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nlpaueb/legal-bert-base-uncased nlpaueb/legal-bert-base-uncased
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Contributed by

nlpaueb AUEB NLP Group university
6 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-base-uncased") model = AutoModelForPreTraining.from_pretrained("nlpaueb/legal-bert-base-uncased")

LEGAL-BERT: The Muppets straight out of Law School

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-domains variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks. A light-weight model (33% the size of BERT-BASE) pre-trained from scratch on legal data with competitive perfomance is also available.




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://arxiv.org/abs/2010.02559)


Pre-training corpora

The pre-training corpora of LEGAL-BERT include:

  • 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.

  • 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk).

  • 19,867 cases from European Court of Justice (ECJ), also available from EURLEX.

  • 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng).

  • 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law).

  • 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml).

Pre-training details

  • We trained BERT using the official code provided in Google BERT's github repository (https://github.com/google-research/bert).
  • We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters).
  • 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.
  • We were able to use a single Google Cloud TPU v3-8 provided for free from TensorFlow Research Cloud (TFRC), while also utilizing GCP research credits. Huge thanks to both Google programs for supporting us!
  • Part of LEGAL-BERT is a light-weight model pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint.

    Models list

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

Load Pretrained Model

from transformers import AutoTokenizer, AutoModel

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

Use LEBAL-BERT variants as Language Models

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')

Evaluation on downstream tasks

Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2018, (https://arxiv.org/abs/2010.02559)

Author

Ilias Chalkidis on behalf of AUEB's Natural Language Processing Group

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