Create README.md

README.md

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+---
+language: en
+pipeline_tag: fill-mask
+license: cc-by-sa-4.0
+thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png
+tags:
+- legal
+widget:
+- text: "Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products."
+---
+
+# LEGAL-BERT: The Muppets straight out of Law School
+
+<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> 
+
+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>
+This is the sub-domain variant pre-trained on EU legislation.
+<br/><br/>
+
+---
+
+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)
+
+---
+
+## 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 the 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)](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!
+
+## 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                 |
+
+\* 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.
+
+\*\* 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).
+
+## Load Pretrained Model
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-uncased-eurlex")
+model = AutoModel.from_pretrained("nlpaueb/bert-base-uncased-eurlex")
+```
+
+## 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., 2020, (https://aclanthology.org/2020.findings-emnlp.261)
+
+## Author - Publication
+
+```
+@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"
+}
+```
+
+## About Us
+
+[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.
+
+The group's current research interests include:
+* question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering,
+* natural language generation from databases and ontologies, especially Semantic Web ontologies,
+text classification, including filtering spam and abusive content,
+* information extraction and opinion mining, including legal text analytics and sentiment analysis,
+* natural language processing tools for Greek, for example parsers and named-entity recognizers,
+machine learning in natural language processing, especially deep learning.
+
+The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business.
+
+[Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr)
+
+| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |