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Legal_Clauses_Found
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ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ื‘ื™ืจื•ืฉืœื™ื ืจืข"ื 2225/01 ื‘ืคื ื™: ื›ื‘ื•ื“ ื”ืฉื•ืคื˜ืช ื˜' ืฉื˜ืจืกื‘ืจื’-ื›ื”ืŸ ื”ืžื‘ืงืฉืช: ืฉื™ืจื•ืชื™ ื‘ืจื™ืื•ืช ื›ืœืœื™ืช ื ื’ื“ ื”ืžืฉื™ื‘ ื” : ื—ื ื™ืชื”...
[ "ืชืงื ื•ืช ืกื“ืจ ื”ื“ื™ืŸ ื”ืื–ืจื—ื™, ืชืฉืž\"ื“1984" ]
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ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ื‘ืฉื‘ืชื• ื›ื‘ื™ืช ืžืฉืคื˜ ืœืขืจืขื•ืจื™ื ืคืœื™ืœื™ื™ื ืข"ืค 7470/00 ื‘ืคื ื™: ื›ื‘ื•ื“ ื”ืฉื•ืคื˜ ื™' ื˜ื™ืจืงืœ ื›ื‘ื•ื“ ื”ืฉื•ืคื˜ ื' ืจื™ื‘ืœื™ืŸ ื›ื‘ื•ื“ ื”ืฉื•ืคื˜ืช...
[ "ื›ืœืœ ืœืขื ื™ื™ื ื• ืฉืœ ื”ืžืขืจืขืจ, ื•ืขืœ ื›ืš ืขืžื“ ื‘ื™ืช ืžืฉืคื˜ ื–ื” ื‘ืคืกืง ื“ื™ื ื• ื‘ืข\"ืค 4935" ]
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86,291
ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ืข"ื 3906/99 ื‘ืคื ื™: ื›ื‘ื•ื“ ื”ืจืฉืžืช ื—' ืžืืง-ืงืœืžื ื•ื‘ื™ืฅ ื”ืžืขืจืขืจ : ื—ืืคื– ื—ื˜ื™ื‘ ื ื’ื“ ื”ืžืฉื™ื‘ : ...
[ "ืชืงื ื•ืช ืกื“ืจ ื”ื“ื™ืŸ ื”ืื–ืจื—ื™, ื”ืชืฉืž\"ื“1984" ]
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117,357
ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ืข"ื 3906/99 ื‘ืคื ื™: ื›ื‘ื•ื“ ื”ืจืฉืžืช ื—' ืžืืง-ืงืœืžื ื•ื‘ื™ืฅ ื”ืžืขืจืขืจ : ื—ืืคื– ื—ื˜ื™ื‘ ื ื’ื“ ื”ืžืฉื™ื‘ : ...
[ "ืชืงื ื•ืช ืกื“ืจ ื”ื“ื™ืŸ ื”ืื–ืจื—ื™ ื”ืชืฉืž\"ื“1984" ]
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119,035
ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ืข"ื 3906/99 ื‘ืคื ื™: ื›ื‘ื•ื“ ื”ืจืฉืžืช ื—' ืžืืง-ืงืœืžื ื•ื‘ื™ืฅ ื”ืžืขืจืขืจ : ื—ืืคื– ื—ื˜ื™ื‘ ื ื’ื“ ื”ืžืฉื™ื‘ : ...
[ "ืชืงื ื•ืช ืกื“ืจ ื”ื“ื™ืŸ ื”ืื–ืจื—ื™ ื”ืชืฉืž\"ื“1984" ]
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137,699
ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ืข"ื 4950/01 ื•ืขืจืขื•ืจ ืฉื›ื ื’ื“ ื‘ืคื ื™: ื›ื‘ื•ื“ ื”ืจืฉื ื‘ืขื– ืื•ืงื•ืŸ ื”ืžืขืจืขืจ : ืงื•ื’ืŸ ื•ื™ืืฆืกืœื‘ ื ื’ื“ ื”ืžืฉื™ื‘ื™ื : ...
[ "ืชืงื ื•ืช ืกื“ืจ ื”ื“ื™ืŸ ื”ืื–ืจื—ื™, ื”ืชืฉืž\"ื“-1984" ]
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ื‘ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ืจืข"ืค 9818/01 ืข"ืค 8295/02 ื‘ืคื ื™: ื›ื‘ื•ื“ ื”ื ืฉื™ื ื' ื‘ืจืง ื›ื‘ื•ื“ ื”ืฉื•ืคื˜ืช ื“' ื“ื•ืจื ืจ ื›ื‘ื•ื“ ื”ืฉื•ืคื˜ ื' ื' ืœื•ื™ ื”ืžืขืจืขืจ ื‘ืจืข"ืค 9818/01: ื”ืž...
[ "ื—ื•ืง ื‘ืชื™ ื”ืžืฉืคื˜, ื”ืชืฉืž\"ื“-1984" ]
36
230,394
" ื‘ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ื‘ืฉื‘ืชื• ื›ื‘ื™ืช ืžืฉืคื˜ ืœืข(...TRUNCATED)
["ื—ื•ืง ืื™ืกื•ืจ ืœืฉื•ืŸ ื”ืจืข, ื”ืชืฉื›\"ื”-1965","ื—ื•ืง ื”ื’ื ืช ื”ืคืจื˜ื™ื•ืช, ืชืฉืž\"ื-(...TRUNCATED)
41
103,052
" ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ื‘ืฉ\"ื 10273/01 ื‘ืคื ื™: (...TRUNCATED)
[ "ื—ื•ืง ื‘ืชื™ ื”ืžืฉืคื˜ (ื ื•ืกื— ืžืฉื•ืœื‘), ื”ืชืฉืž\"ื“-1984" ]
43
143,267
" ื‘ ื‘ื™ืช ื”ืžืฉืคื˜ ื”ืขืœื™ื•ืŸ ื‘ืฉ\"ื 10118/01 (...TRUNCATED)
["ื—ื•ืง ื‘ืชื™ ื”ืžืฉืคื˜ [ื ื•ืกื— ืžืฉื•ืœื‘], ื”ืชืฉืž\"ื“1984","ืชืงื ื•ืช ืกื“ืจ ื”ื“ื™ืŸ ื”ื(...TRUNCATED)
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Dataset Card for Legal_Clauses Dataset

Dataset Summary

This dataset is a subset of the LevMuchnik/SupremeCourtOfIsrael dataset. It has been processed using the shay681/HeBERT_finetuned_Legal_Clauses model, which extracted the legal clauses from the text column.

It can be loaded with the dataset package:

import datasets
data = datasets.load_dataset('shay681/Legal_Clauses')

Dataset Structure

The dataset is a json lines file with each line corresponding to a single document and containing document identification, text and metadata.

Data Fields

The file contains the following fields:

  • Id - document id
  • text - text of the document.
  • Legal_Clauses_Found - legal clauses extracted from the text column.

Data Splits

The entire dataset is qualified as 'train'.

About Me

Created by Shay Doner. This is my final project as part of intelligent systems M.Sc studies at Afeka College in Tel-Aviv. For more cooperation, please contact email: shay681@gmail.com

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