tokens
sequence
tags
sequence
[ "People", "start", "their", "own", "businesses", "for", "many", "reasons", "." ]
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[ "But", "a", "chance", "to", "fill", "out", "sales", "-", "tax", "records", "is", "rarely", "one", "of", "them", "." ]
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[ "Red", "tape", "is", "the", "bugaboo", "of", "small", "business", "." ]
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[ "Ironically", ",", "the", "person", "who", "wants", "to", "run", "his", "or", "her", "own", "business", "is", "probably", "the", "active", ",", "results", "-", "oriented", "sort", "most", "likely", "to", "hate", "meeting", "the", "rules", "and", "record", "-", "keeping", "demands", "of", "federal", ",", "state", "and", "local", "regulators", "." ]
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[ "Yet", "every", "business", "owner", "has", "to", "face", "the", "mound", "of", "forms", "and", "regulations", "--", "and", "often", "is", "the", "only", "one", "available", "to", "tackle", "it", "." ]
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[ "There", "is", "hope", "of", "change", "." ]
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[ "Last", "week", ",", "Sen.", "Malcolm", "Wallop", "-LRB-", "R.", ",", "Wyo", ".", "-RRB-", "held", "hearings", "on", "a", "bill", "to", "strengthen", "an", "existing", "law", "designed", "to", "reduce", "regulatory", "hassles", "for", "small", "businesses", "." ]
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[ "Other", "lawmakers", "are", "busy", "trying", "to", "revive", "the", "recently", "lapsed", "Paperwork", "Reduction", "Act", ",", "which", "many", "feel", "benefited", "small", "enterprises", "." ]
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[ "Meanwhile", ",", "they", "tackle", "the", "mounds", "of", "paper", "--", "and", "fantasize", "about", "a", "dream", "world", "where", "bulk", "-", "mail", "postal", "regulations", "and", "government", "inspectors", "are", "banished", "." ]
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[ "To", "find", "out", "what", "red", "tape", "riles", "entrepreneurs", "most", ",", "the", "Journal", "asked", "a", "completely", "unscientific", ",", "random", "sample", "of", "business", "owners", "to", "fantasize", "about", "the", "forms", "and", "regulations", "they", "would", "most", "like", "to", "get", "lost", "in", "the", "mail", "." ]
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[ "They", "concede", "that", "much", "of", "the", "government", "meddling", "that", "torments", "them", "is", "essential", "to", "the", "public", "good", ",", "and", "even", "to", "their", "own", "businesses", "." ]
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[ "ENVIRONMENTAL", "REGULATIONS", ":" ]
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[ "Along", "the", "way", "there", "also", "are", "lots", "of", "romantic", "dalliances", "." ]
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[ "Wisely", "Ms.", "Bogart", "has", "kept", "Gorky", "'s", "time", "and", "place", "intact", "." ]
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[ "Ed", "Shea", "and", "Barbara", "Orson", "never", "find", "a", "real", "reason", "for", "their", "love", "affair", "as", "the", "foolish", ",", "idealistic", "young", "Vass", "and", "the", "tirelessly", "humanitarian", "doctor", "Maria", "Lvovna", "." ]
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[ "Better", "to", "look", "in", "the", "corners", "for", "performances", "that", "inspire", "or", "amuse", "." ]
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[ "Janice", "Duclos", ",", "in", "addition", "to", "possessing", "one", "of", "the", "evening", "'s", "more", "impressive", "vocal", "instruments", ",", "brings", "an", "unsuspected", "comedic", "touch", "to", "her", "role", "of", "Olga", ",", "everybody", "'s", "favorite", "mom", "." ]
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[ "Marni", "Rice", "plays", "the", "maid", "with", "so", "much", "edge", "as", "to", "steal", "her", "two", "scenes", "." ]
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[ "But", "it", "is", "the", "Trinity", "Rep", "newcomer", ",", "Jonathan", "Fried", "-LRB-", "Zamislov", ",", "the", "paralegal", "-RRB-", "who", "is", "the", "actor", "to", "watch", ",", "whether", "he", "is", "hamming", "it", "up", "while", "conducting", "the", "chamber", "musicians", "or", "seducing", "his", "neighbor", "'s", "wife", "-LRB-", "Becca", "Lish", "-RRB-", "by", "licking", "her", "bosom", "." ]
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[ "Ms.", "de", "Vries", "writes", "frequently", "about", "theater", "." ]
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[ "Yields", "on", "certificates", "of", "deposit", "at", "major", "banks", "were", "little", "changed", "in", "the", "latest", "week", "." ]
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[ "Both", "issues", "are", "among", "the", "most", "popular", "with", "individual", "investors", "." ]
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Dataset Card for "tner/ontonotes5"

Dataset Summary

Ontonotes5 NER dataset formatted in a part of TNER project.

  • Entity Types: CARDINAL, DATE, PERSON, NORP, GPE, LAW, PERCENT, ORDINAL, MONEY, WORK_OF_ART, FAC, TIME, QUANTITY, PRODUCT, LANGUAGE, ORG, LOC, EVENT

Dataset Structure

Data Instances

An example of train looks as follows.

{
    'tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0, 0, 0, 11, 12, 12, 12, 12, 0, 0, 7, 0, 0, 0, 0, 0],
    'tokens': ['``', 'It', "'s", 'very', 'costly', 'and', 'time', '-', 'consuming', ',', "''", 'says', 'Phil', 'Rosen', ',', 'a', 'partner', 'in', 'Fleet', '&', 'Leasing', 'Management', 'Inc.', ',', 'a', 'Boston', 'car', '-', 'leasing', 'company', '.']
}

Label ID

The label2id dictionary can be found at here.

{
    "O": 0,
    "B-CARDINAL": 1,
    "B-DATE": 2,
    "I-DATE": 3,
    "B-PERSON": 4,
    "I-PERSON": 5,
    "B-NORP": 6,
    "B-GPE": 7,
    "I-GPE": 8,
    "B-LAW": 9,
    "I-LAW": 10,
    "B-ORG": 11,
    "I-ORG": 12, 
    "B-PERCENT": 13,
    "I-PERCENT": 14, 
    "B-ORDINAL": 15, 
    "B-MONEY": 16, 
    "I-MONEY": 17, 
    "B-WORK_OF_ART": 18, 
    "I-WORK_OF_ART": 19, 
    "B-FAC": 20, 
    "B-TIME": 21, 
    "I-CARDINAL": 22, 
    "B-LOC": 23, 
    "B-QUANTITY": 24, 
    "I-QUANTITY": 25, 
    "I-NORP": 26, 
    "I-LOC": 27, 
    "B-PRODUCT": 28, 
    "I-TIME": 29, 
    "B-EVENT": 30,
    "I-EVENT": 31,
    "I-FAC": 32,
    "B-LANGUAGE": 33,
    "I-PRODUCT": 34,
    "I-ORDINAL": 35,
    "I-LANGUAGE": 36
}

Data Splits

name train validation test
ontonotes5 59924 8528 8262

Citation Information

@inproceedings{hovy-etal-2006-ontonotes,
    title = "{O}nto{N}otes: The 90{\%} Solution",
    author = "Hovy, Eduard  and
      Marcus, Mitchell  and
      Palmer, Martha  and
      Ramshaw, Lance  and
      Weischedel, Ralph",
    booktitle = "Proceedings of the Human Language Technology Conference of the {NAACL}, Companion Volume: Short Papers",
    month = jun,
    year = "2006",
    address = "New York City, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N06-2015",
    pages = "57--60",
}
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Models trained or fine-tuned on tner/ontonotes5