Google and Stanford University released a new pre-trained model called ELECTRA

Overview

中文说明 | English



GitHub

谷歌与斯坦福大学共同研发的最新预训练模型ELECTRA因其小巧的模型体积以及良好的模型性能受到了广泛关注。 为了进一步促进中文预训练模型技术的研究与发展,哈工大讯飞联合实验室基于官方ELECTRA训练代码以及大规模的中文数据训练出中文ELECTRA预训练模型供大家下载使用。 其中ELECTRA-small模型可与BERT-base甚至其他同等规模的模型相媲美,而参数量仅为BERT-base的1/10。

本项目基于谷歌&斯坦福大学官方的ELECTRA:https://github.com/google-research/electra

其他相关资源:

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

新闻

2021/7/21 由哈工大SCIR多位学者撰写的《自然语言处理:基于预训练模型的方法》已出版,欢迎大家选购,也可参与我们的赠书活动

2020/12/13 基于大规模法律文书数据,我们训练了面向司法领域的中文ELECTRA系列模型,查看模型下载司法任务效果

2020/10/22 ELECTRA-180g已发布,增加了CommonCrawl的高质量数据,查看模型下载

2020/9/15 我们的论文"Revisiting Pre-Trained Models for Chinese Natural Language Processing"Findings of EMNLP录用为长文。

2020/8/27 哈工大讯飞联合实验室在通用自然语言理解评测GLUE中荣登榜首,查看GLUE榜单新闻

点击这里查看历史新闻

2020/5/29 Chinese ELECTRA-large/small-ex已发布,请查看模型下载,目前只提供Google Drive下载地址,敬请谅解。

2020/4/7 PyTorch用户可通过 🤗 Transformers加载模型,查看快速加载

2020/3/31 本目录发布的模型已接入飞桨PaddleHub,查看快速加载

2020/3/25 Chinese ELECTRA-small/base已发布,请查看模型下载

内容导引

章节 描述
简介 介绍ELECTRA基本原理
模型下载 中文ELECTRA预训练模型下载
快速加载 介绍了如何使用 🤗 TransformersPaddleHub快速加载模型
基线系统效果 中文基线系统效果:阅读理解、文本分类等
使用方法 模型的详细使用方法
FAQ 常见问题答疑
引用 本目录的技术报告

简介

ELECTRA提出了一套新的预训练框架,其中包括两个部分:GeneratorDiscriminator

  • Generator: 一个小的MLM,在[MASK]的位置预测原来的词。Generator将用来把输入文本做部分词的替换。
  • Discriminator: 判断输入句子中的每个词是否被替换,即使用Replaced Token Detection (RTD)预训练任务,取代了BERT原始的Masked Language Model (MLM)。需要注意的是这里并没有使用Next Sentence Prediction (NSP)任务。

在预训练阶段结束之后,我们只使用Discriminator作为下游任务精调的基模型。

更详细的内容请查阅ELECTRA论文:ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

模型下载

本目录中包含以下模型,目前仅提供TensorFlow版本权重。

  • ELECTRA-large, Chinese: 24-layer, 1024-hidden, 16-heads, 324M parameters
  • ELECTRA-base, Chinese: 12-layer, 768-hidden, 12-heads, 102M parameters
  • ELECTRA-small-ex, Chinese: 24-layer, 256-hidden, 4-heads, 25M parameters
  • ELECTRA-small, Chinese: 12-layer, 256-hidden, 4-heads, 12M parameters

大语料版(新版,180G数据)

模型简称 Google下载 讯飞云下载 压缩包大小
ELECTRA-180g-large, Chinese TensorFlow TensorFlow(密码Yfcy) 1G
ELECTRA-180g-base, Chinese TensorFlow TensorFlow(密码Xcvm) 383M
ELECTRA-180g-small-ex, Chinese TensorFlow TensorFlow(密码GUdp) 92M
ELECTRA-180g-small, Chinese TensorFlow TensorFlow(密码qsHj) 46M

基础版(原版,20G数据)

模型简称 Google下载 讯飞云下载 压缩包大小
ELECTRA-large, Chinese TensorFlow (待补充) 1G
ELECTRA-base, Chinese TensorFlow TensorFlow(密码3VQu) 383M
ELECTRA-small-ex, Chinese TensorFlow (待补充) 92M
ELECTRA-small, Chinese TensorFlow TensorFlow(密码wm2E) 46M

司法领域版(new)

模型简称 Google下载 讯飞云下载 压缩包大小
legal-ELECTRA-large, Chinese TensorFlow TensorFlow(密码7f7b) 1G
legal-ELECTRA-base, Chinese TensorFlow TensorFlow(密码7f7b) 383M
legal-ELECTRA-small, Chinese TensorFlow TensorFlow(密码7f7b) 46M

PyTorch/TF2版本

如需PyTorch版本,请自行通过 🤗 Transformers提供的转换脚本convert_electra_original_tf_checkpoint_to_pytorch.py进行转换。如需配置文件可进入到本目录下的config文件夹中查找。

python transformers/src/transformers/convert_electra_original_tf_checkpoint_to_pytorch.py \
--tf_checkpoint_path ./path-to-large-model/ \
--config_file ./path-to-large-model/discriminator.json \
--pytorch_dump_path ./path-to-output/model.bin \
--discriminator_or_generator discriminator

或者通过huggingface官网直接下载PyTorch版权重:https://huggingface.co/hfl

方法:点击任意需要下载的model → 拉到最下方点击"List all files in model" → 在弹出的小框中下载bin和json文件。

使用须知

中国大陆境内建议使用讯飞云下载点,境外用户建议使用谷歌下载点。 以TensorFlow版ELECTRA-small, Chinese为例,下载完毕后对zip文件进行解压得到如下文件。

chinese_electra_small_L-12_H-256_A-4.zip
    |- electra_small.data-00000-of-00001    # 模型权重
    |- electra_small.meta                   # 模型meta信息
    |- electra_small.index                  # 模型index信息
    |- vocab.txt                            # 词表
    |- discriminator.json                   # 配置文件:discriminator(若没有可从本repo中的config目录获取)
    |- generator.json                       # 配置文件:generator(若没有可从本repo中的config目录获取)

训练细节

我们采用了大规模中文维基以及通用文本训练了ELECTRA模型,总token数达到5.4B,与RoBERTa-wwm-ext系列模型一致。词表方面沿用了谷歌原版BERT的WordPiece词表,包含21,128个token。其他细节和超参数如下(未提及的参数保持默认):

  • ELECTRA-large: 24层,隐层1024,16个注意力头,学习率1e-4,batch96,最大长度512,训练2M步
  • ELECTRA-base: 12层,隐层768,12个注意力头,学习率2e-4,batch256,最大长度512,训练1M步
  • ELECTRA-small-ex: 24层,隐层256,4个注意力头,学习率5e-4,batch384,最大长度512,训练2M步
  • ELECTRA-small: 12层,隐层256,4个注意力头,学习率5e-4,batch1024,最大长度512,训练1M步

快速加载

使用Huggingface-Transformers

Huggingface-Transformers 2.8.0版本已正式支持ELECTRA模型,可通过如下命令调用。

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModel.from_pretrained(MODEL_NAME) 

其中MODEL_NAME对应列表如下:

模型名 组件 MODEL_NAME
ELECTRA-180g-large, Chinese discriminator hfl/chinese-electra-180g-large-discriminator
ELECTRA-180g-large, Chinese generator hfl/chinese-electra-180g-large-generator
ELECTRA-180g-base, Chinese discriminator hfl/chinese-electra-180g-base-discriminator
ELECTRA-180g-base, Chinese generator hfl/chinese-electra-180g-base-generator
ELECTRA-180g-small-ex, Chinese discriminator hfl/chinese-electra-180g-small-ex-discriminator
ELECTRA-180g-small-ex, Chinese generator hfl/chinese-electra-180g-small-ex-generator
ELECTRA-180g-small, Chinese discriminator hfl/chinese-electra-180g-small-discriminator
ELECTRA-180g-small, Chinese generator hfl/chinese-electra-180g-small-generator
ELECTRA-large, Chinese discriminator hfl/chinese-electra-large-discriminator
ELECTRA-large, Chinese generator hfl/chinese-electra-large-generator
ELECTRA-base, Chinese discriminator hfl/chinese-electra-base-discriminator
ELECTRA-base, Chinese generator hfl/chinese-electra-base-generator
ELECTRA-small-ex, Chinese discriminator hfl/chinese-electra-small-ex-discriminator
ELECTRA-small-ex, Chinese generator hfl/chinese-electra-small-ex-generator
ELECTRA-small, Chinese discriminator hfl/chinese-electra-small-discriminator
ELECTRA-small, Chinese generator hfl/chinese-electra-small-generator

司法领域版本:

模型名 组件 MODEL_NAME
legal-ELECTRA-large, Chinese discriminator hfl/chinese-legal-electra-large-discriminator
legal-ELECTRA-large, Chinese generator hfl/chinese-legal-electra-large-generator
legal-ELECTRA-base, Chinese discriminator hfl/chinese-legal-electra-base-discriminator
legal-ELECTRA-base, Chinese generator hfl/chinese-legal-electra-base-generator
legal-ELECTRA-small, Chinese discriminator hfl/chinese-legal-electra-small-discriminator
legal-ELECTRA-small, Chinese generator hfl/chinese-legal-electra-small-generator

使用PaddleHub

依托PaddleHub,我们只需一行代码即可完成模型下载安装,十余行代码即可完成文本分类、序列标注、阅读理解等任务。

import paddlehub as hub
module = hub.Module(name=MODULE_NAME)

其中MODULE_NAME对应列表如下:

模型名 MODULE_NAME
ELECTRA-base, Chinese chinese-electra-base
ELECTRA-small, Chinese chinese-electra-small

基线系统效果

我们将ELECTRA-small/baseBERT-baseBERT-wwmBERT-wwm-extRoBERTa-wwm-extRBT3进行了效果对比,包括以下六个任务:

对于ELECTRA-small/base模型,我们使用原论文默认的3e-41e-4的学习率。 需要注意的是,我们没有针对任何任务进行参数精调,所以通过调整学习率等超参数可能获得进一步性能提升。 为了保证结果的可靠性,对于同一模型,我们使用不同随机种子训练10遍,汇报模型性能的最大值和平均值(括号内为平均值)。

简体中文阅读理解:CMRC 2018

CMRC 2018数据集是哈工大讯飞联合实验室发布的中文机器阅读理解数据。 根据给定问题,系统需要从篇章中抽取出片段作为答案,形式与SQuAD相同。 评价指标为:EM / F1

模型 开发集 测试集 挑战集 参数量
BERT-base 65.5 (64.4) / 84.5 (84.0) 70.0 (68.7) / 87.0 (86.3) 18.6 (17.0) / 43.3 (41.3) 102M
BERT-wwm 66.3 (65.0) / 85.6 (84.7) 70.5 (69.1) / 87.4 (86.7) 21.0 (19.3) / 47.0 (43.9) 102M
BERT-wwm-ext 67.1 (65.6) / 85.7 (85.0) 71.4 (70.0) / 87.7 (87.0) 24.0 (20.0) / 47.3 (44.6) 102M
RoBERTa-wwm-ext 67.4 (66.5) / 87.2 (86.5) 72.6 (71.4) / 89.4 (88.8) 26.2 (24.6) / 51.0 (49.1) 102M
RBT3 57.0 / 79.0 62.2 / 81.8 14.7 / 36.2 38M
ELECTRA-small 63.4 (62.9) / 80.8 (80.2) 67.8 (67.4) / 83.4 (83.0) 16.3 (15.4) / 37.2 (35.8) 12M
ELECTRA-180g-small 63.8 / 82.7 68.5 / 85.2 15.1 / 35.8 12M
ELECTRA-small-ex 66.4 / 82.2 71.3 / 85.3 18.1 / 38.3 25M
ELECTRA-180g-small-ex 68.1 / 85.1 71.8 / 87.2 20.6 / 41.7 25M
ELECTRA-base 68.4 (68.0) / 84.8 (84.6) 73.1 (72.7) / 87.1 (86.9) 22.6 (21.7) / 45.0 (43.8) 102M
ELECTRA-180g-base 69.3 / 87.0 73.1 / 88.6 24.0 / 48.6 102M
ELECTRA-large 69.1 / 85.2 73.9 / 87.1 23.0 / 44.2 324M
ELECTRA-180g-large 68.5 / 86.2 73.5 / 88.5 21.8 / 42.9 324M

繁体中文阅读理解:DRCD

DRCD数据集由中国台湾台达研究院发布,其形式与SQuAD相同,是基于繁体中文的抽取式阅读理解数据集。 评价指标为:EM / F1

模型 开发集 测试集 参数量
BERT-base 83.1 (82.7) / 89.9 (89.6) 82.2 (81.6) / 89.2 (88.8) 102M
BERT-wwm 84.3 (83.4) / 90.5 (90.2) 82.8 (81.8) / 89.7 (89.0) 102M
BERT-wwm-ext 85.0 (84.5) / 91.2 (90.9) 83.6 (83.0) / 90.4 (89.9) 102M
RoBERTa-wwm-ext 86.6 (85.9) / 92.5 (92.2) 85.6 (85.2) / 92.0 (91.7) 102M
RBT3 76.3 / 84.9 75.0 / 83.9 38M
ELECTRA-small 79.8 (79.4) / 86.7 (86.4) 79.0 (78.5) / 85.8 (85.6) 12M
ELECTRA-180g-small 83.5 / 89.2 82.9 / 88.7 12M
ELECTRA-small-ex 84.0 / 89.5 83.3 / 89.1 25M
ELECTRA-180g-small-ex 87.3 / 92.3 86.5 / 91.3 25M
ELECTRA-base 87.5 (87.0) / 92.5 (92.3) 86.9 (86.6) / 91.8 (91.7) 102M
ELECTRA-180g-base 89.6 / 94.2 88.9 / 93.7 102M
ELECTRA-large 88.8 / 93.3 88.8 / 93.6 324M
ELECTRA-180g-large 90.1 / 94.8 90.5 / 94.7 324M

自然语言推断:XNLI

在自然语言推断任务中,我们采用了XNLI数据,需要将文本分成三个类别:entailmentneutralcontradictory。 评价指标为:Accuracy

模型 开发集 测试集 参数量
BERT-base 77.8 (77.4) 77.8 (77.5) 102M
BERT-wwm 79.0 (78.4) 78.2 (78.0) 102M
BERT-wwm-ext 79.4 (78.6) 78.7 (78.3) 102M
RoBERTa-wwm-ext 80.0 (79.2) 78.8 (78.3) 102M
RBT3 72.2 72.3 38M
ELECTRA-small 73.3 (72.5) 73.1 (72.6) 12M
ELECTRA-180g-small 74.6 74.6 12M
ELECTRA-small-ex 75.4 75.8 25M
ELECTRA-180g-small-ex 76.5 76.6 25M
ELECTRA-base 77.9 (77.0) 78.4 (77.8) 102M
ELECTRA-180g-base 79.6 79.5 102M
ELECTRA-large 81.5 81.0 324M
ELECTRA-180g-large 81.2 80.4 324M

情感分析:ChnSentiCorp

在情感分析任务中,二分类的情感分类数据集ChnSentiCorp。 评价指标为:Accuracy

模型 开发集 测试集 参数量
BERT-base 94.7 (94.3) 95.0 (94.7) 102M
BERT-wwm 95.1 (94.5) 95.4 (95.0) 102M
BERT-wwm-ext 95.4 (94.6) 95.3 (94.7) 102M
RoBERTa-wwm-ext 95.0 (94.6) 95.6 (94.8) 102M
RBT3 92.8 92.8 38M
ELECTRA-small 92.8 (92.5) 94.3 (93.5) 12M
ELECTRA-180g-small 94.1 93.6 12M
ELECTRA-small-ex 92.6 93.6 25M
ELECTRA-180g-small-ex 92.8 93.4 25M
ELECTRA-base 93.8 (93.0) 94.5 (93.5) 102M
ELECTRA-180g-base 94.3 94.8 102M
ELECTRA-large 95.2 95.3 324M
ELECTRA-180g-large 94.8 95.2 324M

句对分类:LCQMC

以下两个数据集均需要将一个句对进行分类,判断两个句子的语义是否相同(二分类任务)。

LCQMC由哈工大深圳研究生院智能计算研究中心发布。 评价指标为:Accuracy

模型 开发集 测试集 参数量
BERT 89.4 (88.4) 86.9 (86.4) 102M
BERT-wwm 89.4 (89.2) 87.0 (86.8) 102M
BERT-wwm-ext 89.6 (89.2) 87.1 (86.6) 102M
RoBERTa-wwm-ext 89.0 (88.7) 86.4 (86.1) 102M
RBT3 85.3 85.1 38M
ELECTRA-small 86.7 (86.3) 85.9 (85.6) 12M
ELECTRA-180g-small 86.6 85.8 12M
ELECTRA-small-ex 87.5 86.0 25M
ELECTRA-180g-small-ex 87.6 86.3 25M
ELECTRA-base 90.2 (89.8) 87.6 (87.3) 102M
ELECTRA-180g-base 90.2 87.1 102M
ELECTRA-large 90.7 87.3 324M
ELECTRA-180g-large 90.3 87.3 324M

句对分类:BQ Corpus

BQ Corpus由哈工大深圳研究生院智能计算研究中心发布,是面向银行领域的数据集。 评价指标为:Accuracy

模型 开发集 测试集 参数量
BERT 86.0 (85.5) 84.8 (84.6) 102M
BERT-wwm 86.1 (85.6) 85.2 (84.9) 102M
BERT-wwm-ext 86.4 (85.5) 85.3 (84.8) 102M
RoBERTa-wwm-ext 86.0 (85.4) 85.0 (84.6) 102M
RBT3 84.1 83.3 38M
ELECTRA-small 83.5 (83.0) 82.0 (81.7) 12M
ELECTRA-180g-small 83.3 82.1 12M
ELECTRA-small-ex 84.0 82.6 25M
ELECTRA-180g-small-ex 84.6 83.4 25M
ELECTRA-base 84.8 (84.7) 84.5 (84.0) 102M
ELECTRA-180g-base 85.8 84.5 102M
ELECTRA-large 86.7 85.1 324M
ELECTRA-180g-large 86.4 85.4 324M

司法任务效果

我们使用CAIL 2018司法评测的罪名预测数据对司法ELECTRA进行了测试。small/base/large学习率分别为:5e-4/3e-4/1e-4。 评价指标为:Accuracy

模型 开发集 测试集 参数量
ELECTRA-small 78.84 76.35 12M
legal-ELECTRA-small 79.60 77.03 12M
ELECTRA-base 80.94 78.41 102M
legal-ELECTRA-base 81.71 79.17 102M
ELECTRA-large 81.53 78.97 324M
legal-ELECTRA-large 82.60 79.89 324M

使用方法

用户可以基于已发布的上述中文ELECTRA预训练模型进行下游任务精调。 在这里我们只介绍最基本的用法,更详细的用法请参考ELECTRA官方介绍

本例中,我们使用ELECTRA-small模型在CMRC 2018任务上进行精调,相关步骤如下。假设,

  • data-dir:工作根目录,可按实际情况设置。
  • model-name:模型名称,本例中为electra-small
  • task-name:任务名称,本例中为cmrc2018。本目录中的代码已适配了以上六个中文任务,task-name分别为cmrc2018drcdxnlichnsenticorplcqmcbqcorpus

第一步:下载预训练模型并解压

模型下载章节中,下载ELECTRA-small模型,并解压至${data-dir}/models/${model-name}。 该目录下应包含electra_model.*vocab.txtcheckpoint,共计5个文件。

第二步:准备任务数据

下载CMRC 2018训练集和开发集,并重命名为train.jsondev.json。 将两个文件放到${data-dir}/finetuning_data/${task-name}

第三步:运行训练命令

python run_finetuning.py \
    --data-dir ${data-dir} \
    --model-name ${model-name} \
    --hparams params_cmrc2018.json

其中data-dirmodel-name在上面已经介绍。hparams是一个JSON词典,在本例中的params_cmrc2018.json包含了精调相关超参数,例如:

{
    "task_names": ["cmrc2018"],
    "max_seq_length": 512,
    "vocab_size": 21128,
    "model_size": "small",
    "do_train": true,
    "do_eval": true,
    "write_test_outputs": true,
    "num_train_epochs": 2,
    "learning_rate": 3e-4,
    "train_batch_size": 32,
    "eval_batch_size": 32,
}

在上述JSON文件中,我们只列举了最重要的一些参数,完整参数列表请查阅configure_finetuning.py

运行完毕后,

  1. 对于阅读理解任务,生成的预测JSON数据cmrc2018_dev_preds.json保存在${data-dir}/results/${task-name}_qa/。可以调用外部评测脚本来得到最终评测结果,例如:python cmrc2018_drcd_evaluate.py dev.json cmrc2018_dev_preds.json
  2. 对于分类任务,相关accuracy信息会直接打印在屏幕,例如:xnli: accuracy: 72.5 - loss: 0.67

FAQ

Q: 在下游任务精调的时候ELECTRA模型的学习率怎么设置?
A: 我们建议使用原论文使用的学习率作为初始基线(small是3e-4,base是1e-4)然后适当增减学习率进行调试。 需要注意的是,相比BERT、RoBERTa一类的模型来说ELECTRA的学习率要相对大一些。

Q: 有没有PyTorch版权重?
A: 有,模型下载

Q: 预训练用的数据能共享一下吗?
A: 很遗憾,不可以。

Q: 未来计划?
A: 敬请关注。

引用

如果本目录中的内容对你的研究工作有所帮助,欢迎在论文中引用下述技术报告。

@inproceedings{cui-etal-2020-revisiting,
    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
    author = "Cui, Yiming  and
      Che, Wanxiang  and
      Liu, Ting  and
      Qin, Bing  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
    pages = "657--668",
}

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