| # Legal-HeBERT |
| Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal and legislative documents. The second version uses [HeBERT](https://github.com/avichaychriqui/HeBERT)'s architecture guidlines to train a BERT model from scratch. <br> |
| We continue collecting legal data, examining different architectural designs, and performing tagged datasets and legal tasks for evaluating and to development of a Hebrew legal tools. |
|
|
| ## Training Data |
| Our training datasets are: |
|
|
| | Name | Hebrew Description | Size (GB) | Documents | Sentences | Words | Notes | |
| |----------------------------------------------------------------------------------------------------------------------------------- |-------------------------------------------------------------------------- |----------- |----------- |------------ |------------- |----------------------------------------- | |
| | The Israeli Law Book | ספר החוקים הישראלי | 0.05 | 2338 | 293352 | 4851063 | | |
| | Judgments of the Supreme Court | מאגר פסקי הדין של בית המשפט העליון | 0.7 | 212348 | 5790138 | 79672415 | | |
| | custody courts | החלטות בתי הדין למשמורת | 2.46 | 169,708 | 8,555,893 | 213,050,492 | | |
| | Law memoranda, drafts of secondary legislation and drafts of support tests that have been distributed to the public for comment | תזכירי חוק, טיוטות חקיקת משנה וטיוטות מבחני תמיכה שהופצו להערות הציבור | 0.4 | 3,291 | 294,752 | 7,218,960 | | |
| | Supervisors of Land Registration judgments | מאגר פסקי דין של המפקחים על רישום המקרקעין | 0.02 | 559 | 67,639 | 1,785,446 | | |
| | Decisions of the Labor Court - Corona | מאגר החלטות בית הדין לעניין שירות התעסוקה – קורונה | 0.001 | 146 | 3505 | 60195 | | |
| | Decisions of the Israel Lands Council | החלטות מועצת מקרקעי ישראל | | 118 | 11283 | 162692 | aggregate file | |
| | Judgments of the Disciplinary Tribunal and the Israel Police Appeals Tribunal | פסקי דין של בית הדין למשמעת ובית הדין לערעורים של משטרת ישראל | 0.02 | 54 | 83724 | 1743419 | aggregate files | |
| | Disciplinary Appeals Committee in the Ministry of Health | ועדת ערר לדין משמעתי במשרד הבריאות | 0.004 | 252 | 21010 | 429807 | 465 files are scanned and didn't parser | |
| | Attorney General's Positions | מאגר התייצבויות היועץ המשפטי לממשלה | 0.008 | 281 | 32724 | 813877 | | |
| | Legal-Opinion of the Attorney General | מאגר חוות דעת היועץ המשפטי לממשלה | 0.002 | 44 | 7132 | 188053 | | |
| | | | | | | | | |
| | total | | 3.665 | 389,139 | 15,161,152 | 309,976,419 | | |
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|
| We thank <b>Yair Gardin</b> for the referring to the governance data, <b>Elhanan Schwarts</b> for collecting and parsing The Israeli law book, and <b>Jonathan Schler</b> for collecting the judgments of the supreme court. |
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|
|
|
| ## Training process |
| * Vocabulary size: 50,000 tokens |
| * 4 epochs (1M steps±) |
| * lr=5e-5 |
| * mlm_probability=0.15 |
| * batch size = 32 (for each gpu) |
| * NVIDIA GeForce RTX 2080 TI + NVIDIA GeForce RTX 3090 (1 week training) |
| |
| ### Additional training settings: |
| <b>Fine-tuned [HeBERT](https://github.com/avichaychriqui/HeBERT) model:</b> The first eight layers were freezed (like [Lee et al. (2019)](https://arxiv.org/abs/1911.03090) suggest)<br> |
| <b>Legal-HeBERT trained from scratch:</b> The training process is similar to [HeBERT](https://github.com/avichaychriqui/HeBERT) and inspired by [Chalkidis et al. (2020)](https://arxiv.org/abs/2010.02559) <br> |
| |
| ## How to use |
| The models can be found in huggingface hub and can be fine-tunned to any down-stream task: |
| ``` |
| # !pip install transformers==4.14.1 |
| from transformers import AutoTokenizer, AutoModel |
| |
| model_name = 'avichr/Legal-heBERT_ft' # for the fine-tuned HeBERT model |
| model_name = 'avichr/Legal-heBERT' # for legal HeBERT model trained from scratch |
|
|
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModel.from_pretrained(model_name) |
|
|
| from transformers import pipeline |
| fill_mask = pipeline( |
| "fill-mask", |
| model=model_name, |
| ) |
| fill_mask("הקורונה לקחה את [MASK] ולנו לא נשאר דבר.") |
| ``` |
| ## Stay tuned! |
| We are still working on our models and the datasets. We will edit this page as we progress. We are open for collaborations. |
| |
| ## If you used this model please cite us as : |
| Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai, Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts (June 27, 2022). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4147127 |
|
|
| ``` |
| @article{chriqui2021hebert, |
| title={Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts}, |
| author={Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai}, |
| journal={SSRN preprint:4147127}, |
| year={2022} |
| } |
| ``` |
|
|
| ## Contact us |
| [Avichay Chriqui](mailto:avichayc@mail.tau.ac.il), The Coller AI Lab <br> |
| [Inbal yahav](mailto:inbalyahav@tauex.tau.ac.il), The Coller AI Lab <br> |
| [Ittai Bar-Siman-Tov](mailto:Ittai.Bar-Siman-Tov@biu.ac.il), the BIU Innovation Lab for Law, Data-Science and Digital Ethics <br> |
|
|
| Thank you, תודה, شكرا <br> |
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