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README.md DELETED
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- # Dataset Card for PlainEnglishContractsSummarization
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-
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- ## Table of Contents
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- - [Table of Contents](#table-of-contents)
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- - [Dataset Description](#dataset-description)
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- - [Dataset Summary](#dataset-summary)
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- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- - [Languages](#languages)
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- - [Dataset Structure](#dataset-structure)
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- - [Data Instances](#data-instances)
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- - [Data Fields](#data-fields)
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- - [Data Splits](#data-splits)
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- - [Dataset Creation](#dataset-creation)
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- - [Curation Rationale](#curation-rationale)
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- - [Source Data](#source-data)
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- - [Annotations](#annotations)
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- - [Personal and Sensitive Information](#personal-and-sensitive-information)
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- - [Considerations for Using the Data](#considerations-for-using-the-data)
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- - [Social Impact of Dataset](#social-impact-of-dataset)
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- - [Discussion of Biases](#discussion-of-biases)
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- - [Other Known Limitations](#other-known-limitations)
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- - [Additional Information](#additional-information)
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- - [Dataset Curators](#dataset-curators)
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- - [Licensing Information](#licensing-information)
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- - [Citation Information](#citation-information)
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- - [Contributions](#contributions)
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-
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- ## Dataset Description
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-
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- - **Homepage:** [GitHub](https://github.com/lauramanor/legal_summarization)
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- - **Repository:**
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- - **Paper:** [ACL Anthology](https://aclanthology.org/W19-2201/)
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- - **Leaderboard:**
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- - **Point of Contact:**
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-
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- ### Dataset Summary
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-
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- [More Information Needed]
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-
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- ### Supported Tasks and Leaderboards
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-
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- [More Information Needed]
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-
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- ### Languages
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- [More Information Needed]
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-
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- ## Dataset Structure
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- ### Data Instances
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-
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- [More Information Needed]
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-
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- ### Data Fields
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-
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- [More Information Needed]
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-
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- ### Data Splits
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-
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- [More Information Needed]
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-
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- ## Dataset Creation
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- ### Curation Rationale
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- [More Information Needed]
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- ### Source Data
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- #### Initial Data Collection and Normalization
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- [More Information Needed]
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- #### Who are the source language producers?
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- [More Information Needed]
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- ### Annotations
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- #### Annotation process
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- [More Information Needed]
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- #### Who are the annotators?
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- [More Information Needed]
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- ### Personal and Sensitive Information
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- [More Information Needed]
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- ## Considerations for Using the Data
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- ### Social Impact of Dataset
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- [More Information Needed]
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- ### Discussion of Biases
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- [More Information Needed]
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- ### Other Known Limitations
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- [More Information Needed]
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-
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- ## Additional Information
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-
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- ### Dataset Curators
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- [More Information Needed]
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-
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- ### Licensing Information
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-
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- [More Information Needed]
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-
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- ### Citation Information
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- [More Information Needed]
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- ### Contributions
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- Thanks to [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
all_v1.json DELETED
The diff for this file is too large to render. See raw diff
 
train.jsonl.xz → joelito--plain_english_contracts_summarization/json-train.parquet RENAMED
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:6c2dfeb33626c09ea7e81398b88000c64e2ac8f24f84c30abbd111c0e51380ee
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- size 84060
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:c4ad5ce66c74e293a22ce1e901a31d234c4999422e9fe8efe8114c21a91dc327
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+ size 198702
prepare_data.py DELETED
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- import json
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- import os
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- import pandas as pd
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- import datasets
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-
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- from typing import Union
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-
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-
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- def save_and_compress(dataset: Union[datasets.Dataset, pd.DataFrame], name: str, idx=None):
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- if idx:
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- path = f"{name}_{idx}.jsonl"
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- else:
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- path = f"{name}.jsonl"
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-
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- print("Saving to", path)
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- dataset.to_json(path, force_ascii=False, orient='records', lines=True)
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-
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- print("Compressing...")
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- os.system(f'xz -zkf -T0 {path}') # -TO to use multithreading
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-
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-
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- entries = []
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- # read json file into a python dictionary
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- with open('all_v1.json') as f:
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- data = json.load(f)
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- for _, obj in data.items():
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- entries.append(obj)
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-
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- save_and_compress(pd.DataFrame(entries), "data/train")