MODEL_DIR")
model = XLMRobertaModel.from_pretrained("PATH_TO_MODEL_DIR")

快速加载

依托于🤗Transformers,可轻松调用以上CINO模型。

from transformers import XLMRobertaTokenizer, XLMRobertaModel
tokenizer = XLMRobertaTokenizer.from_pretrained("MODEL_NAME")
model = XLMRobertaModel.from_pretrained("MODEL_NAME")

其中MODEL_NAME对应列表如下:

模型名 MODEL_NAME
CINO-large-v2 hfl/cino-large-v2
CINO-base-v2 hfl/cino-base-v2
CINO-small-v2 hfl/cino-small-v2
CINO-large hfl/cino-large

少数民族语言分类数据集

Wiki-Chinese-Minority(WCM)

我们基于少数民族语言维基百科语料及其分类体系标签,构建了分类任务数据集 Wiki-Chinese-Minority(WCM)。该数据集覆盖了蒙古语、藏语、维吾尔语、粤语、朝鲜语、哈萨克语,中文,包括艺术、地理、历史、自然、自然科学、人物、技术、教育、经济和健康十个类别。

各个语言上取weighted-F1为评测指标。计算所有语言的weighted-F1平均作为总体评价指标。

数据集名称 Google下载 百度网盘下载
Wiki-Chinese-Minority-v2(WCM-v2) Google Drive
Wiki-Chinese-Minority(WCM) Google Drive
注:语料数据无法通过百度网盘分享,请通过Google Drive下载。

WCM-v2版本调整了各类别与语言的样本数量,分布相对更均衡。WCM-v2版本数据分布:

类别 蒙古语 藏语 维吾尔语 粤语 朝鲜语 哈萨克语 中文-Train 中文-Dev 中文-Test
艺术 135 141 3 387 806 348 2657 331 335
地理 76 339 256 1550 1197 572 12854 1589 1644
历史 66 111 0 499 776 491 1771 227 248
自然 7 0 7 606 442 361 1105 134 110
自然科学 779 133 20 336 532 880 2314 317 287
人物 1402 111 0 1230 684 169 7706 953 924
技术 191 163 8 329 808 515 1184 134 152
教育 6 1 0 289 439 1392 936 130 118
经济 205 0 0 445 575 637 922 113 109
健康 106 111 6 272 299 893 551 67 73
总计 2973 1110 300 5943 6558 6258 32000 3995 4000

数据说明:

  • 包含两个文件夹:zh和minority
  • zh:中文的训练集、开发集和测试集
  • minority:所有语言(各少数民族语言与方言)的测试集

该数据集尚处于alpha阶段,之后的版本可能会有一定改动。
后续还将有其他数据集发布,敬请期待。

实验结果

我们在YNAT、TNCC和Wiki-Chinese-Minority三个数据集上比较了不同模型的效果。

对于同一任务上的各个预训练模型,使用统一的训练轮数、学习率等参数。

朝鲜语文本分类(YNAT)

#Train #Dev #Test #Classes Metric
45,678 9,107 9,107 7 macro-F1

实验参数:学习率为1e-5,batch_size为16。

实验结果:

模型 开发集
XLM-R-large[1] 87.3
XLM-R-large[2] 86.3
CINO-small-v2 84.1
CINO-base-v2 85.5
CINO-large-v2 87.2
CINO-large 87.4

[1] 论文中的结果。
[2] 复现结果,与CINO-large使用相同的学习率。

藏语文本分类(TNCC)

#Train[1] #Dev #Test #Classes Metric
7,363 920 920 12 macro-F1

实验参数:学习率为5e-6,batch_size为16。

实验结果:

模型 开发集 测试集
TextCNN 65.1 63.4
XLM-R-large 14.3 13.3
CINO-small-v2 72.1 66.7
CINO-base-v2 70.3 68.4
CINO-large-v2 72.9 71.0
CINO-large 71.3 68.6

注:原论文中未提供train/dev/test的划分方式。因此,我们重新对数据集按8:1:1做了划分。

Wiki-Chinese-Minority

在中文训练集上训练,在其他语言上做zero-shot测试。各语言的评测指标为weighted-F1。

实验参数:学习率为7e-6,batch_size为32。

WCM-v2实验结果:

模型 蒙古语 藏语 维吾尔语 粤语 朝鲜语 哈萨克语 中文 Average
XLM-R-base 41.2 25.7 84.5 66.1 43.1 23.0 88.3 53.1
XLM-R-large 53.8 24.5 89.4 67.3 45.4 30.0 88.3 57.0
CINO-small-v2 60.3 47.9 86.5 64.6 43.2 33.2 87.9 60.5
CINO-base-v2 62.1 52.7 87.8 68.1 45.6 38.3 89.0 63.4
CINO-large-v2 73.1 58.9 90.1 66.9 45.1 42.0 88.9 66.4

示例代码

参见examples目录,目前包括

引用

如果本目录中的内容对你的研究工作有所帮助,欢迎引用下述论文。

@inproceedings{yang-etal-2022-cino,
    title = "{CINO}: A {C}hinese Minority Pre-trained Language Model",
    author = "Yang, Ziqing  and
      Xu, Zihang  and
      Cui, Yiming  and
      Wang, Baoxin  and
      Lin, Min  and
      Wu, Dayong  and
      Chen, Zhigang",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.346",
    pages = "3937--3949"
}

关注我们

欢迎关注哈工大讯飞联合实验室官方微信公众号,了解最新的技术动态。

qrcode.jpg

问题反馈

如有问题,请在GitHub Issue中提交。

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "{}" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2020 RS_RDG_AI_Group / rc / zqyang5 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

简介

在自然语言处理领域中,预训练语言模型(Pre-trained Language Model, PLM)已成为重要的基础技术,在多语言的研究中,预训练模型的使用也愈加普遍。为了促进中国少数民族语言信息处理的研究与发展,哈工大讯飞联合实验室(HFL)发布少数民族语言预训练模型CINO (Chinese mINOrity PLM)。 展开 收起
Python
Apache-2.0
取消

发行版

暂无发行版

贡献者

全部

近期动态

加载更多
不能加载更多了
1
https://gitee.com/iflytek/cino.git
git@gitee.com:iflytek/cino.git
iflytek
cino
CINO
main

搜索帮助