tokens
sequence
ner_tags
sequence
[ "Pakistani", "scientists", "and", "engineers", "'", "working", "at", "IAEA", "became", "aware", "of", "advancing", "Indian", "nuclear", "program", "towards", "making", "the", "bombs", "." ]
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[ "In", "February", "2008", ",", "Church", "'s", "Chicken", "entered", "the", "UK", "market", "under", "the", "``", "Texas", "Chicken", "``", "name", ",", "claiming", "to", "have", "signed", "up", "50", "former", "Dixy", "Chicken", "franchisees", "." ]
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[ "This", "rivalry", "intensified", "in", "1919", "when", "Arsenal", "were", "unexpectedly", "promoted", "to", "the", "First", "Division", ",", "taking", "a", "place", "that", "Tottenham", "believed", "should", "be", "theirs", "." ]
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[ "Only", "one", "of", "four", "entered", "Corvettes", "-", "GT1", "C6R", "of", "Luc", "Alphand", "Aventures", "-", "eventually", "finished", "the", "race", ",", "taking", "second", "place", "in", "class", "." ]
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[ "After", "many", "appeals", ",", "the", "case", "went", "to", "the", "Supreme", "Court", ",", "which", "in", "1992", "ruled", "in", "``", "Two", "Pesos", ",", "Inc", ".", "v", ".", "Taco", "Cabana", ",", "Inc", ".", "``", "in", "favor", "of", "Taco", "Cabana", "." ]
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[ "Using", "``", "bite-and-hold", "''", "tactics", ",", "with", "objectives", "limited", "to", "what", "could", "be", "held", "against", "German", "counter-attacks", ",", "the", "British", "devastated", "the", "German", "defence", ",", "which", "prompted", "a", "crisis", "among", "the", "German", "commanders", "and", "caused", "a", "severe", "loss", "of", "morale", "in", "the", "4th", "Army", "." ]
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[ "Formerly", "known", "as", "the", "Communiplex", ",", "it", "is", "home", "to", "the", "WHL", "'s", "Prince", "Albert", "Raiders", "and", "the", "Prince", "Albert", "Mintos", "." ]
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[ "The", "score", "to", "``", "Spring", "Breakdown", "''", "was", "composed", "by", "Deborah", "Lurie", "who", "recorded", "her", "score", "with", "the", "Hollywood", "Studio", "Symphony", "conducted", "by", "Blake", "Neely", "and", "recorded", "by", "Greg", "Dennen", "at", "the", "Eastwood", "Scoring", "Stage", "at", "Warner", "Brothers", "." ]
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[ "The", "Swedish", "national", "men", "'s", "ice", "hockey", "team", ",", "affectionately", "known", "as", "``", "Tre", "Kronor", "``", "(", "English", ":", "Three", "Crowns", ";", "the", "national", "symbol", "of", "Sweden", ")", ",", "is", "regarded", "as", "one", "of", "the", "best", "in", "the", "world", "." ]
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[ "Audi", "in", "fourth", "and", "Jaguar", "in", "fifth", "were", "separated", "by", "one", "point", "." ]
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[ "Chiltern", "Air", "Support", "'s", "Luton", "helicopter", "(", "EC135T2", "CPDS", ")", "moved", "from", "London", "Luton", "Airport", "to", "RAF", "Henlow", ",", "near", "Hitchin", "and", "Shillington", "." ]
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[ "To", "enforce", "the", "injunction", ",", "Quixtar", "filed", "an", "action", "against", "30", "anonymous", "bloggers", "." ]
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[ "In", "1963", "Leicester", "City", "Council", "(", "LCC", ")", ",", "identifying", "a", "gap", "in", "cultural", "provision", "for", "live", "performances", ",", "built", "a", "262-seat", "theatre", "in", "Leicester", "." ]
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[ "In", "2010", ",", "Al", "Jazeera", "'s", "Teymoor", "Nabili", "suggested", "that", "the", "article", "``", "Cyrus", "Cylinder", "''", "had", "been", "edited", "for", "political", "purposes", "by", "``", "an", "apparent", "tussle", "of", "opinions", "in", "the", "shadowy", "world", "of", "hard", "drives", "and", "'independent", "'", "editors", "that", "comprise", "the", "Wikipedia", "industry", ".", "''" ]
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[ "The", "bascule", "bridge", "carries", "NJ", "Transit", "Rail", "Operations", "North", "Jersey", "Coast", "Line", "between", "the", "Bradley", "Beach", "and", "Belmar", "stations", "." ]
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[ "In", "1654", "the", "Congregazione", "dei", "Nobili", "put", "Picchiatti", "in", "charge", "of", "designing", "and", "building", "the", "Palazzo", "Monte", "dei", "Poveri", "Vergognosi", "." ]
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Dataset Card for "ner-orgs"

This dataset is a concatenation of subsets of Few-NERD, CoNLL 2003 and OntoNotes v5, but only the "B-ORG" and "I-ORG" labels.

Exactly half of the samples per split contain organisations, while the other half do not contain any.

It was generated using the following script:

import random
from datasets import load_dataset, concatenate_datasets, Features, Sequence, ClassLabel, Value, DatasetDict


FEATURES = Features(
    {
        "tokens": Sequence(feature=Value(dtype="string")),
        "ner_tags": Sequence(feature=ClassLabel(names=["O", "B-ORG", "I-ORG"])),
    }
)


def load_fewnerd():
    def mapper(sample):
        sample["ner_tags"] = [int(tag == 5) for tag in sample["ner_tags"]]
        sample["ner_tags"] = [
            2 if tag == 1 and idx > 0 and sample["ner_tags"][idx - 1] == 1 else tag
            for idx, tag in enumerate(sample["ner_tags"])
        ]
        return sample

    dataset = load_dataset("DFKI-SLT/few-nerd", "supervised")
    dataset = dataset.map(mapper, remove_columns=["id", "fine_ner_tags"])
    dataset = dataset.cast(FEATURES)
    return dataset


def load_conll():
    label_mapping = {3: 1, 4: 2}

    def mapper(sample):
        sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
        return sample

    dataset = load_dataset("conll2003")
    dataset = dataset.map(mapper, remove_columns=["id", "pos_tags", "chunk_tags"])
    dataset = dataset.cast(FEATURES)
    return dataset


def load_ontonotes():
    label_mapping = {11: 1, 12: 2}

    def mapper(sample):
        sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
        return sample

    dataset = load_dataset("tner/ontonotes5")
    dataset = dataset.rename_column("tags", "ner_tags")
    dataset = dataset.map(mapper)
    dataset = dataset.cast(FEATURES)
    return dataset


def has_org(sample):
    return bool(sum(sample["ner_tags"]))


def has_no_org(sample):
    return not has_org(sample)


def preprocess_raw_dataset(raw_dataset):
    # Set the number of sentences without an org equal to the number of sentences with an org
    dataset_org = raw_dataset.filter(has_org)
    dataset_no_org = raw_dataset.filter(has_no_org)
    dataset_no_org = dataset_no_org.select(random.sample(range(len(dataset_no_org)), k=len(dataset_org)))
    dataset = concatenate_datasets([dataset_org, dataset_no_org])
    return dataset


def main() -> None:
    fewnerd_dataset = load_fewnerd()
    conll_dataset = load_conll()
    ontonotes_dataset = load_ontonotes()

    raw_train_dataset = concatenate_datasets([fewnerd_dataset["train"], conll_dataset["train"], ontonotes_dataset["train"]])
    raw_eval_dataset = concatenate_datasets([fewnerd_dataset["validation"], conll_dataset["validation"], ontonotes_dataset["validation"]])
    raw_test_dataset = concatenate_datasets([fewnerd_dataset["test"], conll_dataset["test"], ontonotes_dataset["test"]])

    train_dataset = preprocess_raw_dataset(raw_train_dataset)
    eval_dataset = preprocess_raw_dataset(raw_eval_dataset)
    test_dataset = preprocess_raw_dataset(raw_test_dataset)

    dataset_dict = DatasetDict(
        {
            "train": train_dataset,
            "validation": eval_dataset,
            "test": test_dataset,
        }
    )
    dataset_dict.push_to_hub("ner-orgs", private=True)


if __name__ == "__main__":
    main()
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