LEGAL-BERT: 立法学院的木偶

LEGAL-BERT 是用于法律领域的BERT模型家族，旨在辅助法律自然语言处理研究、计算法律和法律技术应用。为了预训练LEGAL-BERT的不同变体，我们从公开可获得的资源中收集了12GB多样化的英文法律文本，包括立法、法院案例和合同等多个领域。子领域变体（CONTRACTS、EURLEX、ECHR）和（或）通用LEGAL-BERT在领域特定任务中表现优于直接使用原始的BERT模型。本版本是使用法律数据从头开始预训练的BERT-BASE的轻量级版本（仅为BERT-BASE的33%大小），在达到相当性能的同时，更加高效（速度提升约4倍），占用资源更少。

I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". 在Empirical Methods in Natural Language Processing (EMNLP 2020)（短论文）发表的论文中，将在2020年线上举办。（ https://aclanthology.org/2020.findings-emnlp.261 ）

预训练语料库

LEGAL-BERT的预训练语料库包括：

从欧盟法律库EURLEX（ http://eur-lex.europa.eu ）中公开获取的116,062份欧盟立法文档。
从英国立法门户网站（ http://www.legislation.gov.uk ）中公开获取的61,826份英国立法文档。
从EURLEX中获取的19,867个欧洲法院判决案例。
从欧洲人权法院（ECHR）的存储库HUDOC中获取的12,554个案例（ http://hudoc.echr.coe.int/eng ）。
从美国各地法院（托管在Case Law Access Project门户网站）获取的164,141个案例（ https://case.law ）。
从美国证券交易委员会（SEC）的数据库EDGAR获取的76,366份美国合同（ https://www.sec.gov/edgar.shtml ）。

预训练细节

我们使用Google BERT的GitHub仓库中提供的官方代码训练BERT（ https://github.com/google-research/bert ）。
我们发布了类似英文BERT-BASE模型的模型（12层，768隐藏层，12个注意头，110M参数）。
我们选择了相同的训练设置：100万个训练步骤，每个批次256个长度为512的序列，初始学习率为1e-4。
我们能够免费使用提供的单个Google Cloud TPU v3-8（ TensorFlow Research Cloud (TFRC) ），同时还利用了（ GCP research credits ）。非常感谢谷歌程序对我们的支持！

模型列表

Model name	Model Path	Training corpora
CONTRACTS-BERT-BASE	nlpaueb/bert-base-uncased-contracts	US contracts
EURLEX-BERT-BASE	nlpaueb/bert-base-uncased-eurlex	EU legislation
ECHR-BERT-BASE	nlpaueb/bert-base-uncased-echr	ECHR cases
LEGAL-BERT-BASE *	nlpaueb/legal-bert-base-uncased	All
LEGAL-BERT-SMALL	nlpaueb/legal-bert-small-uncased	All

* LEGAL-BERT-BASE是Chalkidis等人（2020）中称为LEGAL-BERT-SC的模型；它是在下面提到的法律语料库上使用新创建的词汇表通过句子片段分词器训练的模型。

** 对于对LEGAL-BERT-FP模型（依赖于原始BERT-BASE检查点）表示兴趣的许多人，它们已在Archive.org（ https://archive.org/details/legal_bert_fp ）上发布，因为这些模型是次要的，可能只对那些对Chalkidis等人（2020）的未解之谜感兴趣的人有意义。

加载预训练模型

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-small-uncased")
model = AutoModel.from_pretrained("nlpaueb/legal-bert-small-uncased")

使用LEGAL-BERT变体作为语言模型

Corpus	Model	Masked token	Predictions
BERT-BASE-UNCASED
(Contracts)	This [MASK] Agreement is between General Motors and John Murray .	employment	('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02')
(ECHR)	The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate	torture	('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03')
(EURLEX)	Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products .	bovine	('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05')
CONTRACTS-BERT-BASE
(Contracts)	This [MASK] Agreement is between General Motors and John Murray .	employment	('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
(ECHR)	The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate	torture	('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02')
(EURLEX)	Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products .	bovine	(('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04')
EURLEX-BERT-BASE
(Contracts)	This [MASK] Agreement is between General Motors and John Murray .	employment	('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05')
(ECHR)	The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate	torture	('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02')
(EURLEX)	Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products .	bovine	('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02')
ECHR-BERT-BASE
(Contracts)	This [MASK] Agreement is between General Motors and John Murray .	employment	('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04')
(ECHR)	The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate	torture	('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00')
(EURLEX)	Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products .	bovine	('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05')
LEGAL-BERT-BASE
(Contracts)	This [MASK] Agreement is between General Motors and John Murray .	employment	('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02')
(ECHR)	The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate	torture	('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00')
(EURLEX)	Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products .	bovine	('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01')
LEGAL-BERT-SMALL
(Contracts)	This [MASK] Agreement is between General Motors and John Murray .	employment	('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03')
(ECHR)	The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate	torture	('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02')
(EURLEX)	Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products .	bovine	('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05')

在下游任务上的评估

请参考文章“LEGAL-BERT: 立法学院的木偶”。 Chalkidis等人，2020年，（ https://aclanthology.org/2020.findings-emnlp.261 ）。

作者 - 出版物

@inproceedings{chalkidis-etal-2020-legal,
    title = "{LEGAL}-{BERT}: The Muppets straight out of Law School",
    author = "Chalkidis, Ilias  and
      Fergadiotis, Manos  and
      Malakasiotis, Prodromos  and
      Aletras, Nikolaos  and
      Androutsopoulos, Ion",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    doi = "10.18653/v1/2020.findings-emnlp.261",
    pages = "2898--2904"
}

关于我们

AUEB's Natural Language Processing Group 开发算法、模型和系统，使计算机能够处理和生成自然语言文本。

该小组目前的研究兴趣包括：

为数据库、本体论、文档集合和网络提供问答系统，特别是生物医学问答系统。
从数据库和本体生成自然语言，特别是语义Web本体。
信息提取和舆情分析，包括法律文本分析和情感分析。
希腊语自然语言处理工具，例如解析器和命名实体识别器。
自然语言处理中的机器学习，特别是深度学习。

该小组属于雅典经济和商业科学大学计算机科学系信息处理实验室。

Ilias Chalkidis 代表 AUEB's Natural Language Processing Group

| Github： @ilias.chalkidis | Twitter： @KiddoThe2B |

作者:

AUEB NLP Group

数据集大小:

464.61 MB