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--- |
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language: en |
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pipeline_tag: fill-mask |
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license: cc-by-sa-4.0 |
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thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png |
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tags: |
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- legal |
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widget: |
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- text: "This [MASK] Agreement is between General Motors and John Murray." |
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--- |
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# LEGAL-BERT: The Muppets straight out of Law School |
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<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> |
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LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks.<br> |
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This is the sub-domain variant pre-trained on US contracts. |
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<br/><br/> |
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--- |
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I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) |
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--- |
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## Pre-training corpora |
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The pre-training corpora of LEGAL-BERT include: |
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* 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. |
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* 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). |
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* 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. |
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* 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). |
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* 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). |
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* 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). |
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## Pre-training details |
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* We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). |
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* We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). |
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* We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. |
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* We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! |
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## Models list |
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| Model name | Model Path | Training corpora | |
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| ------------------- | ------------------------------------ | ------------------- | |
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| CONTRACTS-BERT-BASE | `nlpaueb/bert-base-uncased-contracts` | US contracts | |
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| EURLEX-BERT-BASE | `nlpaueb/bert-base-uncased-eurlex` | EU legislation | |
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| ECHR-BERT-BASE | `nlpaueb/bert-base-uncased-echr` | ECHR cases | |
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| LEGAL-BERT-BASE * | `nlpaueb/legal-bert-base-uncased` | All | |
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| LEGAL-BERT-SMALL | `nlpaueb/legal-bert-small-uncased` | All | |
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\* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. |
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\*\* As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). |
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## Load Pretrained Model |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-uncased-contracts") |
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model = AutoModel.from_pretrained("nlpaueb/bert-base-uncased-contracts") |
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``` |
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## Use LEGAL-BERT variants as Language Models |
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| Corpus | Model | Masked token | Predictions | |
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| --------------------------------- | ---------------------------------- | ------------ | ------------ | |
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| | **BERT-BASE-UNCASED** | |
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| (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') |
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| (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') |
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| (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') |
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| | **CONTRACTS-BERT-BASE** | |
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| (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') |
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| (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') |
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| (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') |
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| | **EURLEX-BERT-BASE** | |
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| (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') |
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| (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') |
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| (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') |
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| | **ECHR-BERT-BASE** | |
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| (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') |
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| (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') |
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| (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') |
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| | **LEGAL-BERT-BASE** | |
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| (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') |
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| (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') |
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| (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') |
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| | **LEGAL-BERT-SMALL** | |
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| (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') |
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| (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') |
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| (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') |
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## Evaluation on downstream tasks |
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Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) |
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## Author - Publication |
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``` |
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@inproceedings{chalkidis-etal-2020-legal, |
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title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", |
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author = "Chalkidis, Ilias and |
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Fergadiotis, Manos and |
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Malakasiotis, Prodromos and |
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Aletras, Nikolaos and |
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Androutsopoulos, Ion", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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doi = "10.18653/v1/2020.findings-emnlp.261", |
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pages = "2898--2904" |
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} |
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``` |
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## About Us |
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[AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. |
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The group's current research interests include: |
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* question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, |
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* natural language generation from databases and ontologies, especially Semantic Web ontologies, |
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text classification, including filtering spam and abusive content, |
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* information extraction and opinion mining, including legal text analytics and sentiment analysis, |
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* natural language processing tools for Greek, for example parsers and named-entity recognizers, |
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machine learning in natural language processing, especially deep learning. |
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The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. |
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[Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) |
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| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) | |
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