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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="sci",
+        ),
+    ]
+    
+    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