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Datasets:
HYUKI5
/
DFKI-SLT__few-nerd

Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K - 1M
Tags:
laya
laya-finetune
mirror
domain-general
use-conditional
genre-encyclopedia
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
Dataset Viewer
Auto-converted to Parquet Duplicate
id
stringlengths
1
6
tokens
listlengths
2
267
ner_tags
listlengths
2
267
fine_ner_tags
listlengths
2
267
0
[ "When", "reconstruction", "of", "the", "building", "was", "complete", ",", "the", "rear", "half", "of", "the", "building", "was", "named", "Budig", "Hall", ",", "for", "then", "KU", "Chancellor", "Gene", "Budig", "." ]
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1
[ "The", "Institute", "of", "International", "Finance", "meetings", "are", "being", "held", "at", "Shangri-La", "Hotel", "Singapore", "for", "a", "period", "of", "two", "days", "and", "an", "opening", "dinner", "will", "be", "organised", "for", "the", "IIF", "...
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2
[ "Harbor", "Police", "is", "the", "premier", "police", "presence", "in", "the", "San", "Diego", "Bay", ",", "the", "San", "Diego", "International", "Airport", "and", "on", "all", "Port", "Tidelands", "." ]
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3
[ "Harbor", "Police", "jurisdiction", "extends", "through", "the", "Port", "'s", "five", "member", "cities", ":", "Chula", "Vista", ",", "Coronado", ",", "Imperial", "Beach", ",", "National", "City", "and", "San", "Diego", "." ]
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4
[ "The", "Beinecke", "Rare", "Book", "and", "Manuscript", "Library", "at", "Yale", "University", "has", "an", "archive", "of", "his", "collected", "papers", "." ]
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5
[ "The", "main", "Catholic", "Church", "of", "the", "town", "is", "located", "in", "a", "park", "area", "next", "to", "the", "Town", "Hall", "(", "Alcaldia", ")", "and", "next", "to", "the", "Cine", "Centenario", "(", "a", "cinema", ")", "which", "are", ...
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6
[ "Recently", ",", "Usulután", "has", "modernized", "considerably", ",", "having", "more", "influence", "from", "brand", "names", "such", "as", "Pollo", "Campero", "(", "a", "chicken", "restaurant", ")", ",", "Biggest", "(", "a", "hamburger", "place", "similar",...
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7
[ "The", "Kansas", "City", "Athletics", "played", "at", "McKechnie", "Field", "from", "1963–1968", "." ]
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8
[ "Ironically", ",", "the", "team", "left", "Connie", "Mack", "Field", "for", "Bradenton", ",", "after", "the", "Milwaukee", "Braves", "arrived", "in", "West", "Palm", "Beach", "." ]
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9
[ "The", "team", "was", "not", "open", "to", "sharing", "their", "spring", "training", "facilities", "or", "stadium", "with", "the", "Braves", "." ]
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10
[ "The", "City", "of", "Bradenton", "talked", "A", "'s", "owner", "Charlie", "Finley", "into", "staying", "at", "McKechnie", "until", "." ]
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11
[ "By", "that", "time", ",", "the", "A", "'s", "relocated", "from", "Kansas", "City", "to", "Oakland", "." ]
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12
[ "Finley", "believed", "that", "his", "west", "coast", "team", "should", "train", "on", "the", "west", "coast", ",", "in", "Mesa", ",", "Arizona", "." ]
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13
[ "During", "the", "Athletics", "tenure", ",", "Finley", "routinely", "tied", "his", "Athletics", "team", "mascot", ",", "a", "donkey", "named", "``", "Charlie", "O", "''", ",", "to", "a", "tree", "inside", "the", "stadium", "during", "games", ".", "''" ]
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14
[ "Fred", "Sherman", ",", "a", "resident", "of", "King", "of", "Prussia", ",", "Pennsylvania", ",", "died", "on", "September", "12", ",", "2009", ",", "at", "the", "Hospital", "of", "the", "University", "of", "Pennsylvania", "in", "Philadelphia", "at", "the"...
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15
[ "Sherman", "had", "suffered", "from", "several", "health", "problems", ",", "including", "kidney", "ailments", ",", "and", "injuries", "from", "a", "car", "accident", "several", "weeks", "before", "his", "death", "may", "have", "contributed", "to", "his", "dec...
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16
[ "He", "suffered", "the", "car", "accident", "shortly", "after", "returning", "from", "a", "trip", "to", "Europe", "in", "July", "2009", ",", "and", "had", "been", "hospitalized", "on", "several", "occasions", "since", "the", "accident", "." ]
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17
[ "Gus", "Greenbaum", "(", "February", "26", ",", "1893", "–", "December", "3", ",", "1958", ")", "was", "an", "American", "gangster", "in", "the", "casino", "industry", ",", "best", "known", "for", "taking", "over", "management", "of", "the", "Flamingo", ...
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18
[ "Nannu", "'s", "younger", "brother", "Shamsul", "Haq", "Monju", "played", "as", "a", "right-back", "for", "the", "Mohammedan", "Sporting", "Club", "." ]
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19
[ "The", "men", "'s", "200", "metre", "freestyle", "event", "in", "swimming", "at", "the", "2013", "World", "Aquatics", "Championships", "took", "place", "on", "29–30", "July", "at", "the", "Palau", "Sant", "Jordi", "in", "Barcelona", ",", "Spain", "." ]
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20
[ "The", "Basilica", "of", "Our", "Lady", "of", "Copacabana", "is", "a", "16th-century", "Spanish", "colonial", "shrine", "that", "houses", "the", "image", "of", "the", "Virgen", "de", "Copacabana", "." ]
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21
[ "It", "is", "located", "in", "the", "town", "of", "Copacabana", ",", "Bolivia", "on", "the", "shores", "of", "Lake", "Titicaca", "near", "the", "Altiplano", "region", "." ]
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22
[ "Our", "Lady", "of", "Copacabana", "is", "the", "patron", "saint", "of", "Bolivia", "." ]
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23
[ "The", "Double", "Down", "is", "a", "sandwich", "offered", "by", "Kentucky", "Fried", "Chicken", "(", "KFC", ")", "restaurants", "." ]
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24
[ "Wakley", "did", "all", "that", "he", "could", ",", "as", "editor", "of", "``", "The", "Lancet", "``", ",", "and", "as", "an", "individual", ",", "to", "oppose", "Elliotson", ",", "and", "to", "place", "all", "of", "his", "endeavours", "and", "enterpri...
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25
[ "(", "1842", ")", "''", "which", "is", "held", "by", "the", "British", "Library", ",", "and", "another", ",", "most", "likely", "written", "by", "either", "Wakley", "or", "one", "of", "his", "associates", ",", "held", "in", "the", "collection", "of", "...
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26
[ "As", "it", "is", "adjacent", "to", "the", "National", "Maritime", "Museum", ",", "the", "Tepid", "Baths", "pools", ",", "and", "the", "stylish", "Princes", "Wharf", "development", ",", "the", "Viaduct", "Harbour", "continues", "to", "be", "a", "popular", ...
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27
[ "Promoted", "to", "lieutenant", "colonel", "on", "1", "March", "1934", ",", "McClellan", "performed", "a", "brief", "tour", "at", "the", "end", "of", "the", "occupation", "of", "Hait", "i", "from", "15", "June", "to", "15", "August", "that", "same", "yea...
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28
[ "After", "a", "few", "months", "at", "the", "Philadelphia", "Naval", "Shipyard", ",", "he", "was", "sent", "to", "Shanghai", "to", "serve", "with", "the", "4th", "Marine", "Regiment", "briefly", "before", "being", "diverted", "to", "the", "Philippines", "aga...
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29
[ "There", ",", "he", "commanded", "the", "Marine", "Barracks", "at", "the", "Cavite", "Navy", "Yard", "until", "his", "retirement", "on", "30", "June", "1936", "." ]
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30
[ "However", ",", "contrary", "to", "these", "concerns", ",", "the", "DPAC", "has", "actually", "benefited", "the", "Carolina", "Theatre", "through", "co-promotion", "opportunities", "and", "other", "policies", "and", "activities", "." ]
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31
[ "``", "HobbyKids", "Adventures", "``", "merchandise", "are", "sold", "exclusively", "by", "Walmart", "." ]
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32
[ "Star", "Pals", "kids", "'", "meal", "toys", "are", "available", "at", "Hardee", "'s", "and", "Carl", "'s", "Jr.", "as", "of", "August", "2019", "." ]
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33
[ "In", "1952", "Beale", "AFB", "was", "placed", "in", "inactive", "status", "for", "conversion", "to", "an", "operational", "airbase", "." ]
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34
[ "Headquarters", ",", "Aviation", "Engineer", "Force", "administered", "the", "base", "for", "the", "next", "six", "years", "while", "a", "runway", "was", "laid", "down", ",", "and", "appropriate", "support", "facilities", "(", "hangars", ",", "maintenance", "s...
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35
[ "The", "2275th", "Air", "Base", "Squadron", "was", "the", "coordinating", "organization", "during", "the", "construction", "period", "." ]
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36
[ "Also", "in", "1952", ",", "Beale", "stopped", "being", "used", "as", "a", "bombing", "range", "and", "the", "U.S.", "Government", "declared", "portions", "of", "Camp", "Beale", "/", "Beale", "AFB", "as", "excess", ",", "eventually", "transferring", "out", ...
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37
[ "The", "new", "home", "of", "PAOK", ",", "the", "PAOK", "Sports", "Arena", ",", "able", "to", "hold", "8,500", "fans", ",", "was", "inaugurated", "on", "17", "March", "2000", "." ]
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38
[ "That", "marked", "the", "end", "of", "a", "long", "period", "of", "time", ",", "in", "which", "the", "club", "had", "shared", "the", "home", "court", "of", "Alexandreio", "Melathron", "with", "Aris", "." ]
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39
[ "Bane", "Prelević", "returned", "to", "PAOK", ",", "after", "having", "quick", "spells", "at", "Kinder", "Bologna", "and", "AEK", "Athens", ",", "and", "then", "quit", "playing", "basketball", "at", "the", "end", "of", "the", "1999–00", "season", "." ]
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[ "Hart", "'s", "surgery", "was", "completed", "at", "the", "University", "of", "Oregon", "Medical", "School", "over", "the", "1917–1918", "winter", "vacation", "." ]
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[ 0, 0, 0, 0, 0, 0, 0, 29, 29, 29, 29, 29, 0, 0, 0, 0, 0, 0 ]
82
[ "The", "Tigers", "have", "played", "in", "three", "different", "home", "ball", "parks", ",", "Bennett", "Park", "from", "1901", "through", "1911", ",", "Tiger", "Stadium", "(", "also", "known", "as", "Navin", "Field", "and", "Briggs", "Stadium", ")", "from...
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[ 0, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 13, 13, 0, 13, 13, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0 ]
83
[ "They", "had", "a", "record", "of", "five", "wins", "and", "two", "losses", "in", "Opening", "Day", "games", "at", "Bennett", "Park", ",", "19", "wins", "and", "22", "losses", "at", "Tiger", "Stadium", "and", "three", "wins", "and", "four", "losses", "...
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 36, 36, 36, 0, 0, 0, 0, 0, 0, 0 ]
84
[ "Their", "record", "in", "Opening", "Day", "away", "games", "is", "27", "wins", ",", "31", "losses", "and", "one", "tie", "." ]
[ 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 36, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
85
[ "In", "2010", ",", "the", "rising", "level", "of", "the", "Yacyretá", "Dam", "flooded", "the", "tracks", "on", "the", "Argentinian", "side", ",", "which", "halted", "grain", "exports", "by", "rail", "from", "the", "Encarnacion", "goods", "terminal", "until",...
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
86
[ "The", "church", "was", "part", "of", "the", "Wesleyan", "Methodist", "tradition", ",", "and", "during", "the", "early", "20th", "century", "became", "part", "of", "a", "circuit", "of", "six", "local", "Wesleyan", "churches", ";", "the", "others", "were", ...
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 0, 21, 0, 21, 0 ]
87
[ "In", "December", "2014", "Willett", "won", "the", "Nedbank", "Golf", "Challenge", "in", "Sun", "City", ",", "South", "Afric", "a", "and", ",", "with", "a", "third", "place", "at", "the", "2015", "WGC-Cadillac", "Match", "Play", "in", "May", "2015", ",",...
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88
[ "The", "show", "was", "revived", "for", "a", "reunion", "of", "the", "63rd", "Division", "held", "at", "the", "Statler", "Hilton", "Hotel", "in", "New", "York", "City", "in", "July", "1965", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 4, 4, 4, 0, 0, 0, 0 ]
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89
[ "For", "the", "re-mount", "the", "characters", "of", "Tom", "Wand", "and", "El", "Stinko", "became", "'", "Tom", "Misse", "l", "'", ",", "American", "astronaut", "and", "'", "El", "Stinko", "'", ",", "Russian", "cosmonaut", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0 ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 0, 0, 0, 0, 0, 0, 0, 21, 0, 0 ]
90
[ "Participating", "veterans", "included", "Fredd", "Wayne", ",", "Joseph", "Pollock", ",", "George", "Goldberg", ",", "Gordon", "Meyers", ",", "Nick", "Bonardi", ",", "Danny", "Canestraro", ",", "Harry", "Butensky", ",", "Herb", "Behrens", "and", "Al", "LaVecch...
[ 0, 0, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0 ]
[ 0, 0, 0, 57, 57, 0, 57, 57, 0, 57, 57, 0, 57, 57, 0, 57, 57, 0, 57, 57, 0, 57, 57, 0, 57, 57, 0, 57, 57, 0 ]
91
[ "The", "Atlanta", "Dream", "are", "a", "2008", "expansion", "team", "in", "the", "Women", "'s", "National", "Basketball", "Association", "." ]
[ 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0 ]
[ 0, 37, 37, 0, 0, 0, 0, 0, 0, 0, 36, 36, 36, 36, 36, 0 ]
92
[ "From", "their", "inception", "until", "the", "2016", "season", ",", "the", "Dream", "shared", "Philips", "Arena", "with", "their", "NBA", "counterpart", ";", "however", ",", "the", "Dream", "moved", "to", "McCamish", "Pavilion", "on", "the", "campus", "of",...
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 0, 13, 0, 0, 0, 36, 0, 0, 0, 0, 0, 37, 0, 0, 13, 13, 0, 0, 0, 0, 29, 29, 0, 0, 0, 0, 13, 13, 0, 0, 0, 36, 0, 0, 0, 0, 0, 0, 0, 0 ]
93
[ "After", "one", "more", "season", "in", "State", "Farm", "Arena", "(", "after", "the", "Philips", "was", "renamed", "during", "the", "renovations", ")", ",", "they", "moved", "their", "home", "games", "to", "the", "new", "Gateway", "Center", "Arena", "for"...
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94
[ "The", "Southern", "Sudan", "Autonomous", "Region", "was", "abolished", "in", "1983", "." ]
[ 0, 4, 4, 4, 4, 0, 0, 0, 0, 0 ]
[ 0, 21, 21, 21, 21, 0, 0, 0, 0, 0 ]
95
[ "Between", "1987", "and", "1989", "a", "Council", "for", "the", "South", "existed", "in", "Southern", "Sudan", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0 ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 21, 0 ]
96
[ "Following", "the", "signing", "of", "the", "Khartoum", "Peace", "Agreement", "of", "1997", ",", "a", "Southern", "Sudan", "Coordination", "Council", "was", "established", "initially", "led", "by", "Riek", "Macha", "r", "who", "was", "also", "appointed", "Assi...
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97
[ "It", "is", "currently", "owned", "by", "the", "McMenamins", "brewpub", "chain", "." ]
[ 0, 0, 0, 0, 0, 0, 5, 5, 5, 0 ]
[ 0, 0, 0, 0, 0, 0, 28, 28, 28, 0 ]
98
[ "In", "early", "2012", ",", "Jeff", "Pehrson", "and", "Jim", "Brunberg", "performed", "together", "for", "the", "first", "time", "since", "2006", "with", "a", "Box", "Set", "Duo", "reunion", "and", "later", "played", "a", "7", "night", "run", "in", "June...
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99
[ "The", "game", "was", "held", "at", "Bank", "of", "America", "Stadium", "in", "Charlotte", ",", "North", "Carolina", "on", "December", "4", ",", "2010", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 0, 21, 21, 0, 0, 0, 0, 0, 0 ]
End of preview. Expand in Data Studio

DFKI-SLT/few-nerd（複製）

データの内容

Wikipedia の文を元にした、人手で注釈された細粒度の固有表現認識（NER）のデータ。1 件は 1 文で、tokens の各語に粗い型（ner_tags、8 種）と細かい型（fine_ner_tags、66 種）のラベルが付く。タグは IO 方式で、教師ありの構成と、少数ショットの intra・inter の 3 つの構成に分かれる。Apple の果物と企業のような名前の曖昧さを直接問うデータではない。

データの概要

項目 値
行数 514,111（3 構成・9 分割の合計）
列数 4
容量 63.67 MB（ファイル 11 本）
ファイルの形式 parquet 9
言語 en
公開 public

構成と分割

構成（config） 分割（split）ごとの行数 列数
inter train 130,112、validation 18,817、test 14,007 4
intra train 99,519、validation 19,358、test 44,059 4
supervised train 131,767、validation 18,824、test 37,648 4

列（構成 inter・分割 train。どの構成も同じ列）

列 型 先頭の行の値
id string 0
tokens list<string> [26 件]
ner_tags list<class_label(9: O, art, building, event, location, organization …)> [26 件]
fine_ner_tags list<class_label(67: O, art-broadcastprogram, art-film, art-music, art-other, art-painting …)> [26 件]

行数と列は HF のデータセットビューアの集計（元のデータセットの現在の版で数えたもの）。全データセットの一覧は HYUKI5/dataset-catalog にある。

複製について

DFKI-SLT/few-nerd をリビジョン 205f3e9c9f3577ea2561d43f2f62dc249ab92d5b の時点でそのまま複製したもの。中身は変えていない。判断モデル Laya（convaiinnovations/laya）の微調整に使う候補として、調査メモ（Laya 学習用データセット調査）に載せたデータを集めている。

出典

論文

アノテーションの方法

キュレーションの根拠

More Information Needed

初期のデータ収集と正規化

More Information Needed

注釈のプロセス

More Information Needed

注釈者は誰か?

More Information Needed

微調整に使うときの注意

  • 調査メモの台帳での区分: B（継承条件・gated・モデル由来の条件・上流の規約などが付く）。区分は記載の文言で機械的に付けたもので、法的な判定ではない。学習に入れる前に元のカードとライセンスの原文で確かめる。
  • Laya の入力は 2,048 token まで（ModernBERT-large / mmBERT）。長い本文は先頭から黙って切られるので、切れる行は事前に除くか、要約・切り出しをしてから入れる。
  • 分割は記事・文書単位で行い、同じ本文から作った別の問いを学習側と較正側に分けない。温度と閾値は学習に使っていない分割で別々に当てる。

調査メモの記述

メモ 節 ライセンス（記載） 確認 注記
ontology_kg_entity.md Q2. エンティティ型付け・NER・エンティティリンキング・名寄せ（同名企業の寄せ）にどんなデータがあり、Laya にどう載るか (L61) cc-by-sa-4.0 確認済 確認印なし(各ファイルの凡例により確認済扱い) ／ 原文注記: 文脈は Wikipedia。企業名の曖昧さ（Apple の果物 / 企業）を直接問うデータではない。SA 継承
jev_sources_tasksource_bekko.csv CSV（bekko-system-one-dataset-v0） CC BY-SA 4.0 確認済 [Jev 元データ source] source_annotation_converted(Choice/Noul/Score recast; soft t

元のカード（日本語訳）

以下は元のデータセットのカードの本文を日本語に訳したもの（機械翻訳）。訳に疑問があるときは、末尾の原文を確かめる。

"Few-NERD" のデータセットカード

目次

  • [データセットの説明](

#dataset-description)

  • データセットの概要
  • 対応するタスクとリーダーボード
  • 言語
  • データセットの構造
    • データインスタンス
    • データフィールド
    • データ分割
  • データセットの作成
    • キュレーションの根拠
    • ソースデータ
    • 注釈
    • 個人情報と機微情報
  • データ利用上の考慮事項
    • データセットの社会的影響
    • バイアスの検討
    • その他の既知の限界
  • 追加情報
    • データセットのキュレーター
    • ライセンス情報
    • 引用情報
    • 貢献

データセットの説明

データセットの概要

このスクリプトは、https://ningding97.github.io/fewnerd/ から Few-NERD データセットを読み込むためのものである。

Few-NERD は、大規模で細粒度の人手注釈付き固有表現認識データセットである。8 種の粗いタイプ、66 種の細かいタイプ、188,200 文、491,711 個のエンティティ、4,601,223 トークンを含む。3 つのベンチマークタスクが構築されており、1 つは教師あり（Few-NERD (SUP)）、残り 2 つは少数ショット（Few-NERD (INTRA) と Few-NERD (INTER)）である。

NER タグには IO タグ付け方式を用いる。元データは、2 列の CoNLL 形式で、文の区切りに空行を使う。文はランダムに並べられているため、DOCSTART の情報は提供されていない。

詳細は https://ningding97.github.io/fewnerd/ と https://aclanthology.org/2021.acl-long.248/ を参照。

対応するタスクとリーダーボード

言語

英語

データセットの構造

データインスタンス

  • ダウンロードしたデータセットファイルのサイズ:

    • super: 14.6 MB
    • intra: 11.4 MB
    • inter: 11.5 MB

  • 生成されたデータセットのサイズ:

    • super: 116.9 MB
    • intra: 106.2 MB
    • inter: 106.2 MB

  • 使用ディスク容量の合計: 366.8 MB

'train' の例は次の通りである。

{
    'id': '1', 
    'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'], 
    'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0], 
    'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}

データフィールド

データフィールドは、すべての分割で同じである。

  • id: string 型の特徴量。
  • tokens: string 型の特徴量のリスト。
  • ner_tags: 分類ラベルのリスト。取りうる値は O（0）、art（1）、building（2）、event（3）、location（4）、organization（5）、other（6）、person（7）、product（8）を含む。
  • fine_ner_tags: 細粒度の分類ラベルのリスト。取りうる値は O（0）、art-broadcastprogram（1）、art-film（2）、... を含む。

データ分割

タスク Train Dev Test
SUP 131767 18824 37648
INTRA 99519 19358 44059
INTER 130112 18817 14007

データセットの作成

キュレーションの根拠

More Information Needed

ソースデータ

初期のデータ収集と正規化

More Information Needed

ソース言語の生成者は誰か?

More Information Needed

注釈

注釈のプロセス

More Information Needed

注釈者は誰か?

More Information Needed

個人情報と機微情報

More Information Needed

データ利用上の考慮事項

データセットの社会的影響

More Information Needed

バイアスの検討

More Information Needed

その他の既知の限界

More Information Needed

追加情報

データセットのキュレーター

More Information Needed

ライセンス情報

CC BY-SA 4.0 license

引用情報 

@inproceedings{ding-etal-2021-nerd,
    title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
    author = "Ding, Ning  and
      Xu, Guangwei  and
      Chen, Yulin  and
      Wang, Xiaobin  and
      Han, Xu  and
      Xie, Pengjun  and
      Zheng, Haitao  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.248",
    doi = "10.18653/v1/2021.acl-long.248",
    pages = "3198--3213",
}

貢献

原文（英語）

Dataset Card for "Few-NERD"

#dataset-description)

Dataset Summary

This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/.

Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).

NER tags use the IO tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered.

For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/.

Supported Tasks and Leaderboards

Languages

English

Dataset Structure

Data Instances

  • Size of downloaded dataset files:

    • super: 14.6 MB
    • intra: 11.4 MB
    • inter: 11.5 MB

  • Size of the generated dataset:

    • super: 116.9 MB
    • intra: 106.2 MB
    • inter: 106.2 MB

  • Total amount of disk used: 366.8 MB

An example of 'train' looks as follows.

{
    'id': '1', 
    'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'], 
    'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0], 
    'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}

Data Fields

The data fields are the same among all splits.

  • id: a string feature.
  • tokens: a list of string features.
  • ner_tags: a list of classification labels, with possible values including O (0), art (1), building (2), event (3), location (4), organization (5), other(6), person (7), product (8)
  • fine_ner_tags: a list of fine-grained classification labels, with possible values including O (0), art-broadcastprogram (1), art-film (2), ...

Data Splits

Task Train Dev Test
SUP 131767 18824 37648
INTRA 99519 19358 44059
INTER 130112 18817 14007

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

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Additional Information

Dataset Curators

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Licensing Information

CC BY-SA 4.0 license

Citation Information 

@inproceedings{ding-etal-2021-nerd,
    title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
    author = "Ding, Ning  and
      Xu, Guangwei  and
      Chen, Yulin  and
      Wang, Xiaobin  and
      Han, Xu  and
      Xie, Pengjun  and
      Zheng, Haitao  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.248",
    doi = "10.18653/v1/2021.acl-long.248",
    pages = "3198--3213",
}

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