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README.md

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----
-annotations_creators:
-- machine-generated
-language_creators:
-- found
-language:
-- en
-license:
-- mit
-multilinguality:
-- monolingual
-size_categories:
-- unknown
-source_datasets:
-- extended|qa_srl
-task_categories:
-- text-retrieval
-task_ids: []
-pretty_name: LSOIE
-tags:
-- Open Information Extraction
----
-
-# Dataset Card for LSOIE
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-instances)
-  - [Data Splits](#data-instances)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-
-## Dataset Description
-
-- **Homepage:** https://github.com/Jacobsolawetz/large-scale-oie
-- **Repository:** https://github.com/Jacobsolawetz/large-scale-oie
-- **Paper:** https://arxiv.org/abs/2101.11177
-- **Leaderboard:** [Needs More Information]
-- **Point of Contact:** [Needs More Information]
-
-### Dataset Summary
-
-The Large Scale Open Information Extraction Dataset (LSOIE), is a dataset 20 times larger than the next largest human-annotated Open Information Extraction (OIE) dataset. LSOIE is a built upon the QA-SRL 2.0 dataset by transforming the list of Questions and answers for each predicate to a tuple representing a fact.
-
-### Supported Tasks and Leaderboards
-
-Open Information Extraction
-
-### Languages
-
-The text in this dataset is english.
-
-## Dataset Structure
-
-### Data Instances
-
-A datapoint comprises one fact together with the sentence it was extracted from. There can be multiple facts for each Sentence. Each fact is represented by a tuple $(a_0, p, a_1,\dots a_n)$ where $a_0$ is the head entity $p$ is the predicate and $a_1, \dots,a_n$ represent the tail.
-
-### Data Fields
-
-- word_ids : sequence of indices (int) representing tokens in a sentence,
-- words : a sequence of strings, the tokens in the sentence,
-- pred : the predicate of the fact,
-- pred_ids : ids of the tokens in the predicate,
-- head_pred_id : id of the head token in the predicate,
-- sent_id : sentence id,
-- run_id : ,
-- label : Sequence of tags (BIO) representing the fact, e.g. if the fact is given by $(a_0, p, a_1, \dots, a_n) $
-
-### Data Splits
-
-[Needs More Information]
-
-## Dataset Creation
-
-### Curation Rationale
-
-[Needs More Information]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[Needs More Information]
-
-#### Who are the source language producers?
-
-[Needs More Information]
-
-### Annotations
-
-#### Annotation process
-
-[Needs More Information]
-
-#### Who are the annotators?
-
-[Needs More Information]
-
-### Personal and Sensitive Information
-
-[Needs More Information]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[Needs More Information]
-
-### Discussion of Biases
-
-[Needs More Information]
-
-### Other Known Limitations
-
-[Needs More Information]
-
-## Additional Information
-
-### Dataset Curators
-
-[Needs More Information]
-
-### Licensing Information
-
-[Needs More Information]
-
-### Citation Information
-
-[Needs More Information]

dataset_infos.json

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-{"wiki": {"description": "\nThe Large Scale Open Information Extraction Dataset (LSOIE), is a dataset 20 \ntimes larger than the next largest human-annotated Open Information Extraction\n(OIE) dataset. LSOIE is a built upon the QA-SRL 2.0 dataset.\n", "citation": "@article{lsoie-2021,\n  title={{LSOIE}: A Large-Scale Dataset for Supervised Open Information Extraction},\n  author={{Solawetz}, Jacob and {Larson}, Stefan},\n  journal={arXiv preprint arXiv:2101.11177},\n  year={2019},\n  url=\"https://arxiv.org/pdf/2101.11177.pdf\"\n}\n", "homepage": "https://github.com/Jacobsolawetz/large-scale-oie/", "license": "", "features": {"word_ids": {"feature": {"dtype": "int16", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pred": {"dtype": "string", "id": null, "_type": "Value"}, "pred_ids": {"feature": {"dtype": "int16", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "head_pred_id": {"dtype": "int16", "id": null, "_type": "Value"}, "sent_id": {"dtype": "int16", "id": null, "_type": "Value"}, "run_id": {"dtype": "int16", "id": null, "_type": "Value"}, "label": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": {"input": "word_ids", "output": "label"}, "task_templates": null, "builder_name": "lsoie", "config_name": "wiki", "version": "0.0.0", "splits": {"train": {"name": "train", "num_bytes": 24938522, "num_examples": 46016, "dataset_name": "lsoie"}, "validation": {"name": "validation", "num_bytes": 2880854, "num_examples": 5269, "dataset_name": "lsoie"}, "test": {"name": "test", "num_bytes": 2840517, "num_examples": 5374, "dataset_name": "lsoie"}}, "download_checksums": {"https://github.com/Jacobsolawetz/large-scale-oie/raw/master/dataset_creation/lsoie_data/lsoie_data.zip": {"num_bytes": 19799926, "checksum": "0d189a3a8fef4b9f9efdad8faf0f53fc53805f9b2ad5354926e09c1449a00330"}}, "download_size": 19799926, "post_processing_size": null, "dataset_size": 30659893, "size_in_bytes": 50459819}}

lsoie.py

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-# -*- coding: utf-8 -*-
-"""LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction."""
-import os
-import datasets
-from datasets.info import SupervisedKeysData
-from zipfile import ZipFile
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@article{lsoie-2021,
-  title={{LSOIE}: A Large-Scale Dataset for Supervised Open Information Extraction},
-  author={{Solawetz}, Jacob and {Larson}, Stefan},
-  journal={arXiv preprint arXiv:2101.11177},
-  year={2019},
-  url="https://arxiv.org/pdf/2101.11177.pdf"
-}
-"""
-
-_DESCRIPTION = """
-The Large Scale Open Information Extraction Dataset (LSOIE), is a dataset 20 
-times larger than the next largest human-annotated Open Information Extraction
-(OIE) dataset. LSOIE is a built upon the QA-SRL 2.0 dataset.
-"""
-
-_URL = "https://github.com/Jacobsolawetz/large-scale-oie/"
-_URLS = {
-    "zip": _URL+"raw/master/dataset_creation/lsoie_data/lsoie_data.zip"
-}
-_ARCHIVE_FILES = [
-    "lsoie_science_train.conll",
-    "lsoie_science_dev.conll",
-    "lsoie_science_test.conll",
-    "lsoie_wiki_train.conll",
-    "lsoie_wiki_dev.conll",
-    "lsoie_wiki_test.conll",
-    ]
-
-
-class LsoieConfig(datasets.BuilderConfig):
-    """BuilderConfig for LSOIE."""
-
-    def __init__(self,subset="wiki", **kwargs):
-        """BuilderConfig for LSOIE.
-        Args:
-          subset: str - either "wiki" or "science"
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(LsoieConfig, self).__init__(**kwargs)
-        self.subset=subset
-
-
-class Lsoie(datasets.GeneratorBasedBuilder):
-    """LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction"""
-
-    BUILDER_CONFIGS = [
-        LsoieConfig(
-            name="wiki",
-            description="LSOIE dataset from wikipedia and wikinews",
-            subset="wiki",
-        ),
-        LsoieConfig(
-            name="sci",
-            description="LSOIE dataset build over scientific domain",
-            subset="science",
-        ),
-    ]
-    
-    DEFAULT_CONFIG_NAME = "wiki"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "word_ids": datasets.Sequence(datasets.Value("int16")),
-                    "words": datasets.Sequence(datasets.Value("string")),
-                    "pred": datasets.Value("string"),
-                    "pred_ids": datasets.Sequence(datasets.Value("int16")),
-                    "head_pred_id": datasets.Value("int16"),
-                    "sent_id": datasets.Value("int16"),
-                    "run_id": datasets.Value("int16"),
-                    "label": datasets.Sequence(datasets.Value("string")),
-                }
-            ),
-            supervised_keys=SupervisedKeysData(input="word_ids",output="label"),
-            homepage=_URL,
-            citation=_CITATION,
-            #there is no default task for open information extraction yet
-            #task_templates=[
-            #    OpenInformationExtraction(
-            #        question_column="question", context_column="context", answers_column="answers"
-            #    )
-            #],
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_archive = dl_manager.download(_URLS)['zip']          
-        #name_pre=os.path.join("lsoie_data","lsoie_")+self.config.subset+"_"
-        name_pre="lsoie_"+self.config.subset+"_"
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, 
-                                    gen_kwargs={
-                                        "archive_path": downloaded_archive,
-                                        "file_name": name_pre+"train.conll",
-                                        }),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, 
-                                    gen_kwargs={
-                                        "archive_path": downloaded_archive,
-                                        "file_name": name_pre+"dev.conll",
-                                        }),
-            datasets.SplitGenerator(name=datasets.Split.TEST, 
-                                    gen_kwargs={
-                                        "archive_path": downloaded_archive,
-                                        "file_name": name_pre+"test.conll",
-                                        }),
-        ]
-                        
-    def _generate_examples(self,archive_path,file_name):
-        """This functions returns the samples in a raw format"""
-        logger.info("generating examples from archive:{}".format(archive_path))
-        columns={'word_ids':int,
-                 'words':str, 
-                 'pred':str,
-                 'pred_ids':lambda x: [ num for num in x.strip('[]').split(',')],
-                 'head_pred_id': int,
-                 'sent_id':int,
-                 'run_id': int,
-                 'label':str}
-        list_columns=["word_ids","words","label"]
-        sep="\t"
-        key=0
-        sentence=dict()
-        for column in list_columns:
-            sentence[column]=[]
-        with ZipFile(archive_path) as zipfile:
-            with zipfile.open('lsoie_data/'+file_name,mode='r') as file:
-                for line in file:
-                    line=line.decode("utf-8").strip('\n').split(sep=sep)
-                    if line[0]=='':
-                        yield key, sentence
-                        key+=1
-                        for column in list_columns:
-                            sentence[column]=[]
-                        continue
-                    for column, val in zip(columns.keys(),line):
-                        val=columns[column](val)
-                        if column in list_columns:
-                            sentence[column].append(val)
-                        else:
-                            sentence[column]=val                        

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