datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
myradeng/diffusion_db_dedup_from50k_val_v2 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: seed
dtype: uint32
- name: step
dtype: uint16
- name: cfg
dtype: float32
- name: sampler
dtype: string
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: user_name
dtype: string
- name: timestamp
dtype: timestamp[ns, tz=UTC]
- name: image_nsfw
dtype: float32
- name: prompt_nsfw
dtype: float32
- name: __index_level_0__
dtype: int64
- name: image_path
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 5204531732.444004
num_examples: 8680
download_size: 5298548135
dataset_size: 5204531732.444004
---
# Dataset Card for "diffusion_db_dedup_from50k_val_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rntc/blurb_jnlpba_a-0-tm | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: type
dtype: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B
'2': I
splits:
- name: train
num_bytes: 53054955
num_examples: 18607
- name: validation
num_bytes: 5346325
num_examples: 1939
- name: test
num_bytes: 11932733
num_examples: 4260
download_size: 10155008
dataset_size: 70334013
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
fathyshalab/clinic-small_talk | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: text
dtype: string
- name: label
dtype: int64
- name: label_text
dtype: string
splits:
- name: train
num_bytes: 54000.1
num_examples: 805
- name: test
num_bytes: 23142.9
num_examples: 345
download_size: 0
dataset_size: 77143.0
---
# Dataset Card for "clinic-small_talk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_DistilRoBERTa_FinetunedNEW | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: '0'
dtype: float32
- name: '1'
dtype: float32
- name: '2'
dtype: float32
- name: '3'
dtype: float32
- name: '4'
dtype: float32
- name: '5'
dtype: float32
- name: '6'
dtype: float32
- name: '7'
dtype: float32
- name: '8'
dtype: float32
- name: '9'
dtype: float32
- name: '10'
dtype: float32
- name: '11'
dtype: float32
- name: '12'
dtype: float32
- name: '13'
dtype: float32
- name: '14'
dtype: float32
- name: '15'
dtype: float32
- name: '16'
dtype: float32
- name: '17'
dtype: float32
- name: '18'
dtype: float32
- name: '19'
dtype: float32
- name: '20'
dtype: float32
- name: '21'
dtype: float32
- name: '22'
dtype: float32
- name: '23'
dtype: float32
- name: '24'
dtype: float32
- name: '25'
dtype: float32
- name: '26'
dtype: float32
- name: '27'
dtype: float32
- name: '28'
dtype: float32
- name: '29'
dtype: float32
- name: '30'
dtype: float32
- name: '31'
dtype: float32
- name: '32'
dtype: float32
- name: '33'
dtype: float32
- name: '34'
dtype: float32
- name: '35'
dtype: float32
- name: '36'
dtype: float32
- name: '37'
dtype: float32
- name: '38'
dtype: float32
- name: '39'
dtype: float32
- name: '40'
dtype: float32
- name: '41'
dtype: float32
- name: '42'
dtype: float32
- name: '43'
dtype: float32
- name: '44'
dtype: float32
- name: '45'
dtype: float32
- name: '46'
dtype: float32
- name: '47'
dtype: float32
- name: '48'
dtype: float32
- name: '49'
dtype: float32
- name: '50'
dtype: float32
- name: '51'
dtype: float32
- name: '52'
dtype: float32
- name: '53'
dtype: float32
- name: '54'
dtype: float32
- name: '55'
dtype: float32
- name: '56'
dtype: float32
- name: '57'
dtype: float32
- name: '58'
dtype: float32
- name: '59'
dtype: float32
- name: '60'
dtype: float32
- name: '61'
dtype: float32
- name: '62'
dtype: float32
- name: '63'
dtype: float32
- name: '64'
dtype: float32
- name: '65'
dtype: float32
- name: '66'
dtype: float32
- name: '67'
dtype: float32
- name: '68'
dtype: float32
- name: '69'
dtype: float32
- name: '70'
dtype: float32
- name: '71'
dtype: float32
- name: '72'
dtype: float32
- name: '73'
dtype: float32
- name: '74'
dtype: float32
- name: '75'
dtype: float32
- name: '76'
dtype: float32
- name: '77'
dtype: float32
- name: '78'
dtype: float32
- name: '79'
dtype: float32
- name: '80'
dtype: float32
- name: '81'
dtype: float32
- name: '82'
dtype: float32
- name: '83'
dtype: float32
- name: '84'
dtype: float32
- name: '85'
dtype: float32
- name: '86'
dtype: float32
- name: '87'
dtype: float32
- name: '88'
dtype: float32
- name: '89'
dtype: float32
- name: '90'
dtype: float32
- name: '91'
dtype: float32
- name: '92'
dtype: float32
- name: '93'
dtype: float32
- name: '94'
dtype: float32
- name: '95'
dtype: float32
- name: '96'
dtype: float32
- name: '97'
dtype: float32
- name: '98'
dtype: float32
- name: '99'
dtype: float32
- name: '100'
dtype: float32
- name: '101'
dtype: float32
- name: '102'
dtype: float32
- name: '103'
dtype: float32
- name: '104'
dtype: float32
- name: '105'
dtype: float32
- name: '106'
dtype: float32
- name: '107'
dtype: float32
- name: '108'
dtype: float32
- name: '109'
dtype: float32
- name: '110'
dtype: float32
- name: '111'
dtype: float32
- name: '112'
dtype: float32
- name: '113'
dtype: float32
- name: '114'
dtype: float32
- name: '115'
dtype: float32
- name: '116'
dtype: float32
- name: '117'
dtype: float32
- name: '118'
dtype: float32
- name: '119'
dtype: float32
- name: '120'
dtype: float32
- name: '121'
dtype: float32
- name: '122'
dtype: float32
- name: '123'
dtype: float32
- name: '124'
dtype: float32
- name: '125'
dtype: float32
- name: '126'
dtype: float32
- name: '127'
dtype: float32
- name: '128'
dtype: float32
- name: '129'
dtype: float32
- name: '130'
dtype: float32
- name: '131'
dtype: float32
- name: '132'
dtype: float32
- name: '133'
dtype: float32
- name: '134'
dtype: float32
- name: '135'
dtype: float32
- name: '136'
dtype: float32
- name: '137'
dtype: float32
- name: '138'
dtype: float32
- name: '139'
dtype: float32
- name: '140'
dtype: float32
- name: '141'
dtype: float32
- name: '142'
dtype: float32
- name: '143'
dtype: float32
- name: '144'
dtype: float32
- name: '145'
dtype: float32
- name: '146'
dtype: float32
- name: '147'
dtype: float32
- name: '148'
dtype: float32
- name: '149'
dtype: float32
- name: '150'
dtype: float32
- name: '151'
dtype: float32
- name: '152'
dtype: float32
- name: '153'
dtype: float32
- name: '154'
dtype: float32
- name: '155'
dtype: float32
- name: '156'
dtype: float32
- name: '157'
dtype: float32
- name: '158'
dtype: float32
- name: '159'
dtype: float32
- name: '160'
dtype: float32
- name: '161'
dtype: float32
- name: '162'
dtype: float32
- name: '163'
dtype: float32
- name: '164'
dtype: float32
- name: '165'
dtype: float32
- name: '166'
dtype: float32
- name: '167'
dtype: float32
- name: '168'
dtype: float32
- name: '169'
dtype: float32
- name: '170'
dtype: float32
- name: '171'
dtype: float32
- name: '172'
dtype: float32
- name: '173'
dtype: float32
- name: '174'
dtype: float32
- name: '175'
dtype: float32
- name: '176'
dtype: float32
- name: '177'
dtype: float32
- name: '178'
dtype: float32
- name: '179'
dtype: float32
- name: '180'
dtype: float32
- name: '181'
dtype: float32
- name: '182'
dtype: float32
- name: '183'
dtype: float32
- name: '184'
dtype: float32
- name: '185'
dtype: float32
- name: '186'
dtype: float32
- name: '187'
dtype: float32
- name: '188'
dtype: float32
- name: '189'
dtype: float32
- name: '190'
dtype: float32
- name: '191'
dtype: float32
- name: '192'
dtype: float32
- name: '193'
dtype: float32
- name: '194'
dtype: float32
- name: '195'
dtype: float32
- name: '196'
dtype: float32
- name: '197'
dtype: float32
- name: '198'
dtype: float32
- name: '199'
dtype: float32
- name: '200'
dtype: float32
- name: '201'
dtype: float32
- name: '202'
dtype: float32
- name: '203'
dtype: float32
- name: '204'
dtype: float32
- name: '205'
dtype: float32
- name: '206'
dtype: float32
- name: '207'
dtype: float32
- name: '208'
dtype: float32
- name: '209'
dtype: float32
- name: '210'
dtype: float32
- name: '211'
dtype: float32
- name: '212'
dtype: float32
- name: '213'
dtype: float32
- name: '214'
dtype: float32
- name: '215'
dtype: float32
- name: '216'
dtype: float32
- name: '217'
dtype: float32
- name: '218'
dtype: float32
- name: '219'
dtype: float32
- name: '220'
dtype: float32
- name: '221'
dtype: float32
- name: '222'
dtype: float32
- name: '223'
dtype: float32
- name: '224'
dtype: float32
- name: '225'
dtype: float32
- name: '226'
dtype: float32
- name: '227'
dtype: float32
- name: '228'
dtype: float32
- name: '229'
dtype: float32
- name: '230'
dtype: float32
- name: '231'
dtype: float32
- name: '232'
dtype: float32
- name: '233'
dtype: float32
- name: '234'
dtype: float32
- name: '235'
dtype: float32
- name: '236'
dtype: float32
- name: '237'
dtype: float32
- name: '238'
dtype: float32
- name: '239'
dtype: float32
- name: '240'
dtype: float32
- name: '241'
dtype: float32
- name: '242'
dtype: float32
- name: '243'
dtype: float32
- name: '244'
dtype: float32
- name: '245'
dtype: float32
- name: '246'
dtype: float32
- name: '247'
dtype: float32
- name: '248'
dtype: float32
- name: '249'
dtype: float32
- name: '250'
dtype: float32
- name: '251'
dtype: float32
- name: '252'
dtype: float32
- name: '253'
dtype: float32
- name: '254'
dtype: float32
- name: '255'
dtype: float32
- name: '256'
dtype: float32
- name: '257'
dtype: float32
- name: '258'
dtype: float32
- name: '259'
dtype: float32
- name: '260'
dtype: float32
- name: '261'
dtype: float32
- name: '262'
dtype: float32
- name: '263'
dtype: float32
- name: '264'
dtype: float32
- name: '265'
dtype: float32
- name: '266'
dtype: float32
- name: '267'
dtype: float32
- name: '268'
dtype: float32
- name: '269'
dtype: float32
- name: '270'
dtype: float32
- name: '271'
dtype: float32
- name: '272'
dtype: float32
- name: '273'
dtype: float32
- name: '274'
dtype: float32
- name: '275'
dtype: float32
- name: '276'
dtype: float32
- name: '277'
dtype: float32
- name: '278'
dtype: float32
- name: '279'
dtype: float32
- name: '280'
dtype: float32
- name: '281'
dtype: float32
- name: '282'
dtype: float32
- name: '283'
dtype: float32
- name: '284'
dtype: float32
- name: '285'
dtype: float32
- name: '286'
dtype: float32
- name: '287'
dtype: float32
- name: '288'
dtype: float32
- name: '289'
dtype: float32
- name: '290'
dtype: float32
- name: '291'
dtype: float32
- name: '292'
dtype: float32
- name: '293'
dtype: float32
- name: '294'
dtype: float32
- name: '295'
dtype: float32
- name: '296'
dtype: float32
- name: '297'
dtype: float32
- name: '298'
dtype: float32
- name: '299'
dtype: float32
- name: '300'
dtype: float32
- name: '301'
dtype: float32
- name: '302'
dtype: float32
- name: '303'
dtype: float32
- name: '304'
dtype: float32
- name: '305'
dtype: float32
- name: '306'
dtype: float32
- name: '307'
dtype: float32
- name: '308'
dtype: float32
- name: '309'
dtype: float32
- name: '310'
dtype: float32
- name: '311'
dtype: float32
- name: '312'
dtype: float32
- name: '313'
dtype: float32
- name: '314'
dtype: float32
- name: '315'
dtype: float32
- name: '316'
dtype: float32
- name: '317'
dtype: float32
- name: '318'
dtype: float32
- name: '319'
dtype: float32
- name: '320'
dtype: float32
- name: '321'
dtype: float32
- name: '322'
dtype: float32
- name: '323'
dtype: float32
- name: '324'
dtype: float32
- name: '325'
dtype: float32
- name: '326'
dtype: float32
- name: '327'
dtype: float32
- name: '328'
dtype: float32
- name: '329'
dtype: float32
- name: '330'
dtype: float32
- name: '331'
dtype: float32
- name: '332'
dtype: float32
- name: '333'
dtype: float32
- name: '334'
dtype: float32
- name: '335'
dtype: float32
- name: '336'
dtype: float32
- name: '337'
dtype: float32
- name: '338'
dtype: float32
- name: '339'
dtype: float32
- name: '340'
dtype: float32
- name: '341'
dtype: float32
- name: '342'
dtype: float32
- name: '343'
dtype: float32
- name: '344'
dtype: float32
- name: '345'
dtype: float32
- name: '346'
dtype: float32
- name: '347'
dtype: float32
- name: '348'
dtype: float32
- name: '349'
dtype: float32
- name: '350'
dtype: float32
- name: '351'
dtype: float32
- name: '352'
dtype: float32
- name: '353'
dtype: float32
- name: '354'
dtype: float32
- name: '355'
dtype: float32
- name: '356'
dtype: float32
- name: '357'
dtype: float32
- name: '358'
dtype: float32
- name: '359'
dtype: float32
- name: '360'
dtype: float32
- name: '361'
dtype: float32
- name: '362'
dtype: float32
- name: '363'
dtype: float32
- name: '364'
dtype: float32
- name: '365'
dtype: float32
- name: '366'
dtype: float32
- name: '367'
dtype: float32
- name: '368'
dtype: float32
- name: '369'
dtype: float32
- name: '370'
dtype: float32
- name: '371'
dtype: float32
- name: '372'
dtype: float32
- name: '373'
dtype: float32
- name: '374'
dtype: float32
- name: '375'
dtype: float32
- name: '376'
dtype: float32
- name: '377'
dtype: float32
- name: '378'
dtype: float32
- name: '379'
dtype: float32
- name: '380'
dtype: float32
- name: '381'
dtype: float32
- name: '382'
dtype: float32
- name: '383'
dtype: float32
- name: '384'
dtype: float32
- name: '385'
dtype: float32
- name: '386'
dtype: float32
- name: '387'
dtype: float32
- name: '388'
dtype: float32
- name: '389'
dtype: float32
- name: '390'
dtype: float32
- name: '391'
dtype: float32
- name: '392'
dtype: float32
- name: '393'
dtype: float32
- name: '394'
dtype: float32
- name: '395'
dtype: float32
- name: '396'
dtype: float32
- name: '397'
dtype: float32
- name: '398'
dtype: float32
- name: '399'
dtype: float32
- name: '400'
dtype: float32
- name: '401'
dtype: float32
- name: '402'
dtype: float32
- name: '403'
dtype: float32
- name: '404'
dtype: float32
- name: '405'
dtype: float32
- name: '406'
dtype: float32
- name: '407'
dtype: float32
- name: '408'
dtype: float32
- name: '409'
dtype: float32
- name: '410'
dtype: float32
- name: '411'
dtype: float32
- name: '412'
dtype: float32
- name: '413'
dtype: float32
- name: '414'
dtype: float32
- name: '415'
dtype: float32
- name: '416'
dtype: float32
- name: '417'
dtype: float32
- name: '418'
dtype: float32
- name: '419'
dtype: float32
- name: '420'
dtype: float32
- name: '421'
dtype: float32
- name: '422'
dtype: float32
- name: '423'
dtype: float32
- name: '424'
dtype: float32
- name: '425'
dtype: float32
- name: '426'
dtype: float32
- name: '427'
dtype: float32
- name: '428'
dtype: float32
- name: '429'
dtype: float32
- name: '430'
dtype: float32
- name: '431'
dtype: float32
- name: '432'
dtype: float32
- name: '433'
dtype: float32
- name: '434'
dtype: float32
- name: '435'
dtype: float32
- name: '436'
dtype: float32
- name: '437'
dtype: float32
- name: '438'
dtype: float32
- name: '439'
dtype: float32
- name: '440'
dtype: float32
- name: '441'
dtype: float32
- name: '442'
dtype: float32
- name: '443'
dtype: float32
- name: '444'
dtype: float32
- name: '445'
dtype: float32
- name: '446'
dtype: float32
- name: '447'
dtype: float32
- name: '448'
dtype: float32
- name: '449'
dtype: float32
- name: '450'
dtype: float32
- name: '451'
dtype: float32
- name: '452'
dtype: float32
- name: '453'
dtype: float32
- name: '454'
dtype: float32
- name: '455'
dtype: float32
- name: '456'
dtype: float32
- name: '457'
dtype: float32
- name: '458'
dtype: float32
- name: '459'
dtype: float32
- name: '460'
dtype: float32
- name: '461'
dtype: float32
- name: '462'
dtype: float32
- name: '463'
dtype: float32
- name: '464'
dtype: float32
- name: '465'
dtype: float32
- name: '466'
dtype: float32
- name: '467'
dtype: float32
- name: '468'
dtype: float32
- name: '469'
dtype: float32
- name: '470'
dtype: float32
- name: '471'
dtype: float32
- name: '472'
dtype: float32
- name: '473'
dtype: float32
- name: '474'
dtype: float32
- name: '475'
dtype: float32
- name: '476'
dtype: float32
- name: '477'
dtype: float32
- name: '478'
dtype: float32
- name: '479'
dtype: float32
- name: '480'
dtype: float32
- name: '481'
dtype: float32
- name: '482'
dtype: float32
- name: '483'
dtype: float32
- name: '484'
dtype: float32
- name: '485'
dtype: float32
- name: '486'
dtype: float32
- name: '487'
dtype: float32
- name: '488'
dtype: float32
- name: '489'
dtype: float32
- name: '490'
dtype: float32
- name: '491'
dtype: float32
- name: '492'
dtype: float32
- name: '493'
dtype: float32
- name: '494'
dtype: float32
- name: '495'
dtype: float32
- name: '496'
dtype: float32
- name: '497'
dtype: float32
- name: '498'
dtype: float32
- name: '499'
dtype: float32
- name: '500'
dtype: float32
- name: '501'
dtype: float32
- name: '502'
dtype: float32
- name: '503'
dtype: float32
- name: '504'
dtype: float32
- name: '505'
dtype: float32
- name: '506'
dtype: float32
- name: '507'
dtype: float32
- name: '508'
dtype: float32
- name: '509'
dtype: float32
- name: '510'
dtype: float32
- name: '511'
dtype: float32
- name: '512'
dtype: float32
- name: '513'
dtype: float32
- name: '514'
dtype: float32
- name: '515'
dtype: float32
- name: '516'
dtype: float32
- name: '517'
dtype: float32
- name: '518'
dtype: float32
- name: '519'
dtype: float32
- name: '520'
dtype: float32
- name: '521'
dtype: float32
- name: '522'
dtype: float32
- name: '523'
dtype: float32
- name: '524'
dtype: float32
- name: '525'
dtype: float32
- name: '526'
dtype: float32
- name: '527'
dtype: float32
- name: '528'
dtype: float32
- name: '529'
dtype: float32
- name: '530'
dtype: float32
- name: '531'
dtype: float32
- name: '532'
dtype: float32
- name: '533'
dtype: float32
- name: '534'
dtype: float32
- name: '535'
dtype: float32
- name: '536'
dtype: float32
- name: '537'
dtype: float32
- name: '538'
dtype: float32
- name: '539'
dtype: float32
- name: '540'
dtype: float32
- name: '541'
dtype: float32
- name: '542'
dtype: float32
- name: '543'
dtype: float32
- name: '544'
dtype: float32
- name: '545'
dtype: float32
- name: '546'
dtype: float32
- name: '547'
dtype: float32
- name: '548'
dtype: float32
- name: '549'
dtype: float32
- name: '550'
dtype: float32
- name: '551'
dtype: float32
- name: '552'
dtype: float32
- name: '553'
dtype: float32
- name: '554'
dtype: float32
- name: '555'
dtype: float32
- name: '556'
dtype: float32
- name: '557'
dtype: float32
- name: '558'
dtype: float32
- name: '559'
dtype: float32
- name: '560'
dtype: float32
- name: '561'
dtype: float32
- name: '562'
dtype: float32
- name: '563'
dtype: float32
- name: '564'
dtype: float32
- name: '565'
dtype: float32
- name: '566'
dtype: float32
- name: '567'
dtype: float32
- name: '568'
dtype: float32
- name: '569'
dtype: float32
- name: '570'
dtype: float32
- name: '571'
dtype: float32
- name: '572'
dtype: float32
- name: '573'
dtype: float32
- name: '574'
dtype: float32
- name: '575'
dtype: float32
- name: '576'
dtype: float32
- name: '577'
dtype: float32
- name: '578'
dtype: float32
- name: '579'
dtype: float32
- name: '580'
dtype: float32
- name: '581'
dtype: float32
- name: '582'
dtype: float32
- name: '583'
dtype: float32
- name: '584'
dtype: float32
- name: '585'
dtype: float32
- name: '586'
dtype: float32
- name: '587'
dtype: float32
- name: '588'
dtype: float32
- name: '589'
dtype: float32
- name: '590'
dtype: float32
- name: '591'
dtype: float32
- name: '592'
dtype: float32
- name: '593'
dtype: float32
- name: '594'
dtype: float32
- name: '595'
dtype: float32
- name: '596'
dtype: float32
- name: '597'
dtype: float32
- name: '598'
dtype: float32
- name: '599'
dtype: float32
- name: '600'
dtype: float32
- name: '601'
dtype: float32
- name: '602'
dtype: float32
- name: '603'
dtype: float32
- name: '604'
dtype: float32
- name: '605'
dtype: float32
- name: '606'
dtype: float32
- name: '607'
dtype: float32
- name: '608'
dtype: float32
- name: '609'
dtype: float32
- name: '610'
dtype: float32
- name: '611'
dtype: float32
- name: '612'
dtype: float32
- name: '613'
dtype: float32
- name: '614'
dtype: float32
- name: '615'
dtype: float32
- name: '616'
dtype: float32
- name: '617'
dtype: float32
- name: '618'
dtype: float32
- name: '619'
dtype: float32
- name: '620'
dtype: float32
- name: '621'
dtype: float32
- name: '622'
dtype: float32
- name: '623'
dtype: float32
- name: '624'
dtype: float32
- name: '625'
dtype: float32
- name: '626'
dtype: float32
- name: '627'
dtype: float32
- name: '628'
dtype: float32
- name: '629'
dtype: float32
- name: '630'
dtype: float32
- name: '631'
dtype: float32
- name: '632'
dtype: float32
- name: '633'
dtype: float32
- name: '634'
dtype: float32
- name: '635'
dtype: float32
- name: '636'
dtype: float32
- name: '637'
dtype: float32
- name: '638'
dtype: float32
- name: '639'
dtype: float32
- name: '640'
dtype: float32
- name: '641'
dtype: float32
- name: '642'
dtype: float32
- name: '643'
dtype: float32
- name: '644'
dtype: float32
- name: '645'
dtype: float32
- name: '646'
dtype: float32
- name: '647'
dtype: float32
- name: '648'
dtype: float32
- name: '649'
dtype: float32
- name: '650'
dtype: float32
- name: '651'
dtype: float32
- name: '652'
dtype: float32
- name: '653'
dtype: float32
- name: '654'
dtype: float32
- name: '655'
dtype: float32
- name: '656'
dtype: float32
- name: '657'
dtype: float32
- name: '658'
dtype: float32
- name: '659'
dtype: float32
- name: '660'
dtype: float32
- name: '661'
dtype: float32
- name: '662'
dtype: float32
- name: '663'
dtype: float32
- name: '664'
dtype: float32
- name: '665'
dtype: float32
- name: '666'
dtype: float32
- name: '667'
dtype: float32
- name: '668'
dtype: float32
- name: '669'
dtype: float32
- name: '670'
dtype: float32
- name: '671'
dtype: float32
- name: '672'
dtype: float32
- name: '673'
dtype: float32
- name: '674'
dtype: float32
- name: '675'
dtype: float32
- name: '676'
dtype: float32
- name: '677'
dtype: float32
- name: '678'
dtype: float32
- name: '679'
dtype: float32
- name: '680'
dtype: float32
- name: '681'
dtype: float32
- name: '682'
dtype: float32
- name: '683'
dtype: float32
- name: '684'
dtype: float32
- name: '685'
dtype: float32
- name: '686'
dtype: float32
- name: '687'
dtype: float32
- name: '688'
dtype: float32
- name: '689'
dtype: float32
- name: '690'
dtype: float32
- name: '691'
dtype: float32
- name: '692'
dtype: float32
- name: '693'
dtype: float32
- name: '694'
dtype: float32
- name: '695'
dtype: float32
- name: '696'
dtype: float32
- name: '697'
dtype: float32
- name: '698'
dtype: float32
- name: '699'
dtype: float32
- name: '700'
dtype: float32
- name: '701'
dtype: float32
- name: '702'
dtype: float32
- name: '703'
dtype: float32
- name: '704'
dtype: float32
- name: '705'
dtype: float32
- name: '706'
dtype: float32
- name: '707'
dtype: float32
- name: '708'
dtype: float32
- name: '709'
dtype: float32
- name: '710'
dtype: float32
- name: '711'
dtype: float32
- name: '712'
dtype: float32
- name: '713'
dtype: float32
- name: '714'
dtype: float32
- name: '715'
dtype: float32
- name: '716'
dtype: float32
- name: '717'
dtype: float32
- name: '718'
dtype: float32
- name: '719'
dtype: float32
- name: '720'
dtype: float32
- name: '721'
dtype: float32
- name: '722'
dtype: float32
- name: '723'
dtype: float32
- name: '724'
dtype: float32
- name: '725'
dtype: float32
- name: '726'
dtype: float32
- name: '727'
dtype: float32
- name: '728'
dtype: float32
- name: '729'
dtype: float32
- name: '730'
dtype: float32
- name: '731'
dtype: float32
- name: '732'
dtype: float32
- name: '733'
dtype: float32
- name: '734'
dtype: float32
- name: '735'
dtype: float32
- name: '736'
dtype: float32
- name: '737'
dtype: float32
- name: '738'
dtype: float32
- name: '739'
dtype: float32
- name: '740'
dtype: float32
- name: '741'
dtype: float32
- name: '742'
dtype: float32
- name: '743'
dtype: float32
- name: '744'
dtype: float32
- name: '745'
dtype: float32
- name: '746'
dtype: float32
- name: '747'
dtype: float32
- name: '748'
dtype: float32
- name: '749'
dtype: float32
- name: '750'
dtype: float32
- name: '751'
dtype: float32
- name: '752'
dtype: float32
- name: '753'
dtype: float32
- name: '754'
dtype: float32
- name: '755'
dtype: float32
- name: '756'
dtype: float32
- name: '757'
dtype: float32
- name: '758'
dtype: float32
- name: '759'
dtype: float32
- name: '760'
dtype: float32
- name: '761'
dtype: float32
- name: '762'
dtype: float32
- name: '763'
dtype: float32
- name: '764'
dtype: float32
- name: '765'
dtype: float32
- name: '766'
dtype: float32
- name: '767'
dtype: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 80318780.21618997
num_examples: 26057
- name: test
num_bytes: 26774087.073587257
num_examples: 8686
download_size: 147166259
dataset_size: 107092867.28977722
---
# Dataset Card for "AA_DistilRoBERTa_FinetunedNEW"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Anusha64/aeon3 | ---
license: mit
---
|
Multimodal-Fatima/VQAv2_test_split_0 | ---
dataset_info:
features:
- name: question_type
dtype: string
- name: multiple_choice_answer
dtype: string
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: image
dtype: image
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_ViT_L_14_wo_openai
sequence: string
- name: clip_tags_ViT_L_14_with_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_with_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_with_openai
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_ViT_B_16_with_openai
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random
list:
- name: attribute
dtype: string
- name: box
sequence: float64
- name: captions_module
sequence: string
- name: captions_module_filter
sequence: string
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float64
- name: size
dtype: string
- name: tag
dtype: string
splits:
- name: test
num_bytes: 9462509481.0
num_examples: 44780
download_size: 1944305372
dataset_size: 9462509481.0
---
# Dataset Card for "VQAv2_test_split_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mvb6969/Fotos_mvb6969 | ---
license: openrail
---
|
Helife/mattis | ---
license: mit
---
|
ozayezerceli/eng-to-cypher-trQuestions | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: output_text
dtype: string
splits:
- name: train
num_bytes: 128546
num_examples: 120
download_size: 22033
dataset_size: 128546
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_TheBloke__tulu-7B-fp16 | ---
pretty_name: Evaluation run of TheBloke/tulu-7B-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/tulu-7B-fp16](https://huggingface.co/TheBloke/tulu-7B-fp16) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__tulu-7B-fp16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T23:41:54.207641](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__tulu-7B-fp16/blob/main/results_2023-10-22T23-41-54.207641.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2993917785234899,\n\
\ \"em_stderr\": 0.004690263056389047,\n \"f1\": 0.33736996644295303,\n\
\ \"f1_stderr\": 0.004651138439477223,\n \"acc\": 0.42508495529786566,\n\
\ \"acc_stderr\": 0.010526343784939971\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2993917785234899,\n \"em_stderr\": 0.004690263056389047,\n\
\ \"f1\": 0.33736996644295303,\n \"f1_stderr\": 0.004651138439477223\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11220621683093253,\n \
\ \"acc_stderr\": 0.008693743138242383\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637561\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/tulu-7B-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T23_41_54.207641
path:
- '**/details_harness|drop|3_2023-10-22T23-41-54.207641.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T23-41-54.207641.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T23_41_54.207641
path:
- '**/details_harness|gsm8k|5_2023-10-22T23-41-54.207641.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T23-41-54.207641.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:17:47.759549.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:17:47.759549.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:17:47.759549.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T23_41_54.207641
path:
- '**/details_harness|winogrande|5_2023-10-22T23-41-54.207641.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T23-41-54.207641.parquet'
- config_name: results
data_files:
- split: 2023_07_19T17_17_47.759549
path:
- results_2023-07-19T17:17:47.759549.parquet
- split: 2023_10_22T23_41_54.207641
path:
- results_2023-10-22T23-41-54.207641.parquet
- split: latest
path:
- results_2023-10-22T23-41-54.207641.parquet
---
# Dataset Card for Evaluation run of TheBloke/tulu-7B-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/tulu-7B-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/tulu-7B-fp16](https://huggingface.co/TheBloke/tulu-7B-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TheBloke__tulu-7B-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T23:41:54.207641](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__tulu-7B-fp16/blob/main/results_2023-10-22T23-41-54.207641.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2993917785234899,
"em_stderr": 0.004690263056389047,
"f1": 0.33736996644295303,
"f1_stderr": 0.004651138439477223,
"acc": 0.42508495529786566,
"acc_stderr": 0.010526343784939971
},
"harness|drop|3": {
"em": 0.2993917785234899,
"em_stderr": 0.004690263056389047,
"f1": 0.33736996644295303,
"f1_stderr": 0.004651138439477223
},
"harness|gsm8k|5": {
"acc": 0.11220621683093253,
"acc_stderr": 0.008693743138242383
},
"harness|winogrande|5": {
"acc": 0.7379636937647988,
"acc_stderr": 0.012358944431637561
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## 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
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
kiennguyen1703/HoChiMinhLeader | ---
license: apache-2.0
language:
- en
--- |
coallaoh/COCO-AB | ---
annotations_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
paperswithcode_id: coco
pretty_name: COCO
size_categories:
- 100K<n<1M
source_datasets:
- https://huggingface.co/datasets/HuggingFaceM4/COCO
task_categories:
- image-classification
---
## General Information
**Title**: COCO-AB
**Description**:
The COCO-AB dataset is an extension of the COCO 2014 training set, enriched with additional annotation byproducts (AB).
The data includes 82,765 reannotated images from the original COCO 2014 training set.
It has relevance in computer vision, specifically in object detection and location.
The aim of the dataset is to provide a richer understanding of the images (without extra costs) by recording additional actions and interactions from the annotation process.
**Links**:
- [ICCV'23 Paper](https://arxiv.org/abs/2303.17595)
- [Main Repository](https://github.com/naver-ai/NeglectedFreeLunch)
- [COCO Annotation Interface](https://github.com/naver-ai/coco-annotation-tool)
## Collection Process
**Collection Details**:
The additional annotations for the COCO-AB dataset were collected using Amazon Mechanical Turk (MTurk) workers from the US region, due to the task being described in English.
The task was designed as a human intelligence task (HIT), and the qualification approval rate was set at 90% to ensure the task's quality.
Each HIT contained 20 pages of annotation tasks, each page having a single candidate image to be tagged.
We follow the original annotation interface of COCO as much as possible.
See [GitHub repository](https://github.com/naver-ai/coco-annotation-tool) and [Paper](https://arxiv.org/abs/2303.17595) for further information.
A total of 4140 HITs were completed, with 365 HITs being rejected based on criteria such as recall rate, accuracy of icon location, task completion rate, and verification with database and secret hash code.
**Annotator Compensation**:
Annotators were paid 2.0 USD per HIT.
The median time taken to complete each HIT was 12.1 minutes, yielding an approximate hourly wage of 9.92 USD.
This wage is above the US federal minimum hourly wage.
A total of 8,280 USD was paid to the MTurk annotators, with an additional 20% fee paid to Amazon.
**Annotation Rejection**:
We rejected a HIT under the following circumstances.
- The recall rate was lower than 0.333.
- The accuracy of icon location is lower than 0.75.
- The annotator did not complete at least 16 out of the 20 pages of tasks.
- The annotation was not found in our database, and the secret hash code for confirming their completion was incorrect.
- In total, 365 out of 4,140 completed HITs (8.8%) were rejected.
**Collection Time**:
The entire annotation collection process took place between January 9, 2022, and January 12, 2022
## Data Schema
```json
{
"image_id": 459214,
"originalImageHeight": 428,
"originalImageWidth": 640,
"categories": [”car”, “bicycle”],
"imageHeight": 450,
"imageWidth": 450,
"timeSpent": 22283,
"actionHistories": [
{"actionType": ”add”,
"iconType": ”car”,
"pointTo": {"x": 0.583, "y": 0.588},
"timeAt": 16686},
{"actionType": ”add”,
"iconType": “bicycle”,
"pointTo": {"x": 0.592, "y": 0.639},
"timeAt": 16723}
],
"categoryHistories": [
{"categoryIndex": 1,
"categoryName": ”Animal”,
"timeAt": 10815,
"usingKeyboard": false},
{"categoryIndex": 10,
"categoryName": ”IndoorObjects”,
"timeAt": 19415,
"usingKeyboard": false}
],
"mouseTracking": [
{"x": 0.679, "y": 0.862, "timeAt": 15725},
{"x": 0.717, "y": 0.825, "timeAt": 15731}
],
"worker_id": "00AA3B5E80",
"assignment_id": "3AMYWKA6YLE80HK9QYYHI2YEL2YO6L",
"page_idx": 8
}
```
## Usage
One could use the annotation byproducts to improve the model generalisability and robustness.
This is appealing, as the annotation byproducts do not incur extra annotation costs for the annotators.
For more information, refer to our [ICCV'23 Paper](https://arxiv.org/abs/2303.17595).
## Dataset Statistics
Annotators have reannotated 82,765 (99.98%) of 82,783 training images from the COCO 2014 training set.
For those images, we have recorded the annotation byproducts.
We found that each HIT recalls 61.9% of the list of classes per image, with the standard deviation ±0.118%p.
The average localisation accuracy for icon placement is 92.3% where the standard deviation is ±0.057%p.
## Ethics and Legalities
The crowdsourced annotators were fairly compensated for their time at a rate well above the U.S. federal minimum wage.
In terms of data privacy, the dataset maintains the same ethical standards as the original COCO dataset.
Worker identifiers were anonymized using a non-reversible hashing function, ensuring privacy.
Our data collection has obtained IRB approval from an author’s institute.
For the future collection of annotation byproducts, we note that there exist potential risks that annotation byproducts may contain annotators’ privacy.
Data collectors may even attempt to leverage more private information as byproducts.
We urge data collectors not to collect or exploit private information from annotators.
Whenever appropriate, one must ask for the annotators’ consent.
## Maintenance and Updates
This section will be updated as and when there are changes or updates to the dataset.
## Known Limitations
Given the budget constraint, we have not been able to acquire 8+ annotations per sample, as done in the original work.
## Citation Information
```
@inproceedings{han2023iccv,
title = {Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts},
author = {Han, Dongyoon and Choe, Junsuk and Chun, Seonghyeok and Chung, John Joon Young and Chang, Minsuk and Yun, Sangdoo and Song, Jean Y. and Oh, Seong Joon},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2023}
}
``` |
tierdesafinante/kid_gohan | ---
license: openrail
---
|
atomwoz/j | ---
license: mit
---
|
Roscall/elvis_jailhouse_rock_speaking | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 4283299.0
num_examples: 1
download_size: 3488157
dataset_size: 4283299.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
daqc/constitucion-politica-del-peru-1993-qa-gemma-2b-it-format | ---
dataset_info:
features:
- name: pregunta
dtype: string
- name: respuesta
dtype: string
splits:
- name: train
num_bytes: 1807541
num_examples: 2075
download_size: 680292
dataset_size: 1807541
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xianni/apache-2.0 | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_porkorbeef__Llama-2-13b-public | ---
pretty_name: Evaluation run of porkorbeef/Llama-2-13b-public
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [porkorbeef/Llama-2-13b-public](https://huggingface.co/porkorbeef/Llama-2-13b-public)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_porkorbeef__Llama-2-13b-public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T07:54:08.425167](https://huggingface.co/datasets/open-llm-leaderboard/details_porkorbeef__Llama-2-13b-public/blob/main/results_2023-10-17T07-54-08.425167.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 5.76761744966443e-05,\n \"f1_stderr\"\
: 2.9264210748527048e-05,\n \"acc\": 0.24191002367797948,\n \"acc_stderr\"\
: 0.007022563065489301\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 5.76761744966443e-05,\n \"\
f1_stderr\": 2.9264210748527048e-05\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.48382004735595896,\n \"acc_stderr\": 0.014045126130978603\n\
\ }\n}\n```"
repo_url: https://huggingface.co/porkorbeef/Llama-2-13b-public
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|arc:challenge|25_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T07_54_08.425167
path:
- '**/details_harness|drop|3_2023-10-17T07-54-08.425167.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T07-54-08.425167.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T07_54_08.425167
path:
- '**/details_harness|gsm8k|5_2023-10-17T07-54-08.425167.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T07-54-08.425167.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hellaswag|10_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T02:45:47.354690.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-04T02:45:47.354690.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-04T02:45:47.354690.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T07_54_08.425167
path:
- '**/details_harness|winogrande|5_2023-10-17T07-54-08.425167.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T07-54-08.425167.parquet'
- config_name: results
data_files:
- split: 2023_09_04T02_45_47.354690
path:
- results_2023-09-04T02:45:47.354690.parquet
- split: 2023_10_17T07_54_08.425167
path:
- results_2023-10-17T07-54-08.425167.parquet
- split: latest
path:
- results_2023-10-17T07-54-08.425167.parquet
---
# Dataset Card for Evaluation run of porkorbeef/Llama-2-13b-public
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/porkorbeef/Llama-2-13b-public
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [porkorbeef/Llama-2-13b-public](https://huggingface.co/porkorbeef/Llama-2-13b-public) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_porkorbeef__Llama-2-13b-public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T07:54:08.425167](https://huggingface.co/datasets/open-llm-leaderboard/details_porkorbeef__Llama-2-13b-public/blob/main/results_2023-10-17T07-54-08.425167.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 5.76761744966443e-05,
"f1_stderr": 2.9264210748527048e-05,
"acc": 0.24191002367797948,
"acc_stderr": 0.007022563065489301
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 5.76761744966443e-05,
"f1_stderr": 2.9264210748527048e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.48382004735595896,
"acc_stderr": 0.014045126130978603
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## 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
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
distilled-one-sec-cv12-each-chunk-uniq/chunk_270 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 821448448.0
num_examples: 160064
download_size: 838621930
dataset_size: 821448448.0
---
# Dataset Card for "chunk_270"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DataHammer/Tokenizer-Test-Set | ---
license: apache-2.0
---
|
walabulu4/test1 | ---
license: unknown
dataset_info:
features:
- name: question
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4149644
num_examples: 1000
download_size: 1107439
dataset_size: 4149644
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sanjay920/goat-sharegpt | ---
dataset_info:
features:
- name: input
dtype: string
- name: answer
dtype: string
- name: conversations
dtype: string
splits:
- name: train
num_bytes: 430825158
num_examples: 1746300
download_size: 200576103
dataset_size: 430825158
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HiTZ/Multilingual-Opinion-Target-Extraction | ---
arxiv: 2210.12623
paperswithcode_id: aspect-based-sentiment-analysis
license: apache-2.0
configs:
- config_name: en
data_files:
- split: train
path: en.ote.train.json
- split: test
path: en.ote.test.json
- config_name: es
data_files:
- split: train
path: es.ote.train.json
- split: test
path: es.ote.test.json
- config_name: fr
data_files:
- split: train
path: fr.ote.train.json
- split: test
path: fr.ote.test.json
- config_name: ru
data_files:
- split: train
path: ru.ote.train.json
- split: test
path: ru.ote.test.json
- config_name: tr
data_files:
- split: train
path: tr.ote.train.json
task_categories:
- token-classification
language:
- en
- fr
- es
- ru
- tr
tags:
- opinion
- target
- absa
- aspect
- sentiment analysis
pretty_name: Multilingual Opinion Target Extraction
size_categories:
- 1K<n<10K
---
This repository contains the English '[SemEval-2014 Task 4: Aspect Based Sentiment Analysis](https://aclanthology.org/S14-2004/)'. translated with DeepL into Spanish, French, Russian, and Turkish. The **labels have been manually projected**. For more details, read this paper: [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://arxiv.org/abs/2210.12623).
**Intended Usage**: Since the datasets are parallel across languages, they are ideal for evaluating annotation projection algorithms, such as [T-Projection](https://arxiv.org/abs/2212.10548).
# Label Dictionary
```python
{
"O": 0,
"B-TARGET": 1,
"I-TARGET": 2
}
```
# Cication
If you use this data, please cite the following papers:
```bibtex
@inproceedings{garcia-ferrero-etal-2022-model,
title = "Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings",
author = "Garc{\'\i}a-Ferrero, Iker and
Agerri, Rodrigo and
Rigau, German",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.478",
doi = "10.18653/v1/2022.findings-emnlp.478",
pages = "6403--6416",
abstract = "Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to important differences in language use. Morespecifically, machine translation often generates a textual signal which isdifferent to what the models are exposed to when using gold standard data,which affects both the fine-tuning and evaluation processes. Our results alsoindicate that data-based cross-lingual transfer approaches remain a competitiveoption when high-capacity multilingual language models are not available.",
}
@inproceedings{pontiki-etal-2014-semeval,
title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
author = "Pontiki, Maria and
Galanis, Dimitris and
Pavlopoulos, John and
Papageorgiou, Harris and
Androutsopoulos, Ion and
Manandhar, Suresh",
editor = "Nakov, Preslav and
Zesch, Torsten",
booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
month = aug,
year = "2014",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S14-2004",
doi = "10.3115/v1/S14-2004",
pages = "27--35",
}
``` |
datasets-examples/doc-yaml-3 | ---
configs:
- config_name: default
data_files:
- split: train
path: "data/*.csv"
- split: test
path: "holdout/*.csv"
size_categories:
- n<1K
---
# [doc] manual configuration 3
This dataset contains two csv files in the data/ directory and one csv file in the holdout/ directory, and a YAML field `configs` that specifies the data files and splits, using glob expressions.
|
clement-bonnet/test | ---
dataset_info:
features:
- name: key
dtype: string
- name: poses
struct:
- name: bodies
struct:
- name: candidate
sequence:
sequence: float64
- name: subset
sequence:
sequence: float64
- name: faces
sequence:
sequence:
sequence: float64
- name: feet
sequence:
sequence:
sequence: float64
- name: hands
sequence:
sequence:
sequence: float64
- name: caption_success
dtype: bool
- name: size
sequence: int64
- name: caption
dtype: string
- name: text_features
sequence: float64
- name: face_bbox
sequence:
sequence: float64
splits:
- name: train
num_bytes: 16077183613
num_examples: 1520000
download_size: 3112491681
dataset_size: 16077183613
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tfshaman/gsm8k_sympy_temp | ---
dataset_info:
features:
- name: gsm8k_id
dtype: int64
- name: input
dtype: string
- name: output
dtype: string
- name: code
dtype: string
- name: answer
dtype: string
- name: question
dtype: string
- name: code_output
dtype: float64
- name: answer_value
dtype: float64
splits:
- name: train
num_bytes: 22246031
num_examples: 5907
download_size: 8407461
dataset_size: 22246031
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gsm8k_sympy_temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/lmind_hotpot_train8000_eval7405_v1_qa | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: train_ic_qa
path: data/train_ic_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: eval_ic_qa
path: data/eval_ic_qa-*
- split: all_docs
path: data/all_docs-*
- split: all_docs_eval
path: data/all_docs_eval-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
splits:
- name: train_qa
num_bytes: 1380987
num_examples: 8000
- name: train_recite_qa
num_bytes: 8547861
num_examples: 8000
- name: train_ic_qa
num_bytes: 8539861
num_examples: 8000
- name: eval_qa
num_bytes: 1201450
num_examples: 7405
- name: eval_recite_qa
num_bytes: 7941487
num_examples: 7405
- name: eval_ic_qa
num_bytes: 7934082
num_examples: 7405
- name: all_docs
num_bytes: 12508009
num_examples: 26854
- name: all_docs_eval
num_bytes: 12506219
num_examples: 26854
- name: train
num_bytes: 1380987
num_examples: 8000
- name: validation
num_bytes: 1201450
num_examples: 7405
download_size: 0
dataset_size: 63142393
---
# Dataset Card for "lmind_hotpot_train8000_eval7405_v1_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/daikokuten_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of daikokuten/大黒天/大黑天 (Fate/Grand Order)
This is the dataset of daikokuten/大黒天/大黑天 (Fate/Grand Order), containing 60 images and their tags.
The core tags of this character are `animal_ears, mouse_ears, mouse_girl, dark_skin, dark-skinned_female, short_hair, mouse_tail, red_eyes, tail, white_hair, maid_headdress`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 60 | 54.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/daikokuten_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 60 | 51.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/daikokuten_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 116 | 91.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/daikokuten_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/daikokuten_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | long_sleeves, smile, white_apron, 1girl, looking_at_viewer, maid_apron, open_mouth, solo, simple_background, blush_stickers, full_body, grey_dress, red_ribbon, neck_ribbon |
| 1 | 11 |  |  |  |  |  | 1girl, dress, white_apron, looking_at_viewer, nurse_cap, solo, white_background, hairclip, maid, long_sleeves, simple_background, smile, armband, blush_stickers, orange_eyes, twintails, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | long_sleeves | smile | white_apron | 1girl | looking_at_viewer | maid_apron | open_mouth | solo | simple_background | blush_stickers | full_body | grey_dress | red_ribbon | neck_ribbon | dress | nurse_cap | white_background | hairclip | maid | armband | orange_eyes | twintails | upper_body |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:--------------|:--------|:--------------------|:-------------|:-------------|:-------|:--------------------|:-----------------|:------------|:-------------|:-------------|:--------------|:--------|:------------|:-------------------|:-----------|:-------|:----------|:--------------|:------------|:-------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | X | X | | | X | X | X | | | | | X | X | X | X | X | X | X | X | X |
|
naorm/malware-text-db | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 907829
num_examples: 38
download_size: 500326
dataset_size: 907829
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xxl_mode_CM_D_PNP_GENERIC_Q_rices_ns_5046 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
sequence: string
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text
num_bytes: 57177169
num_examples: 5046
download_size: 10087126
dataset_size: 57177169
---
# Dataset Card for "OK-VQA_test_google_flan_t5_xxl_mode_CM_D_PNP_GENERIC_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/Open_Platypus_standardized_cluster_13 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 14829681
num_examples: 1635
download_size: 3991724
dataset_size: 14829681
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Open_Platypus_standardized_cluster_13"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 43534
num_examples: 100
download_size: 15782
dataset_size: 43534
---
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CausalLM/Refined-Anime-Text | ---
license: wtfpl
language:
- en
- zh
tags:
- synthetic
---
**Sorry, it's no longer available on Hugging Face. Please reach out to those who have already downloaded it. If you have a copy, please refrain from re-uploading it to Hugging Face. The people here don't deserve it. See also: https://twitter.com/RealJosephus/status/1779913520529707387**
# Refined Anime Text for Continual Pre-training of Language Models
This is a subset of our novel synthetic dataset of anime-themed text, containing over 1M entries, ~440M GPT-4/3.5 tokens. This dataset has never been publicly released before. We are releasing this subset due to the community's interest in anime culture, which is underrepresented in general-purpose datasets, and the low quality of raw text due to the prevalence of internet slang and irrelevant content, making it difficult to clean.
This dataset is intended for research on data governance of internet subcultures in large language models and to explore challenging LLM continual pre-training problems such as knowledge distillation on specific topics and continual learning of unseen knowledge.
The data was created by taking web-scraped text data (wikipedia excluded in this subset), passing the full web page text through a large language model (GPT-4-32k/GPT-3.5-16K, switching dynamically based on the difficulty) that supports long context windows, and synthesizing a refined version.
The dataset contains text in English and Chinese.
Thank you to [milashkaarshif/MoeGirlPedia_wikitext_raw_archive](https://huggingface.co/datasets/milashkaarshif/MoeGirlPedia_wikitext_raw_archive) and [RyokoAI/Fandom23K](https://huggingface.co/datasets/RyokoAI/Fandom23K) for the inspiration. All the data is obtained from publicly available text crawled from the internet, following the rules specified in robots.txt. The data was compiled in February 2024.
Subsets for other topics will be released in the future, so please stay tuned.
# 用于语言模型的持续预训练的高质量动漫主题文本数据
这是一份包含超过一百万条、约4400万个 GPT-4/3.5 token的、全新合成的文本数据集的动漫主题子集。该数据集此前从未公开发布过。由于社区对动漫文化的浓厚兴趣，且考虑到通识数据集中此类题材的代表性不足，以及原始文本中网络俚语和无关内容的泛滥而导致的低质量、难以清理的问题，我们决定发布这份子集供进一步研究。
这份数据集旨在用于研究大型语言模型中网络亚文化的数据治理，并探索具有挑战性的 LLM 持续预训练问题，例如特定主题的知识蒸馏以及对未见知识的持续学习。
该数据是通过以下方式创建的：获取网络爬取的文本数据（此子集中不包含维基百科内容），将完整的网页文本通过支持长文本窗口的大型语言模型（GPT-4-32k/GPT-3.5-16K，根据难度动态切换），并合成一个精炼版本。
数据集包含英文和中文文本。
感谢 [milashkaarshif/MoeGirlPedia_wikitext_raw_archive](https://huggingface.co/datasets/milashkaarshif/MoeGirlPedia_wikitext_raw_archive) 和 [RyokoAI/Fandom23K](https://huggingface.co/datasets/RyokoAI/Fandom23K) 提供的灵感。所有数据均从互联网上公开可用的文本中爬取，遵循 robots.txt 中规定的规则。数据于 2024 年 2 月编译。
其他主题的子集将在未来发布，敬请期待。 |
rufimelo/PortugueseLegalSentences-v3 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- pt
license:
- apache-2.0
multilinguality:
- monolingual
source_datasets:
- original
---
# Portuguese Legal Sentences
Collection of Legal Sentences from the Portuguese Supreme Court of Justice
The goal of this dataset was to be used for MLM and TSDAE
Extended version of rufimelo/PortugueseLegalSentences-v1
400000/50000/50000
### Contributions
[@rufimelo99](https://github.com/rufimelo99)
|
MU-NLPC/Calc-ape210k | ---
license: mit
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_chinese
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
splits:
- name: test
num_bytes: 1153807
num_examples: 1785
- name: train
num_bytes: 111628273
num_examples: 195179
- name: validation
num_bytes: 1169676
num_examples: 1783
download_size: 50706818
dataset_size: 113951756
- config_name: original-splits
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_chinese
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
splits:
- name: test
num_bytes: 2784396
num_examples: 4867
- name: train
num_bytes: 111628273
num_examples: 195179
- name: validation
num_bytes: 2789481
num_examples: 4867
download_size: 52107586
dataset_size: 117202150
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- config_name: original-splits
data_files:
- split: test
path: original-splits/test-*
- split: train
path: original-splits/train-*
- split: validation
path: original-splits/validation-*
---
# Dataset Card for Calc-ape210k
## Summary
This dataset is an instance of Ape210K dataset, converted to a simple HTML-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags:
- gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case)
- output: An output of the external tool
- result: The final answer to the mathematical problem (a number)
## Supported Tasks
The dataset is intended for training Chain-of-Thought reasoning **models able to use external tools** to enhance the factuality of their responses.
This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator.
## Construction Process
First, we translated the questions into English using Google Translate. Next, we parsed the equations and the results. We linearized
the equations into a sequence of elementary steps and evaluated them using a sympy-based calculator. We numerically compare the output
with the result in the data and remove all examples where they do not match (less than 3% loss in each split). Finally, we save the
chain of steps in the HTML-like language in the `chain` column. We keep the original columns in the dataset for convenience. We also perform
in-dataset and cross-dataset data-leak detection within [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
Specifically for Ape210k, we removed parts of the validation and test split, with around 1700 remaining in each.
You can read more information about this process in our [Calc-X paper](https://arxiv.org/abs/2305.15017).
## Data splits
The default config contains filtered splits with data leaks removed.
You can load it using:
```python
datasets.load_dataset("MU-NLPC/calc-ape210k")
```
In the `original-splits` config, the data splits are unfiltered and correspond to the original Ape210K dataset. See [ape210k dataset github](https://github.com/Chenny0808/ape210k) and [the paper](https://arxiv.org/abs/2009.11506) for more info.
You can load it using:
```python
datasets.load_dataset("MU-NLPC/calc-ape210k", "original-splits")
```
## Attributes
- **id** - id of the example
- **question** - the description of the math problem. Automatically translated from the `question_chinese` column into English using Google Translate
- **question_chinese** - the original description of the math problem in Chinese
- **chain** - linearized `equation`, sequence of arithmetic steps in HTML-like language that can be evaluated using our sympy-based calculator
- **result** - result as a string (can be an integer, float, or a fraction)
- **result_float** - result, converted to a float
- **equation** - a nested expression that evaluates to the correct answer
Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
## Related work
This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers.
- [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers
- [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF
- [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017)
- [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x)
Here are links to the original dataset:
- [**original Ape210k dataset and repo**](https://github.com/Chenny0808/ape210k)
- [**original Ape210k paper**](https://arxiv.org/abs/2009.11506)
## Licence
MIT, consistently with the original dataset.
## Cite
If you use this version of the dataset in research, please cite the [original Ape210k paper](https://arxiv.org/abs/2009.11506), and the [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows:
```bibtex
@inproceedings{kadlcik-etal-2023-soft,
title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
month = dec,
year = "2023",
address = "Singapore, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2305.15017",
}
``` |
TheGreatUnknown/LLML | ---
language:
- en
license: apache-2.0
---
This is a very rough draft of the advanced fuzzy logic engine AfterThought. It should be conceptually applied to all responses and then utilized to refine the system further. |
huggingartists/pop-smoke | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/pop-smoke"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [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/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.512514 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/41eeb550a79204cee6bdee9acdb584a2.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/pop-smoke">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Pop Smoke</div>
<a href="https://genius.com/artists/pop-smoke">
<div style="text-align: center; font-size: 14px;">@pop-smoke</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/pop-smoke).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/pop-smoke")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|TRAIN_0.512514| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/pop-smoke")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
|
somosnlp/spanish_medica_llm | ---
language:
- es
- en
license: apache-2.0
dataset_info:
features:
- name: raw_text
dtype: string
- name: topic
dtype: string
- name: speciallity
dtype: string
- name: raw_text_type
dtype: string
- name: topic_type
dtype: string
- name: source
dtype: string
- name: country
dtype: string
- name: document_id
dtype: string
splits:
- name: train
num_bytes: 190710909
num_examples: 2136490
download_size: 48472707
dataset_size: 190710909
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Dataset Spanish Medical
<!-- Provide a quick summary of the dataset. -->
Este dataset agrupa y organiza varios dataset presentes en hugginface (p.ej: PlanTL-GOB-ES/cantemist-ner, PlanTL-GOB-ES/pharmaconer)
y otros recursos públicos creados por investigadores con distintos formatos (p.ej.; MedLexSp )
para permitir ser fuente de conocimiento de grandes modelos de lenguaje en idioma español para el dominio médico.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Funded by:** [Dionis López Ramos](), [Alvaro Garcia Barragan](https://huggingface.co/Alvaro8gb), [Dylan Montoya](https://huggingface.co/dylanmontoya22), [Daniel](https://huggingface.co/Danielbrdz)
- **Language(s) (NLP):** Spanish
- **License:** Apache License 2.0
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset.
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
-->
## Uses
Se sugiere el uso de este dataset para lograr el autojuste y prentrenamiento de LLM para el dominio médico con información en idioma español.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
Los creadores del dataset no se hacen responsable de resultados nocivos que puedan generar los modelos al ser entrenados con esta información.
Se sugiere un proceso de evaluación riguroso con especialistas de los resultados generados por modelos de LLM entrenados.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
Por cada entrada o documento en la fuente de información organizarla en un dataset de hugginface de la siguiente forma:
- **question (raw_text)**: Texto asociado al documento, pregunta, caso clínico u otro tipo de información.
- **answer (topic)**: (Texto asociado al tratamiento médico (healthcare_treatment), diagnóstico (healthcare_diagnosis),
tópico de salud (topic), respuesta de una pregunta (answer), other, o estar vacío p.ej en el texto abierto)
- **speciality**: (Especialidad médica a la que se relaciona el raw_text p.ej: cardiología, cirugía, otros)
- **raw_text_type**: (Puede ser clinic_case, open_text, question o vacio)
- **topic_type**: (Puede ser medical_topic, medical_diagnostic,answer,natural_medicine_topic, other, o vacio)
- **source**: Identificador de la fuente asociada al documento que aparece en el README y descripción del dataset.
- **country**: Identificador del país de procedencia de la fuente (p.ej.; ch, es) usando el estándar ISO 3166-1 alfa-2 (Códigos de país de dos letras.).
- **document_id**: Identificador del documento en el dataset de procedencia, este valor puede estar vacio en caso que no se conozca
<!-- - **idioma**: (Variedad geográfica) código ISO del idioma -->
<!--- **registro** (Variedad funcional): Siempre es `medio`. -->
<!-- - **periodo** (Variedad histórica): Siempre es `actual`. -->
<!-- - **dominio**: salud (clínico, biomédico, farmacia). -->
<!-- - **tarea**: `pregunta` | `resumen` | `open_text` | `clinic_case`. -->
<!-- - **país_origen**: País de origen de los datos. -->
Al inicio de este proceso de construcción se debe actualizar en la tabla de la sección [Source Data](#source_data) la
descripción de la fuente de información con los siguientes datos:
- **Id**: Este será un número para que la fuente de información pueda ser referenciada en cada entrada del conjunto de datos.
- **Nombre**: Nombre de la fuente de donde procede.
- **Tokens**: Cantidad de tokens que contiene.
- **Memoria**: Tamaño en memoria del dataset generado para hugginface
- **Licencia**: En este caso si es solo para investigación o si posee otra licencia como MIT,
Apache 2 u otras
- **Dirección**: URL de donde se puede descargar o consultar la información.
- **País**: País de procedencia de la información.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
| Id | Nombre | Tokens | Memoria | Licencia | Dirección | País |
| --- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
| 1 | Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology | 349287 | 9157 kB | [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) | https://huggingface.co/datasets/bigbio/cantemist/viewer/cantemist_bigbio_kb | es |
| 2 | MedlinePlus Spanish (National Library of Medicine, NLM) | 7757337 | 35 MB | | https://medlineplus.gov/spanish/ | es |
| 3 | PharmaCoNER | 275955 | 2 MB | [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) | https://huggingface.co/datasets/PlanTL-GOB-ES/pharmaconer | es |
| 4 | Spanish Biomedical Crawled Corpus | 1973048 | 264 MB | cc-by-4.0 | https://zenodo.org/records/5513237 | es |
| 5 | CARES | 322353 | 1828 kB | Afl-3.0 | https://huggingface.co/datasets/chizhikchi/CARES | es |
| 6 | MEDDOCAN | 364462 | 1639 kB | cc-by-4.0 | https://huggingface.co/datasets/bigbio/meddocan | es |
| 7 | Alvaro8gb/enfermedades-wiki-marzo-2024 | 1424685 | 9073 kB | [MIT](https://choosealicense.com/licenses/mit/) | https://huggingface.co/datasets/Alvaro8gb/enfermedades-wiki-marzo-2024 | es |
| 8 | BioMistral/BioInstructQA(**spanish**) | 1072476 | 5963 kB | [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) | https://huggingface.co/datasets/BioMistral/BioInstructQA | ca |
| 9 | DisTEMIST | 550203 | 2754 kB | cc-by-4.0 | https://huggingface.co/datasets/bigbio/distemist | es |
| 10 | The Chilean Waiting List Corpus | 678934 | 3116 kB | cc-by-4.0 | https://zenodo.org/records/5518225 or https://huggingface.co/plncmm | chl |
| 11 | BARR2 | 1732432 | 8472 kB | cc-by-4.0 | https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2 | es |
| 12 | SPACC | 551849 | 2711 kB | cc-by-4.0 | https://zenodo.org/records/2560316 | es |
| 13 | MedLexSp | 608374 | 21 MByte | MedLexSp is distributed freely for research or educational purposes. You need to sign an agreement with the authors for other purposes. | https://digital.csic.es/handle/10261/270429 | es |
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
- En el caso de [BioMistral/BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA) se utilizó la información en idioma español. Para más información consultar el artículo [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://arxiv.org/pdf/2402.10373.pdf?trk=public_post_comment-text).
- Para [Cantemist](https://huggingface.co/datasets/bigbio/cantemist/viewer/cantemist_bigbio_kb) se hizo una búsqueda del código asociado a la patología y se estableció como tópico.
- En [CARES](https://huggingface.co/datasets/chizhikchi/CARES) se busco el tipo asociado en la tabla de códigos establecido.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Se sugiere tener en cuenta el alcance de las licencia de cada una de las fuentes (e.d., revisar el campo source y Licencia de la tabla anterior).
En el caso de necesitar filtrar por fuente de datos u otro criterio usted puede auxiliarse de las propiedades de la estructura de datos `Dataset` del marco de trabajo
Hugginface. En el siguiente ejemplo de código se obtienen del conjunto de datos las entradas que tienen un tipo de tópico sobre diagnóstico medico o un tópico médico:
`
spanishMedicaLllmDataset = load_dataset(SPANISH_MEDICA_LLM_DATASET, split="train")
spanishMedicaLllmDataset = spanishMedicaLllmDataset.filter(lambda example: example["topic_type"] in ['medical_diagnostic' | 'medical_topic'])
`
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Se sugiere tener en cuenta las filas donde el tipo del topic (e.d., campo topic_type) tenga valores `medical_topic`, `medical_diagnostic`, `answer`, `natural_medicine_topic`
para un autojuste del modelo y en el caso de que el texto se `open_text` un pre-entranmiento inicial del modelo LLM.
## Dataset Card Contact
For any doubt or suggestion contact to: PhD Dionis López (inoid2007@gmail.com) |
CATIE-AQ/orange_sum_fr_prompt_title_generation_from_an_article | ---
language:
- fr
license: cc-by-sa-4.0
size_categories:
- 100K<n<1M
task_categories:
- text-generation
tags:
- title generation
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- orange_sum
---
# orange_sum_fr_prompt_title_generation_from_an_article
## Summary
**orange_sum_fr_prompt_title_generation_from_an_article** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **639,521** rows that can be used for a title generation task.
The original data (without prompts) comes from the dataset [orange_sum](https://huggingface.co/datasets/orange_sum) by Eddine et al.
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
19 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
'"'+document+'"\n Générer un titre pour cet article :',
'"'+document+'"\n Génère un titre pour cet article :',
'"'+document+'"\n Générez un titre pour cet article :',
'"'+document+'"\n Rédiger un titre pour cet article :',
'"'+document+'"\n Rédige un titre pour cet article :',
'"'+document+'"\n Rédigez un titre pour cet article :',
'"'+document+'"\n Ecrire un titre pour cet article :',
'"'+document+'"\n Ecris un titre pour cet article :',
'"'+document+'"\n Ecrivez un titre pour cet article :',
"Générer un titre pour l'article suivant : "+document,
"Génère un titre pour l'article suivant : "+document,
"Générez un titre pour l'article suivant : "+document,
"Rédiger un titre pour l'article suivant : "+document,
"Rédige un titre pour l'article suivant : "+document,
"Rédigez un titre pour l'article suivant : "+document,
"Ecrire un titre pour l'article suivant : "+document,
"Ecris un titre pour l'article suivant : "+document,
"Ecrivez un titre pour l'article suivant : "+document,
'"'+document+'"\n Titre :\n '
```
### Features used in the prompts
In the prompt list above, `document` and `targets` have been constructed from:
```
orange_sum = load_dataset('orange_sum','title')
document = orange_sum['train'][i]['text']
targets = orange_sum['train'][i]['summary']
```
# Splits
- `train` with 582,521 samples
- `valid` with 28,500 samples
- `test` with 28,500 samples
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/orange_sum_fr_prompt_title_generation_from_an_article")
```
# Citation
## Original data
> @article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis},
journal={arXiv preprint arXiv:2010.12321},
year={2020}
}
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
CC-BY-SA-4.0 |
knowrohit07/know_cot | ---
license: other
---
|
Doub7e/SD-CLIP-alignment-composition-T5 | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
- name: clip_pred
dtype: string
- name: T5_last_hidden_states
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 431432487.0
num_examples: 900
download_size: 419405907
dataset_size: 431432487.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "SD-CLIP-alignment-composition-T5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ilikeit/theftcasesbot | ---
license: apache-2.0
---
|
phyloforfun/HLT_MICH_Angiospermae_SLTPvA_v1-0_large__OCR-C35-L35-E100-R01 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 169975516
num_examples: 100003
download_size: 32919297
dataset_size: 169975516
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yuvalkirstain/PickaPic-images | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: created_at
dtype: timestamp[ns]
- name: image_uid
dtype: string
- name: user_id
dtype: int64
- name: prompt
dtype: string
- name: negative_prompt
dtype: string
- name: seed
dtype: int64
- name: gs
dtype: float64
- name: steps
dtype: int64
- name: idx
dtype: int64
- name: num_generated
dtype: int64
- name: scheduler_cls
dtype: string
- name: model_id
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 70620168
num_examples: 109356
download_size: 12059565
dataset_size: 70620168
---
# Dataset Card for "PickaPic-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CodecSR/fsd50k_16k_synth | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: id
dtype: string
splits:
- name: original
num_bytes: 13195529214.0
num_examples: 14400
- name: academicodec_hifi_16k_320d_large_uni
num_bytes: 4394953932.0
num_examples: 14400
- name: academicodec_hifi_24k_320d
num_bytes: 4394953932.0
num_examples: 14400
- name: audiodec_24k_300d
num_bytes: 4403255492.0
num_examples: 14400
- name: audiodec_48k_300d_uni
num_bytes: 4403255492.0
num_examples: 14400
- name: dac_16k
num_bytes: 4399077054.0
num_examples: 14400
- name: dac_24k
num_bytes: 4399077043.6
num_examples: 14400
- name: dac_44k
num_bytes: 4399077054.0
num_examples: 14400
- name: encodec_24k_12bps
num_bytes: 4399077054.0
num_examples: 14400
- name: encodec_24k_1_5bps
num_bytes: 4399077050.0
num_examples: 14400
- name: encodec_24k_24bps
num_bytes: 4399077057.0
num_examples: 14400
- name: encodec_24k_3bps
num_bytes: 4399077050.0
num_examples: 14400
- name: encodec_24k_6bps
num_bytes: 4399077050.0
num_examples: 14400
- name: facodec_16k
num_bytes: 4397686092.0
num_examples: 14400
- name: funcodec_en_libritts_16k_nq32ds320
num_bytes: 4399077057.0
num_examples: 14400
- name: funcodec_en_libritts_16k_nq32ds640
num_bytes: 4399077054.0
num_examples: 14400
- name: funcodec_zh_en_16k_nq32ds320
num_bytes: 4399077045.6
num_examples: 14400
- name: funcodec_zh_en_16k_nq32ds640
num_bytes: 4399077045.6
num_examples: 14400
- name: language_codec_chinese_24k_nq8_12kbps
num_bytes: 4403185607.6
num_examples: 14400
- name: language_codec_paper_24k_nq8_12kbps
num_bytes: 4403185607.6
num_examples: 14400
- name: speech_tokenizer_16k
num_bytes: 4403185607.6
num_examples: 14400
download_size: 93168736444
dataset_size: 101188115591.60004
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
- split: academicodec_hifi_24k_320d
path: data/academicodec_hifi_24k_320d-*
- split: audiodec_24k_300d
path: data/audiodec_24k_300d-*
- split: audiodec_48k_300d_uni
path: data/audiodec_48k_300d_uni-*
- split: dac_16k
path: data/dac_16k-*
- split: dac_24k
path: data/dac_24k-*
- split: dac_44k
path: data/dac_44k-*
- split: encodec_24k_12bps
path: data/encodec_24k_12bps-*
- split: encodec_24k_1_5bps
path: data/encodec_24k_1_5bps-*
- split: encodec_24k_24bps
path: data/encodec_24k_24bps-*
- split: encodec_24k_3bps
path: data/encodec_24k_3bps-*
- split: encodec_24k_6bps
path: data/encodec_24k_6bps-*
- split: facodec_16k
path: data/facodec_16k-*
- split: funcodec_en_libritts_16k_nq32ds320
path: data/funcodec_en_libritts_16k_nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds640
path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: language_codec_chinese_24k_nq8_12kbps
path: data/language_codec_chinese_24k_nq8_12kbps-*
- split: language_codec_paper_24k_nq8_12kbps
path: data/language_codec_paper_24k_nq8_12kbps-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
---
|
w95/databricks-dolly-15k-az | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
language:
- az
size_categories:
- 1K<n<10K
---
This dataset is a machine-translated version of [databricks-dolly-15k.jsonl](https://huggingface.co/datasets/databricks/databricks-dolly-15k) into Azerbaijani. Dataset size is 8k.
-----
# Summary
`databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several
of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification,
closed QA, generation, information extraction, open QA, and summarization.
This dataset can be used for any purpose, whether academic or commercial, under the terms of the
[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: English
Version: 1.0 |
facebook/babi_qa | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: babi-1
pretty_name: BabiQa
configs:
- en-10k-qa1
- en-10k-qa10
- en-10k-qa11
- en-10k-qa12
- en-10k-qa13
- en-10k-qa14
- en-10k-qa15
- en-10k-qa16
- en-10k-qa17
- en-10k-qa18
- en-10k-qa19
- en-10k-qa2
- en-10k-qa20
- en-10k-qa3
- en-10k-qa4
- en-10k-qa5
- en-10k-qa6
- en-10k-qa7
- en-10k-qa8
- en-10k-qa9
- en-qa1
- en-qa10
- en-qa11
- en-qa12
- en-qa13
- en-qa14
- en-qa15
- en-qa16
- en-qa17
- en-qa18
- en-qa19
- en-qa2
- en-qa20
- en-qa3
- en-qa4
- en-qa5
- en-qa6
- en-qa7
- en-qa8
- en-qa9
- en-valid-10k-qa1
- en-valid-10k-qa10
- en-valid-10k-qa11
- en-valid-10k-qa12
- en-valid-10k-qa13
- en-valid-10k-qa14
- en-valid-10k-qa15
- en-valid-10k-qa16
- en-valid-10k-qa17
- en-valid-10k-qa18
- en-valid-10k-qa19
- en-valid-10k-qa2
- en-valid-10k-qa20
- en-valid-10k-qa3
- en-valid-10k-qa4
- en-valid-10k-qa5
- en-valid-10k-qa6
- en-valid-10k-qa7
- en-valid-10k-qa8
- en-valid-10k-qa9
- en-valid-qa1
- en-valid-qa10
- en-valid-qa11
- en-valid-qa12
- en-valid-qa13
- en-valid-qa14
- en-valid-qa15
- en-valid-qa16
- en-valid-qa17
- en-valid-qa18
- en-valid-qa19
- en-valid-qa2
- en-valid-qa20
- en-valid-qa3
- en-valid-qa4
- en-valid-qa5
- en-valid-qa6
- en-valid-qa7
- en-valid-qa8
- en-valid-qa9
- hn-10k-qa1
- hn-10k-qa10
- hn-10k-qa11
- hn-10k-qa12
- hn-10k-qa13
- hn-10k-qa14
- hn-10k-qa15
- hn-10k-qa16
- hn-10k-qa17
- hn-10k-qa18
- hn-10k-qa19
- hn-10k-qa2
- hn-10k-qa20
- hn-10k-qa3
- hn-10k-qa4
- hn-10k-qa5
- hn-10k-qa6
- hn-10k-qa7
- hn-10k-qa8
- hn-10k-qa9
- hn-qa1
- hn-qa10
- hn-qa11
- hn-qa12
- hn-qa13
- hn-qa14
- hn-qa15
- hn-qa16
- hn-qa17
- hn-qa18
- hn-qa19
- hn-qa2
- hn-qa20
- hn-qa3
- hn-qa4
- hn-qa5
- hn-qa6
- hn-qa7
- hn-qa8
- hn-qa9
- shuffled-10k-qa1
- shuffled-10k-qa10
- shuffled-10k-qa11
- shuffled-10k-qa12
- shuffled-10k-qa13
- shuffled-10k-qa14
- shuffled-10k-qa15
- shuffled-10k-qa16
- shuffled-10k-qa17
- shuffled-10k-qa18
- shuffled-10k-qa19
- shuffled-10k-qa2
- shuffled-10k-qa20
- shuffled-10k-qa3
- shuffled-10k-qa4
- shuffled-10k-qa5
- shuffled-10k-qa6
- shuffled-10k-qa7
- shuffled-10k-qa8
- shuffled-10k-qa9
- shuffled-qa1
- shuffled-qa10
- shuffled-qa11
- shuffled-qa12
- shuffled-qa13
- shuffled-qa14
- shuffled-qa15
- shuffled-qa16
- shuffled-qa17
- shuffled-qa18
- shuffled-qa19
- shuffled-qa2
- shuffled-qa20
- shuffled-qa3
- shuffled-qa4
- shuffled-qa5
- shuffled-qa6
- shuffled-qa7
- shuffled-qa8
- shuffled-qa9
tags:
- chained-qa
dataset_info:
- config_name: en-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 165386
num_examples: 200
- name: test
num_bytes: 165517
num_examples: 200
download_size: 15719851
dataset_size: 330903
- config_name: en-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 302888
num_examples: 200
- name: test
num_bytes: 306631
num_examples: 200
download_size: 15719851
dataset_size: 609519
- config_name: en-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 887756
num_examples: 200
- name: test
num_bytes: 883187
num_examples: 200
download_size: 15719851
dataset_size: 1770943
- config_name: en-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 205510
num_examples: 1000
- name: test
num_bytes: 205434
num_examples: 1000
download_size: 15719851
dataset_size: 410944
- config_name: en-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 337349
num_examples: 200
- name: test
num_bytes: 350457
num_examples: 200
download_size: 15719851
dataset_size: 687806
- config_name: en-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 173053
num_examples: 200
- name: test
num_bytes: 172249
num_examples: 200
download_size: 15719851
dataset_size: 345302
- config_name: en-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 224778
num_examples: 200
- name: test
num_bytes: 215512
num_examples: 200
download_size: 15719851
dataset_size: 440290
- config_name: en-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 212517
num_examples: 200
- name: test
num_bytes: 216244
num_examples: 200
download_size: 15719851
dataset_size: 428761
- config_name: en-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 168350
num_examples: 200
- name: test
num_bytes: 168248
num_examples: 200
download_size: 15719851
dataset_size: 336598
- config_name: en-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 170257
num_examples: 200
- name: test
num_bytes: 170672
num_examples: 200
download_size: 15719851
dataset_size: 340929
- config_name: en-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 178560
num_examples: 200
- name: test
num_bytes: 178840
num_examples: 200
download_size: 15719851
dataset_size: 357400
- config_name: en-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 185600
num_examples: 200
- name: test
num_bytes: 185529
num_examples: 200
download_size: 15719851
dataset_size: 371129
- config_name: en-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 190556
num_examples: 200
- name: test
num_bytes: 190484
num_examples: 200
download_size: 15719851
dataset_size: 381040
- config_name: en-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 234355
num_examples: 200
- name: test
num_bytes: 233204
num_examples: 200
download_size: 15719851
dataset_size: 467559
- config_name: en-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 163728
num_examples: 250
- name: test
num_bytes: 163809
num_examples: 250
download_size: 15719851
dataset_size: 327537
- config_name: en-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 456374
num_examples: 1000
- name: test
num_bytes: 456248
num_examples: 1000
download_size: 15719851
dataset_size: 912622
- config_name: en-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 103636
num_examples: 125
- name: test
num_bytes: 103618
num_examples: 125
download_size: 15719851
dataset_size: 207254
- config_name: en-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 162875
num_examples: 198
- name: test
num_bytes: 161266
num_examples: 199
download_size: 15719851
dataset_size: 324141
- config_name: en-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 404536
num_examples: 1000
- name: test
num_bytes: 404489
num_examples: 1000
download_size: 15719851
dataset_size: 809025
- config_name: en-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 115812
num_examples: 94
- name: test
num_bytes: 115863
num_examples: 93
download_size: 15719851
dataset_size: 231675
- config_name: hn-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 168605
num_examples: 200
- name: test
num_bytes: 168572
num_examples: 200
download_size: 15719851
dataset_size: 337177
- config_name: hn-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 296391
num_examples: 200
- name: test
num_bytes: 288429
num_examples: 200
download_size: 15719851
dataset_size: 584820
- config_name: hn-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 842184
num_examples: 167
- name: test
num_bytes: 808460
num_examples: 167
download_size: 15719851
dataset_size: 1650644
- config_name: hn-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 231303
num_examples: 1000
- name: test
num_bytes: 231230
num_examples: 1000
download_size: 15719851
dataset_size: 462533
- config_name: hn-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 320859
num_examples: 200
- name: test
num_bytes: 315396
num_examples: 200
download_size: 15719851
dataset_size: 636255
- config_name: hn-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 170796
num_examples: 200
- name: test
num_bytes: 171360
num_examples: 200
download_size: 15719851
dataset_size: 342156
- config_name: hn-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 206981
num_examples: 200
- name: test
num_bytes: 208080
num_examples: 200
download_size: 15719851
dataset_size: 415061
- config_name: hn-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 211584
num_examples: 200
- name: test
num_bytes: 222232
num_examples: 200
download_size: 15719851
dataset_size: 433816
- config_name: hn-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 187718
num_examples: 200
- name: test
num_bytes: 187341
num_examples: 200
download_size: 15719851
dataset_size: 375059
- config_name: hn-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 183583
num_examples: 200
- name: test
num_bytes: 182932
num_examples: 200
download_size: 15719851
dataset_size: 366515
- config_name: hn-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 179698
num_examples: 200
- name: test
num_bytes: 180461
num_examples: 200
download_size: 15719851
dataset_size: 360159
- config_name: hn-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 187731
num_examples: 200
- name: test
num_bytes: 187954
num_examples: 200
download_size: 15719851
dataset_size: 375685
- config_name: hn-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 191395
num_examples: 125
- name: test
num_bytes: 191747
num_examples: 125
download_size: 15719851
dataset_size: 383142
- config_name: hn-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 240659
num_examples: 200
- name: test
num_bytes: 240436
num_examples: 200
download_size: 15719851
dataset_size: 481095
- config_name: hn-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 170358
num_examples: 250
- name: test
num_bytes: 170259
num_examples: 250
download_size: 15719851
dataset_size: 340617
- config_name: hn-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 523093
num_examples: 1000
- name: test
num_bytes: 523032
num_examples: 1000
download_size: 15719851
dataset_size: 1046125
- config_name: hn-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 103878
num_examples: 125
- name: test
num_bytes: 104061
num_examples: 125
download_size: 15719851
dataset_size: 207939
- config_name: hn-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 173056
num_examples: 198
- name: test
num_bytes: 176824
num_examples: 198
download_size: 15719851
dataset_size: 349880
- config_name: hn-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 470225
num_examples: 1000
- name: test
num_bytes: 470479
num_examples: 1000
download_size: 15719851
dataset_size: 940704
- config_name: hn-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 115021
num_examples: 93
- name: test
num_bytes: 115088
num_examples: 94
download_size: 15719851
dataset_size: 230109
- config_name: en-10k-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1654288
num_examples: 2000
- name: test
num_bytes: 165517
num_examples: 200
download_size: 15719851
dataset_size: 1819805
- config_name: en-10k-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3062580
num_examples: 2000
- name: test
num_bytes: 306631
num_examples: 200
download_size: 15719851
dataset_size: 3369211
- config_name: en-10k-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 8921215
num_examples: 2000
- name: test
num_bytes: 883187
num_examples: 200
download_size: 15719851
dataset_size: 9804402
- config_name: en-10k-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2055105
num_examples: 10000
- name: test
num_bytes: 205434
num_examples: 1000
download_size: 15719851
dataset_size: 2260539
- config_name: en-10k-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3592157
num_examples: 2000
- name: test
num_bytes: 350457
num_examples: 200
download_size: 15719851
dataset_size: 3942614
- config_name: en-10k-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1726716
num_examples: 2000
- name: test
num_bytes: 172249
num_examples: 200
download_size: 15719851
dataset_size: 1898965
- config_name: en-10k-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2228087
num_examples: 2000
- name: test
num_bytes: 215512
num_examples: 200
download_size: 15719851
dataset_size: 2443599
- config_name: en-10k-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2141880
num_examples: 2000
- name: test
num_bytes: 216244
num_examples: 200
download_size: 15719851
dataset_size: 2358124
- config_name: en-10k-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1681213
num_examples: 2000
- name: test
num_bytes: 168248
num_examples: 200
download_size: 15719851
dataset_size: 1849461
- config_name: en-10k-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1707675
num_examples: 2000
- name: test
num_bytes: 170672
num_examples: 200
download_size: 15719851
dataset_size: 1878347
- config_name: en-10k-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1786179
num_examples: 2000
- name: test
num_bytes: 178840
num_examples: 200
download_size: 15719851
dataset_size: 1965019
- config_name: en-10k-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1854745
num_examples: 2000
- name: test
num_bytes: 185529
num_examples: 200
download_size: 15719851
dataset_size: 2040274
- config_name: en-10k-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1903149
num_examples: 2000
- name: test
num_bytes: 190484
num_examples: 200
download_size: 15719851
dataset_size: 2093633
- config_name: en-10k-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2321511
num_examples: 2000
- name: test
num_bytes: 233204
num_examples: 200
download_size: 15719851
dataset_size: 2554715
- config_name: en-10k-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1637398
num_examples: 2500
- name: test
num_bytes: 163809
num_examples: 250
download_size: 15719851
dataset_size: 1801207
- config_name: en-10k-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4562844
num_examples: 10000
- name: test
num_bytes: 456248
num_examples: 1000
download_size: 15719851
dataset_size: 5019092
- config_name: en-10k-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1034333
num_examples: 1250
- name: test
num_bytes: 103618
num_examples: 125
download_size: 15719851
dataset_size: 1137951
- config_name: en-10k-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1641650
num_examples: 1978
- name: test
num_bytes: 161266
num_examples: 199
download_size: 15719851
dataset_size: 1802916
- config_name: en-10k-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4045086
num_examples: 10000
- name: test
num_bytes: 404489
num_examples: 1000
download_size: 15719851
dataset_size: 4449575
- config_name: en-10k-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1157351
num_examples: 933
- name: test
num_bytes: 115863
num_examples: 93
download_size: 15719851
dataset_size: 1273214
- config_name: en-valid-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 148887
num_examples: 180
- name: test
num_bytes: 165517
num_examples: 200
- name: validation
num_bytes: 16539
num_examples: 20
download_size: 15719851
dataset_size: 330943
- config_name: en-valid-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 275106
num_examples: 180
- name: test
num_bytes: 306631
num_examples: 200
- name: validation
num_bytes: 27822
num_examples: 20
download_size: 15719851
dataset_size: 609559
- config_name: en-valid-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 794565
num_examples: 180
- name: test
num_bytes: 883187
num_examples: 200
- name: validation
num_bytes: 93231
num_examples: 20
download_size: 15719851
dataset_size: 1770983
- config_name: en-valid-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 184992
num_examples: 900
- name: test
num_bytes: 205434
num_examples: 1000
- name: validation
num_bytes: 20558
num_examples: 100
download_size: 15719851
dataset_size: 410984
- config_name: en-valid-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 305472
num_examples: 180
- name: test
num_bytes: 350457
num_examples: 200
- name: validation
num_bytes: 31917
num_examples: 20
download_size: 15719851
dataset_size: 687846
- config_name: en-valid-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 155845
num_examples: 180
- name: test
num_bytes: 172249
num_examples: 200
- name: validation
num_bytes: 17248
num_examples: 20
download_size: 15719851
dataset_size: 345342
- config_name: en-valid-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 203642
num_examples: 180
- name: test
num_bytes: 215512
num_examples: 200
- name: validation
num_bytes: 21176
num_examples: 20
download_size: 15719851
dataset_size: 440330
- config_name: en-valid-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 191599
num_examples: 180
- name: test
num_bytes: 216244
num_examples: 200
- name: validation
num_bytes: 20958
num_examples: 20
download_size: 15719851
dataset_size: 428801
- config_name: en-valid-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 151458
num_examples: 180
- name: test
num_bytes: 168248
num_examples: 200
- name: validation
num_bytes: 16932
num_examples: 20
download_size: 15719851
dataset_size: 336638
- config_name: en-valid-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 153240
num_examples: 180
- name: test
num_bytes: 170672
num_examples: 200
- name: validation
num_bytes: 17057
num_examples: 20
download_size: 15719851
dataset_size: 340969
- config_name: en-valid-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 160701
num_examples: 180
- name: test
num_bytes: 178840
num_examples: 200
- name: validation
num_bytes: 17899
num_examples: 20
download_size: 15719851
dataset_size: 357440
- config_name: en-valid-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 167031
num_examples: 180
- name: test
num_bytes: 185529
num_examples: 200
- name: validation
num_bytes: 18609
num_examples: 20
download_size: 15719851
dataset_size: 371169
- config_name: en-valid-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 171527
num_examples: 180
- name: test
num_bytes: 190484
num_examples: 200
- name: validation
num_bytes: 19069
num_examples: 20
download_size: 15719851
dataset_size: 381080
- config_name: en-valid-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 210650
num_examples: 180
- name: test
num_bytes: 233204
num_examples: 200
- name: validation
num_bytes: 23745
num_examples: 20
download_size: 15719851
dataset_size: 467599
- config_name: en-valid-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 147356
num_examples: 225
- name: test
num_bytes: 163809
num_examples: 250
- name: validation
num_bytes: 16412
num_examples: 25
download_size: 15719851
dataset_size: 327577
- config_name: en-valid-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 410711
num_examples: 900
- name: test
num_bytes: 456248
num_examples: 1000
- name: validation
num_bytes: 45703
num_examples: 100
download_size: 15719851
dataset_size: 912662
- config_name: en-valid-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 93596
num_examples: 113
- name: test
num_bytes: 103618
num_examples: 125
- name: validation
num_bytes: 10080
num_examples: 12
download_size: 15719851
dataset_size: 207294
- config_name: en-valid-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 147338
num_examples: 179
- name: test
num_bytes: 161266
num_examples: 199
- name: validation
num_bytes: 15577
num_examples: 19
download_size: 15719851
dataset_size: 324181
- config_name: en-valid-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 364090
num_examples: 900
- name: test
num_bytes: 404489
num_examples: 1000
- name: validation
num_bytes: 40486
num_examples: 100
download_size: 15719851
dataset_size: 809065
- config_name: en-valid-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 104706
num_examples: 85
- name: test
num_bytes: 115863
num_examples: 93
- name: validation
num_bytes: 11146
num_examples: 9
download_size: 15719851
dataset_size: 231715
- config_name: en-valid-10k-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1488751
num_examples: 1800
- name: test
num_bytes: 165517
num_examples: 200
- name: validation
num_bytes: 165577
num_examples: 200
download_size: 15719851
dataset_size: 1819845
- config_name: en-valid-10k-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2746462
num_examples: 1800
- name: test
num_bytes: 306631
num_examples: 200
- name: validation
num_bytes: 316158
num_examples: 200
download_size: 15719851
dataset_size: 3369251
- config_name: en-valid-10k-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 8021847
num_examples: 1800
- name: test
num_bytes: 883187
num_examples: 200
- name: validation
num_bytes: 899408
num_examples: 200
download_size: 15719851
dataset_size: 9804442
- config_name: en-valid-10k-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1849497
num_examples: 9000
- name: test
num_bytes: 205434
num_examples: 1000
- name: validation
num_bytes: 205648
num_examples: 1000
download_size: 15719851
dataset_size: 2260579
- config_name: en-valid-10k-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3234186
num_examples: 1800
- name: test
num_bytes: 350457
num_examples: 200
- name: validation
num_bytes: 358011
num_examples: 200
download_size: 15719851
dataset_size: 3942654
- config_name: en-valid-10k-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1553957
num_examples: 1800
- name: test
num_bytes: 172249
num_examples: 200
- name: validation
num_bytes: 172799
num_examples: 200
download_size: 15719851
dataset_size: 1899005
- config_name: en-valid-10k-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2003820
num_examples: 1800
- name: test
num_bytes: 215512
num_examples: 200
- name: validation
num_bytes: 224307
num_examples: 200
download_size: 15719851
dataset_size: 2443639
- config_name: en-valid-10k-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1926339
num_examples: 1800
- name: test
num_bytes: 216244
num_examples: 200
- name: validation
num_bytes: 215581
num_examples: 200
download_size: 15719851
dataset_size: 2358164
- config_name: en-valid-10k-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1512917
num_examples: 1800
- name: test
num_bytes: 168248
num_examples: 200
- name: validation
num_bytes: 168336
num_examples: 200
download_size: 15719851
dataset_size: 1849501
- config_name: en-valid-10k-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1536416
num_examples: 1800
- name: test
num_bytes: 170672
num_examples: 200
- name: validation
num_bytes: 171299
num_examples: 200
download_size: 15719851
dataset_size: 1878387
- config_name: en-valid-10k-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1607505
num_examples: 1800
- name: test
num_bytes: 178840
num_examples: 200
- name: validation
num_bytes: 178714
num_examples: 200
download_size: 15719851
dataset_size: 1965059
- config_name: en-valid-10k-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1669198
num_examples: 1800
- name: test
num_bytes: 185529
num_examples: 200
- name: validation
num_bytes: 185587
num_examples: 200
download_size: 15719851
dataset_size: 2040314
- config_name: en-valid-10k-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1712558
num_examples: 1800
- name: test
num_bytes: 190484
num_examples: 200
- name: validation
num_bytes: 190631
num_examples: 200
download_size: 15719851
dataset_size: 2093673
- config_name: en-valid-10k-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2091491
num_examples: 1800
- name: test
num_bytes: 233204
num_examples: 200
- name: validation
num_bytes: 230060
num_examples: 200
download_size: 15719851
dataset_size: 2554755
- config_name: en-valid-10k-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1473615
num_examples: 2250
- name: test
num_bytes: 163809
num_examples: 250
- name: validation
num_bytes: 163823
num_examples: 250
download_size: 15719851
dataset_size: 1801247
- config_name: en-valid-10k-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4106444
num_examples: 9000
- name: test
num_bytes: 456248
num_examples: 1000
- name: validation
num_bytes: 456440
num_examples: 1000
download_size: 15719851
dataset_size: 5019132
- config_name: en-valid-10k-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 930465
num_examples: 1125
- name: test
num_bytes: 103618
num_examples: 125
- name: validation
num_bytes: 103908
num_examples: 125
download_size: 15719851
dataset_size: 1137991
- config_name: en-valid-10k-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1477467
num_examples: 1781
- name: test
num_bytes: 161266
num_examples: 199
- name: validation
num_bytes: 164223
num_examples: 197
download_size: 15719851
dataset_size: 1802956
- config_name: en-valid-10k-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3640527
num_examples: 9000
- name: test
num_bytes: 404489
num_examples: 1000
- name: validation
num_bytes: 404599
num_examples: 1000
download_size: 15719851
dataset_size: 4449615
- config_name: en-valid-10k-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1041856
num_examples: 840
- name: test
num_bytes: 115863
num_examples: 93
- name: validation
num_bytes: 115535
num_examples: 93
download_size: 15719851
dataset_size: 1273254
- config_name: hn-10k-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1684003
num_examples: 2000
- name: test
num_bytes: 168572
num_examples: 200
download_size: 15719851
dataset_size: 1852575
- config_name: hn-10k-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2934642
num_examples: 2000
- name: test
num_bytes: 288429
num_examples: 200
download_size: 15719851
dataset_size: 3223071
- config_name: hn-10k-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 8440008
num_examples: 1667
- name: test
num_bytes: 808460
num_examples: 167
download_size: 15719851
dataset_size: 9248468
- config_name: hn-10k-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2312075
num_examples: 10000
- name: test
num_bytes: 231230
num_examples: 1000
download_size: 15719851
dataset_size: 2543305
- config_name: hn-10k-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3301271
num_examples: 2000
- name: test
num_bytes: 315396
num_examples: 200
download_size: 15719851
dataset_size: 3616667
- config_name: hn-10k-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1703863
num_examples: 2000
- name: test
num_bytes: 171360
num_examples: 200
download_size: 15719851
dataset_size: 1875223
- config_name: hn-10k-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2091460
num_examples: 2000
- name: test
num_bytes: 208080
num_examples: 200
download_size: 15719851
dataset_size: 2299540
- config_name: hn-10k-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2178277
num_examples: 2000
- name: test
num_bytes: 222232
num_examples: 200
download_size: 15719851
dataset_size: 2400509
- config_name: hn-10k-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1874753
num_examples: 2000
- name: test
num_bytes: 187341
num_examples: 200
download_size: 15719851
dataset_size: 2062094
- config_name: hn-10k-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1830698
num_examples: 2000
- name: test
num_bytes: 182932
num_examples: 200
download_size: 15719851
dataset_size: 2013630
- config_name: hn-10k-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1798057
num_examples: 2000
- name: test
num_bytes: 180461
num_examples: 200
download_size: 15719851
dataset_size: 1978518
- config_name: hn-10k-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1879776
num_examples: 2000
- name: test
num_bytes: 187954
num_examples: 200
download_size: 15719851
dataset_size: 2067730
- config_name: hn-10k-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1915482
num_examples: 1250
- name: test
num_bytes: 191747
num_examples: 125
download_size: 15719851
dataset_size: 2107229
- config_name: hn-10k-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2392212
num_examples: 2000
- name: test
num_bytes: 240436
num_examples: 200
download_size: 15719851
dataset_size: 2632648
- config_name: hn-10k-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1702512
num_examples: 2500
- name: test
num_bytes: 170259
num_examples: 250
download_size: 15719851
dataset_size: 1872771
- config_name: hn-10k-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 5229983
num_examples: 10000
- name: test
num_bytes: 523032
num_examples: 1000
download_size: 15719851
dataset_size: 5753015
- config_name: hn-10k-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1039729
num_examples: 1250
- name: test
num_bytes: 104061
num_examples: 125
download_size: 15719851
dataset_size: 1143790
- config_name: hn-10k-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1738458
num_examples: 1977
- name: test
num_bytes: 176824
num_examples: 198
download_size: 15719851
dataset_size: 1915282
- config_name: hn-10k-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4702044
num_examples: 10000
- name: test
num_bytes: 470479
num_examples: 1000
download_size: 15719851
dataset_size: 5172523
- config_name: hn-10k-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1147599
num_examples: 934
- name: test
num_bytes: 115088
num_examples: 94
download_size: 15719851
dataset_size: 1262687
- config_name: shuffled-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 165386
num_examples: 200
- name: test
num_bytes: 165517
num_examples: 200
download_size: 15719851
dataset_size: 330903
- config_name: shuffled-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 302888
num_examples: 200
- name: test
num_bytes: 306631
num_examples: 200
download_size: 15719851
dataset_size: 609519
- config_name: shuffled-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 887756
num_examples: 200
- name: test
num_bytes: 883187
num_examples: 200
download_size: 15719851
dataset_size: 1770943
- config_name: shuffled-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 205510
num_examples: 1000
- name: test
num_bytes: 205434
num_examples: 1000
download_size: 15719851
dataset_size: 410944
- config_name: shuffled-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 337349
num_examples: 200
- name: test
num_bytes: 350457
num_examples: 200
download_size: 15719851
dataset_size: 687806
- config_name: shuffled-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 173053
num_examples: 200
- name: test
num_bytes: 172249
num_examples: 200
download_size: 15719851
dataset_size: 345302
- config_name: shuffled-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 224778
num_examples: 200
- name: test
num_bytes: 215512
num_examples: 200
download_size: 15719851
dataset_size: 440290
- config_name: shuffled-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 212517
num_examples: 200
- name: test
num_bytes: 216244
num_examples: 200
download_size: 15719851
dataset_size: 428761
- config_name: shuffled-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 168350
num_examples: 200
- name: test
num_bytes: 168248
num_examples: 200
download_size: 15719851
dataset_size: 336598
- config_name: shuffled-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 170257
num_examples: 200
- name: test
num_bytes: 170672
num_examples: 200
download_size: 15719851
dataset_size: 340929
- config_name: shuffled-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 178083
num_examples: 200
- name: test
num_bytes: 178313
num_examples: 200
download_size: 15719851
dataset_size: 356396
- config_name: shuffled-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 185600
num_examples: 200
- name: test
num_bytes: 185529
num_examples: 200
download_size: 15719851
dataset_size: 371129
- config_name: shuffled-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 190556
num_examples: 200
- name: test
num_bytes: 190484
num_examples: 200
download_size: 15719851
dataset_size: 381040
- config_name: shuffled-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 234355
num_examples: 200
- name: test
num_bytes: 233204
num_examples: 200
download_size: 15719851
dataset_size: 467559
- config_name: shuffled-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 163728
num_examples: 250
- name: test
num_bytes: 163809
num_examples: 250
download_size: 15719851
dataset_size: 327537
- config_name: shuffled-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 456374
num_examples: 1000
- name: test
num_bytes: 456248
num_examples: 1000
download_size: 15719851
dataset_size: 912622
- config_name: shuffled-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 103636
num_examples: 125
- name: test
num_bytes: 103618
num_examples: 125
download_size: 15719851
dataset_size: 207254
- config_name: shuffled-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 162875
num_examples: 198
- name: test
num_bytes: 161266
num_examples: 199
download_size: 15719851
dataset_size: 324141
- config_name: shuffled-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 404536
num_examples: 1000
- name: test
num_bytes: 404489
num_examples: 1000
download_size: 15719851
dataset_size: 809025
- config_name: shuffled-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 115812
num_examples: 94
- name: test
num_bytes: 115863
num_examples: 93
download_size: 15719851
dataset_size: 231675
- config_name: shuffled-10k-qa1
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1654288
num_examples: 2000
- name: test
num_bytes: 165517
num_examples: 200
download_size: 15719851
dataset_size: 1819805
- config_name: shuffled-10k-qa2
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3062580
num_examples: 2000
- name: test
num_bytes: 306631
num_examples: 200
download_size: 15719851
dataset_size: 3369211
- config_name: shuffled-10k-qa3
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 8921215
num_examples: 2000
- name: test
num_bytes: 883187
num_examples: 200
download_size: 15719851
dataset_size: 9804402
- config_name: shuffled-10k-qa4
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2055105
num_examples: 10000
- name: test
num_bytes: 205434
num_examples: 1000
download_size: 15719851
dataset_size: 2260539
- config_name: shuffled-10k-qa5
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3592157
num_examples: 2000
- name: test
num_bytes: 350457
num_examples: 200
download_size: 15719851
dataset_size: 3942614
- config_name: shuffled-10k-qa6
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1726716
num_examples: 2000
- name: test
num_bytes: 172249
num_examples: 200
download_size: 15719851
dataset_size: 1898965
- config_name: shuffled-10k-qa7
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2228087
num_examples: 2000
- name: test
num_bytes: 215512
num_examples: 200
download_size: 15719851
dataset_size: 2443599
- config_name: shuffled-10k-qa8
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2141880
num_examples: 2000
- name: test
num_bytes: 216244
num_examples: 200
download_size: 15719851
dataset_size: 2358124
- config_name: shuffled-10k-qa9
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1681213
num_examples: 2000
- name: test
num_bytes: 168248
num_examples: 200
download_size: 15719851
dataset_size: 1849461
- config_name: shuffled-10k-qa10
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1707675
num_examples: 2000
- name: test
num_bytes: 170672
num_examples: 200
download_size: 15719851
dataset_size: 1878347
- config_name: shuffled-10k-qa11
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1781176
num_examples: 2000
- name: test
num_bytes: 178313
num_examples: 200
download_size: 15719851
dataset_size: 1959489
- config_name: shuffled-10k-qa12
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1854745
num_examples: 2000
- name: test
num_bytes: 185529
num_examples: 200
download_size: 15719851
dataset_size: 2040274
- config_name: shuffled-10k-qa13
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1903149
num_examples: 2000
- name: test
num_bytes: 190484
num_examples: 200
download_size: 15719851
dataset_size: 2093633
- config_name: shuffled-10k-qa14
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2321511
num_examples: 2000
- name: test
num_bytes: 233204
num_examples: 200
download_size: 15719851
dataset_size: 2554715
- config_name: shuffled-10k-qa15
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1637398
num_examples: 2500
- name: test
num_bytes: 163809
num_examples: 250
download_size: 15719851
dataset_size: 1801207
- config_name: shuffled-10k-qa16
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4562844
num_examples: 10000
- name: test
num_bytes: 456248
num_examples: 1000
download_size: 15719851
dataset_size: 5019092
- config_name: shuffled-10k-qa17
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1034333
num_examples: 1250
- name: test
num_bytes: 103618
num_examples: 125
download_size: 15719851
dataset_size: 1137951
- config_name: shuffled-10k-qa18
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1641650
num_examples: 1978
- name: test
num_bytes: 161266
num_examples: 199
download_size: 15719851
dataset_size: 1802916
- config_name: shuffled-10k-qa19
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4045086
num_examples: 10000
- name: test
num_bytes: 404489
num_examples: 1000
download_size: 15719851
dataset_size: 4449575
- config_name: shuffled-10k-qa20
features:
- name: story
sequence:
- name: id
dtype: string
- name: type
dtype:
class_label:
names:
'0': context
'1': question
- name: text
dtype: string
- name: supporting_ids
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1157351
num_examples: 933
- name: test
num_bytes: 115863
num_examples: 93
download_size: 15719851
dataset_size: 1273214
---
# Dataset Card for bAbi QA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[The bAbI project](https://research.fb.com/downloads/babi/)
- **Repository:**
- **Paper:** [arXiv Paper](https://arxiv.org/pdf/1502.05698.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. The aim is to classify these tasks into skill sets,so that researchers can identify (and then rectify) the failings of their systems.
### Supported Tasks and Leaderboards
The dataset supports a set of 20 proxy story-based question answering tasks for various "types" in English and Hindi. The tasks are:
|task_no|task_name|
|----|------------|
|qa1 |single-supporting-fact|
|qa2 |two-supporting-facts|
|qa3 |three-supporting-facts|
|qa4 |two-arg-relations|
|qa5 |three-arg-relations|
|qa6 |yes-no-questions|
|qa7 |counting|
|qa8 |lists-sets|
|qa9 |simple-negation|
|qa10| indefinite-knowledge|
|qa11| basic-coreference|
|qa12| conjunction|
|qa13| compound-coreference|
|qa14| time-reasoning|
|qa15| basic-deduction|
|qa16| basic-induction|
|qa17| positional-reasoning|
|qa18| size-reasoning|
|qa19| path-finding|
|qa20| agents-motivations|
The "types" are are:
- `en`
- the tasks in English, readable by humans.
- `hn`
- the tasks in Hindi, readable by humans.
- `shuffled`
- the same tasks with shuffled letters so they are not readable by humans, and for existing parsers and taggers cannot be used in a straight-forward fashion to leverage extra resources-- in this case the learner is more forced to rely on the given training data. This mimics a learner being first presented a language and having to learn from scratch.
- `en-10k`, `shuffled-10k` and `hn-10k`
- the same tasks in the three formats, but with 10,000 training examples, rather than 1000 training examples.
- `en-valid` and `en-valid-10k`
- are the same as `en` and `en10k` except the train sets have been conveniently split into train and valid portions (90% and 10% split).
To get a particular dataset, use `load_dataset('babi_qa',type=f'{type}',task_no=f'{task_no}')` where `type` is one of the types, and `task_no` is one of the task numbers. For example, `load_dataset('babi_qa', type='en', task_no='qa1')`.
### Languages
## Dataset Structure
### Data Instances
An instance from the `en-qa1` config's `train` split:
```
{'story': {'answer': ['', '', 'bathroom', '', '', 'hallway', '', '', 'hallway', '', '', 'office', '', '', 'bathroom'], 'id': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'], 'supporting_ids': [[], [], ['1'], [], [], ['4'], [], [], ['4'], [], [], ['11'], [], [], ['8']], 'text': ['Mary moved to the bathroom.', 'John went to the hallway.', 'Where is Mary?', 'Daniel went back to the hallway.', 'Sandra moved to the garden.', 'Where is Daniel?', 'John moved to the office.', 'Sandra journeyed to the bathroom.', 'Where is Daniel?', 'Mary moved to the hallway.', 'Daniel travelled to the office.', 'Where is Daniel?', 'John went back to the garden.', 'John moved to the bedroom.', 'Where is Sandra?'], 'type': [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1]}}
```
### Data Fields
- `story`: a dictionary feature containing:
- `id`: a `string` feature, which denotes the line number in the example.
- `type`: a classification label, with possible values including `context`, `question`, denoting whether the text is context or a question.
- `text`: a `string` feature the text present, whether it is a question or context.
- `supporting_ids`: a `list` of `string` features containing the line numbers of the lines in the example which support the answer.
- `answer`: a `string` feature containing the answer to the question, or an empty string if the `type`s is not `question`.
### Data Splits
The splits and corresponding sizes are:
| | train | test | validation |
|-------------------|---------|--------|--------------|
| en-qa1 | 200 | 200 | - |
| en-qa2 | 200 | 200 | - |
| en-qa3 | 200 | 200 | - |
| en-qa4 | 1000 | 1000 | - |
| en-qa5 | 200 | 200 | - |
| en-qa6 | 200 | 200 | - |
| en-qa7 | 200 | 200 | - |
| en-qa8 | 200 | 200 | - |
| en-qa9 | 200 | 200 | - |
| en-qa10 | 200 | 200 | - |
| en-qa11 | 200 | 200 | - |
| en-qa12 | 200 | 200 | - |
| en-qa13 | 200 | 200 | - |
| en-qa14 | 200 | 200 | - |
| en-qa15 | 250 | 250 | - |
| en-qa16 | 1000 | 1000 | - |
| en-qa17 | 125 | 125 | - |
| en-qa18 | 198 | 199 | - |
| en-qa19 | 1000 | 1000 | - |
| en-qa20 | 94 | 93 | - |
| en-10k-qa1 | 2000 | 200 | - |
| en-10k-qa2 | 2000 | 200 | - |
| en-10k-qa3 | 2000 | 200 | - |
| en-10k-qa4 | 10000 | 1000 | - |
| en-10k-qa5 | 2000 | 200 | - |
| en-10k-qa6 | 2000 | 200 | - |
| en-10k-qa7 | 2000 | 200 | - |
| en-10k-qa8 | 2000 | 200 | - |
| en-10k-qa9 | 2000 | 200 | - |
| en-10k-qa10 | 2000 | 200 | - |
| en-10k-qa11 | 2000 | 200 | - |
| en-10k-qa12 | 2000 | 200 | - |
| en-10k-qa13 | 2000 | 200 | - |
| en-10k-qa14 | 2000 | 200 | - |
| en-10k-qa15 | 2500 | 250 | - |
| en-10k-qa16 | 10000 | 1000 | - |
| en-10k-qa17 | 1250 | 125 | - |
| en-10k-qa18 | 1978 | 199 | - |
| en-10k-qa19 | 10000 | 1000 | - |
| en-10k-qa20 | 933 | 93 | - |
| en-valid-qa1 | 180 | 200 | 20 |
| en-valid-qa2 | 180 | 200 | 20 |
| en-valid-qa3 | 180 | 200 | 20 |
| en-valid-qa4 | 900 | 1000 | 100 |
| en-valid-qa5 | 180 | 200 | 20 |
| en-valid-qa6 | 180 | 200 | 20 |
| en-valid-qa7 | 180 | 200 | 20 |
| en-valid-qa8 | 180 | 200 | 20 |
| en-valid-qa9 | 180 | 200 | 20 |
| en-valid-qa10 | 180 | 200 | 20 |
| en-valid-qa11 | 180 | 200 | 20 |
| en-valid-qa12 | 180 | 200 | 20 |
| en-valid-qa13 | 180 | 200 | 20 |
| en-valid-qa14 | 180 | 200 | 20 |
| en-valid-qa15 | 225 | 250 | 25 |
| en-valid-qa16 | 900 | 1000 | 100 |
| en-valid-qa17 | 113 | 125 | 12 |
| en-valid-qa18 | 179 | 199 | 19 |
| en-valid-qa19 | 900 | 1000 | 100 |
| en-valid-qa20 | 85 | 93 | 9 |
| en-valid-10k-qa1 | 1800 | 200 | 200 |
| en-valid-10k-qa2 | 1800 | 200 | 200 |
| en-valid-10k-qa3 | 1800 | 200 | 200 |
| en-valid-10k-qa4 | 9000 | 1000 | 1000 |
| en-valid-10k-qa5 | 1800 | 200 | 200 |
| en-valid-10k-qa6 | 1800 | 200 | 200 |
| en-valid-10k-qa7 | 1800 | 200 | 200 |
| en-valid-10k-qa8 | 1800 | 200 | 200 |
| en-valid-10k-qa9 | 1800 | 200 | 200 |
| en-valid-10k-qa10 | 1800 | 200 | 200 |
| en-valid-10k-qa11 | 1800 | 200 | 200 |
| en-valid-10k-qa12 | 1800 | 200 | 200 |
| en-valid-10k-qa13 | 1800 | 200 | 200 |
| en-valid-10k-qa14 | 1800 | 200 | 200 |
| en-valid-10k-qa15 | 2250 | 250 | 250 |
| en-valid-10k-qa16 | 9000 | 1000 | 1000 |
| en-valid-10k-qa17 | 1125 | 125 | 125 |
| en-valid-10k-qa18 | 1781 | 199 | 197 |
| en-valid-10k-qa19 | 9000 | 1000 | 1000 |
| en-valid-10k-qa20 | 840 | 93 | 93 |
| hn-qa1 | 200 | 200 | - |
| hn-qa2 | 200 | 200 | - |
| hn-qa3 | 167 | 167 | - |
| hn-qa4 | 1000 | 1000 | - |
| hn-qa5 | 200 | 200 | - |
| hn-qa6 | 200 | 200 | - |
| hn-qa7 | 200 | 200 | - |
| hn-qa8 | 200 | 200 | - |
| hn-qa9 | 200 | 200 | - |
| hn-qa10 | 200 | 200 | - |
| hn-qa11 | 200 | 200 | - |
| hn-qa12 | 200 | 200 | - |
| hn-qa13 | 125 | 125 | - |
| hn-qa14 | 200 | 200 | - |
| hn-qa15 | 250 | 250 | - |
| hn-qa16 | 1000 | 1000 | - |
| hn-qa17 | 125 | 125 | - |
| hn-qa18 | 198 | 198 | - |
| hn-qa19 | 1000 | 1000 | - |
| hn-qa20 | 93 | 94 | - |
| hn-10k-qa1 | 2000 | 200 | - |
| hn-10k-qa2 | 2000 | 200 | - |
| hn-10k-qa3 | 1667 | 167 | - |
| hn-10k-qa4 | 10000 | 1000 | - |
| hn-10k-qa5 | 2000 | 200 | - |
| hn-10k-qa6 | 2000 | 200 | - |
| hn-10k-qa7 | 2000 | 200 | - |
| hn-10k-qa8 | 2000 | 200 | - |
| hn-10k-qa9 | 2000 | 200 | - |
| hn-10k-qa10 | 2000 | 200 | - |
| hn-10k-qa11 | 2000 | 200 | - |
| hn-10k-qa12 | 2000 | 200 | - |
| hn-10k-qa13 | 1250 | 125 | - |
| hn-10k-qa14 | 2000 | 200 | - |
| hn-10k-qa15 | 2500 | 250 | - |
| hn-10k-qa16 | 10000 | 1000 | - |
| hn-10k-qa17 | 1250 | 125 | - |
| hn-10k-qa18 | 1977 | 198 | - |
| hn-10k-qa19 | 10000 | 1000 | - |
| hn-10k-qa20 | 934 | 94 | - |
| shuffled-qa1 | 200 | 200 | - |
| shuffled-qa2 | 200 | 200 | - |
| shuffled-qa3 | 200 | 200 | - |
| shuffled-qa4 | 1000 | 1000 | - |
| shuffled-qa5 | 200 | 200 | - |
| shuffled-qa6 | 200 | 200 | - |
| shuffled-qa7 | 200 | 200 | - |
| shuffled-qa8 | 200 | 200 | - |
| shuffled-qa9 | 200 | 200 | - |
| shuffled-qa10 | 200 | 200 | - |
| shuffled-qa11 | 200 | 200 | - |
| shuffled-qa12 | 200 | 200 | - |
| shuffled-qa13 | 200 | 200 | - |
| shuffled-qa14 | 200 | 200 | - |
| shuffled-qa15 | 250 | 250 | - |
| shuffled-qa16 | 1000 | 1000 | - |
| shuffled-qa17 | 125 | 125 | - |
| shuffled-qa18 | 198 | 199 | - |
| shuffled-qa19 | 1000 | 1000 | - |
| shuffled-qa20 | 94 | 93 | - |
| shuffled-10k-qa1 | 2000 | 200 | - |
| shuffled-10k-qa2 | 2000 | 200 | - |
| shuffled-10k-qa3 | 2000 | 200 | - |
| shuffled-10k-qa4 | 10000 | 1000 | - |
| shuffled-10k-qa5 | 2000 | 200 | - |
| shuffled-10k-qa6 | 2000 | 200 | - |
| shuffled-10k-qa7 | 2000 | 200 | - |
| shuffled-10k-qa8 | 2000 | 200 | - |
| shuffled-10k-qa9 | 2000 | 200 | - |
| shuffled-10k-qa10 | 2000 | 200 | - |
| shuffled-10k-qa11 | 2000 | 200 | - |
| shuffled-10k-qa12 | 2000 | 200 | - |
| shuffled-10k-qa13 | 2000 | 200 | - |
| shuffled-10k-qa14 | 2000 | 200 | - |
| shuffled-10k-qa15 | 2500 | 250 | - |
| shuffled-10k-qa16 | 10000 | 1000 | - |
| shuffled-10k-qa17 | 1250 | 125 | - |
| shuffled-10k-qa18 | 1978 | 199 | - |
| shuffled-10k-qa19 | 10000 | 1000 | - |
| shuffled-10k-qa20 | 933 | 93 | - |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Code to generate tasks is available on [github](https://github.com/facebook/bAbI-tasks)
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston, at Facebook Research.
### Licensing Information
```
Creative Commons Attribution 3.0 License
```
### Citation Information
```
@misc{dodge2016evaluating,
title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
year={2016},
eprint={1511.06931},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. |
HoangCuongNguyen/CTI-to-MITRE-dataset | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
size_categories:
- 10K<n<100K
--- |
codesignal/lending-club-loan-accepted | ---
license: cc0-1.0
---
|
liuyanchen1015/MULTI_VALUE_qqp_inverted_indirect_question | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 120296
num_examples: 637
- name: test
num_bytes: 1176553
num_examples: 6123
- name: train
num_bytes: 1126859
num_examples: 5754
download_size: 1405040
dataset_size: 2423708
---
# Dataset Card for "MULTI_VALUE_qqp_inverted_indirect_question"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
freshpearYoon/vr_train_free_12 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: filename
dtype: string
- name: NumOfUtterance
dtype: int64
- name: text
dtype: string
- name: samplingrate
dtype: int64
- name: begin_time
dtype: float64
- name: end_time
dtype: float64
- name: speaker_id
dtype: string
- name: directory
dtype: string
splits:
- name: train
num_bytes: 6924344989
num_examples: 10000
download_size: 1073285964
dataset_size: 6924344989
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/British_English_Spontaneous_Speech_Data | ---
task_categories:
- automatic-speech-recognition
language:
- en
---
# Dataset Card for Nexdata/British_English_Spontaneous_Speech_Data
## Description
1,013 Hours – British English Spontaneous Speech Data, the content covering multiple topics. All the speech audio was manually transcribed into text content; speaker identity, gender, and other attribution are also annotated. This dataset can be used for voiceprint recognition model training, corpus construction for machine translation, and algorithm research introduction
For more details, please refer to the link: https://www.nexdata.ai/datasets/1262?source=Huggingface
# Specifications
## Format
16kHz, 16bit, mono channel;
## Content category
including conversation, self-media, etc.
## Language
British English;
## Annotation
annotation for the transcription text, speaker identification, gender;
## Application scenarios
speech recognition, video caption generation and video content review;
## Accuracy
at a Sentence Accuracy Rate (SAR) of being no less than 95%.
# Licensing Information
Commercial License |
Thai2009/Voi_12 | ---
license: bigcode-openrail-m
---
|
ai-ml-ops-eng/tensortrust-datasets | ---
license: unknown
---
|
BEE-spoke-data/allNLI-sbert | ---
language:
- en
license: odc-by
size_categories:
- 100K<n<1M
task_categories:
- sentence-similarity
dataset_info:
- config_name: default
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 144780011.33594054
num_examples: 942069
- name: validation
num_bytes: 3020947.173540986
num_examples: 19657
- name: test
num_bytes: 3020793.490518473
num_examples: 19656
download_size: 72629620
dataset_size: 150821752
- config_name: float-labels
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 138755142
num_examples: 942069
- name: validation
num_bytes: 3034127
num_examples: 19657
- name: test
num_bytes: 3142127
num_examples: 19656
download_size: 72653539
dataset_size: 144931396
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: float-labels
data_files:
- split: train
path: float-labels/train-*
- split: validation
path: float-labels/validation-*
- split: test
path: float-labels/test-*
---
this is literally the allNLI example dataset but parsed and reformatted as HF datasets parquet.
## token counts
### sentence1 column
bert-base-uncased token count:
```
token_count
count 942069.000000
mean 20.834934
std 12.953432
min 3.000000
25% 13.000000
50% 17.000000
75% 25.000000
max 428.000000
```
- Total count: 19.63 M tokens
google/bigbird-roberta-base token count:
```
token_count
count 942069.000000
mean 20.678186
std 12.618819
min 3.000000
25% 13.000000
50% 17.000000
75% 25.000000
max 407.000000
```
- Total count: 19.48 M tokens
### sentence2 column
bert-base-uncased token count:
```
token_count
count 942069.000000
mean 12.058493
std 4.507284
min 0.000000
25% 9.000000
50% 11.000000
75% 14.000000
max 77.000000
```
- Total count: 11.36 M tokens
google/bigbird-roberta-base token count:
```
token_count
count 942069.000000
mean 12.003818
std 4.423798
min 0.000000
25% 9.000000
50% 11.000000
75% 14.000000
max 79.000000
```
- Total count: 11.31 M tokens |
biglam/early_printed_books_font_detection | ---
dataset_info:
features:
- name: image
dtype: image
- name: labels
sequence:
class_label:
names:
0: greek
1: antiqua
2: other_font
3: not_a_font
4: italic
5: rotunda
6: textura
7: fraktur
8: schwabacher
9: hebrew
10: bastarda
11: gotico_antiqua
splits:
- name: test
num_bytes: 2345451
num_examples: 10757
- name: train
num_bytes: 5430875
num_examples: 24866
download_size: 44212934313
dataset_size: 7776326
annotations_creators:
- expert-generated
language: []
language_creators: []
license:
- cc-by-nc-sa-4.0
multilinguality: []
pretty_name: Early Printed Books Font Detection Dataset
size_categories:
- 10K<n<100K
source_datasets: []
tags: []
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
---
# Dataset Card for Early Printed Books Font Detection Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**https://doi.org/10.5281/zenodo.3366686
- **Paper:**: https://doi.org/10.1145/3352631.3352640
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
> This dataset is composed of photos of various resolution of 35'623 pages of printed books dating from the 15th to the 18th century. Each page has been attributed by experts from one to five labels corresponding to the font groups used in the text, with two extra-classes for non-textual content and fonts not present in the following list: Antiqua, Bastaπrda, Fraktur, Gotico Antiqua, Greek, Hebrew, Italic, Rotunda, Schwabacher, and Textura.
[More Information Needed]
### Supported Tasks and Leaderboards
The primary use case for this datasets is
- `multi-label-image-classification`: This dataset can be used to train a model for multi label image classification where each image can have one, or more labels.
- `image-classification`: This dataset could also be adapted to only predict a single label for each image
### Languages
The dataset includes books from a range of libraries (see below for further details). The paper doesn't provide a detailed overview of language breakdown. However, the books are from the 15th-18th century and appear to be dominated by European languages from that time period. The dataset also includes Hebrew.
[More Information Needed]
## Dataset Structure
This dataset has a single configuration.
### Data Instances
An example instance from this dataset:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x3840 at 0x7F6AC192D850>,
'labels': [5]}
```
### Data Fields
This dataset contains two fields:
- `image`: the image of the book page
- `labels`: one or more labels for the font used in the book page depicted in the `image`
### Data Splits
The dataset is broken into a train and test split with the following breakdown of number of examples:
- train: 24,866
- test: 10,757
## Dataset Creation
### Curation Rationale
The dataset was created to help train and evaluate automatic methods for font detection. The paper describing the paper also states that:
>data was cherry-picked, thus it is not statistically representative of what can be found in libraries. For example, as we had a small amount of Textura at the start, we specifically looked for more pages containing this font group, so we can expect that less than 3.6 % of randomly selected pages from libraries would contain Textura.
### Source Data
#### Initial Data Collection and Normalization
The images in this dataset are from books held by the British Library (London), Bayerische Staatsbibliothek München, Staatsbibliothek zu Berlin, Universitätsbibliothek Erlangen, Universitätsbibliothek Heidelberg, Staats- und Universitäatsbibliothek Göttingen, Stadt- und Universitätsbibliothek Köln, Württembergische Landesbibliothek Stuttgart and Herzog August Bibliothek Wolfenbüttel.
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
Siki-77/hcad_imdb | ---
configs:
- config_name: original
data_files:
- split: train
path: "original17/train.tsv"
- split: test
path: "original17/test.tsv"
- split: val
path: "original17/dev.tsv"
- config_name: revised
data_files:
- split: train
path: "revised17/train.tsv"
- split: test
path: "revised17/test.tsv"
- split: val
path: "revised17/dev.tsv"
- config_name: cad
data_files:
- split: train
path: "pair/train_paired.tsv"
- split: test
path: "pair/test_paired.tsv"
- split: val
path: "pair/dev_paired.tsv"
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- code
size_categories:
- 1K<n<10K
--- |
louisbrulenaudet/code-justice-administrative | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de justice administrative
source_datasets:
- original
pretty_name: Code de justice administrative
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code de justice administrative, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
chenhaodev/uptodate-heart-failure-self-management | ---
license: afl-3.0
---
|
OpenLeecher/Teatime | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
- ko
size_categories:
- 10K<n<100K
---
### INFO:
These are the parsed logs from the "teatime logs" xlsx files.
Every user edit or message regeneration makes a new branch in the conversation tree. This leads to message duplication in the 'all_logs.json' file. Every change creates a fresh branch, copying all earlier messages.
The 'longest' files are different. They only contain the longest path from the first to the last message. This approach aims to avoid duplication. Ideally, the '_longest' files should have no repeat messages.
### all_logs.json
Total tokens: 237442515
Average chat token length: 4246.03
Median chat token length: 3797.0
Average messages per chat: 18.96
Median messages per chat: 15.0
Total number of chats: 55921
### all_logs_longest.json
Total tokens: 27611121
Average chat token length: 2499.65
Median chat token length: 1335.5
Average messages per chat: 11.27
Median messages per chat: 5.0
Total number of chats: 11046
 |
vucinatim/spectrogram-captions | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Captioned generic audio clips with spectrogram images
size_categories:
- n<1K
source_datasets: []
tags:
- 'stable diffusion sound generation
text-to-sound
text-to-image-to-sound
spectrogram'
task_categories:
- text-to-image
task_ids: []
---
Dataset of captioned spectrograms (text describing the sound). |
Bruno1424/MEKA_COME_COME | ---
license: openrail
---
|
Harsha9044/Malayalam_dataset | ---
dataset_info:
features:
- name: File Name
dtype: string
- name: Text
dtype: string
- name: Audio
dtype: audio
- name: Sentiment
dtype: string
splits:
- name: train
num_bytes: 91397254.0
num_examples: 70
download_size: 91083349
dataset_size: 91397254.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_TinyPixel__testmodel2 | ---
pretty_name: Evaluation run of TinyPixel/testmodel2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TinyPixel/testmodel2](https://huggingface.co/TinyPixel/testmodel2) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TinyPixel__testmodel2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T13:54:24.629963](https://huggingface.co/datasets/open-llm-leaderboard/details_TinyPixel__testmodel2/blob/main/results_2023-10-24T13-54-24.629963.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\
\ \"em_stderr\": 0.00036305608931189794,\n \"f1\": 0.05664848993288591,\n\
\ \"f1_stderr\": 0.001329470291478584,\n \"acc\": 0.4072684276253865,\n\
\ \"acc_stderr\": 0.009841754656544565\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931189794,\n\
\ \"f1\": 0.05664848993288591,\n \"f1_stderr\": 0.001329470291478584\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07657316148597422,\n \
\ \"acc_stderr\": 0.007324564881451568\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637561\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TinyPixel/testmodel2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|arc:challenge|25_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T13_54_24.629963
path:
- '**/details_harness|drop|3_2023-10-24T13-54-24.629963.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T13-54-24.629963.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T13_54_24.629963
path:
- '**/details_harness|gsm8k|5_2023-10-24T13-54-24.629963.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T13-54-24.629963.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hellaswag|10_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-28-17.558290.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T14-28-17.558290.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T14-28-17.558290.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T13_54_24.629963
path:
- '**/details_harness|winogrande|5_2023-10-24T13-54-24.629963.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T13-54-24.629963.parquet'
- config_name: results
data_files:
- split: 2023_09_18T14_28_17.558290
path:
- results_2023-09-18T14-28-17.558290.parquet
- split: 2023_10_24T13_54_24.629963
path:
- results_2023-10-24T13-54-24.629963.parquet
- split: latest
path:
- results_2023-10-24T13-54-24.629963.parquet
---
# Dataset Card for Evaluation run of TinyPixel/testmodel2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TinyPixel/testmodel2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TinyPixel/testmodel2](https://huggingface.co/TinyPixel/testmodel2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TinyPixel__testmodel2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T13:54:24.629963](https://huggingface.co/datasets/open-llm-leaderboard/details_TinyPixel__testmodel2/blob/main/results_2023-10-24T13-54-24.629963.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931189794,
"f1": 0.05664848993288591,
"f1_stderr": 0.001329470291478584,
"acc": 0.4072684276253865,
"acc_stderr": 0.009841754656544565
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931189794,
"f1": 0.05664848993288591,
"f1_stderr": 0.001329470291478584
},
"harness|gsm8k|5": {
"acc": 0.07657316148597422,
"acc_stderr": 0.007324564881451568
},
"harness|winogrande|5": {
"acc": 0.7379636937647988,
"acc_stderr": 0.012358944431637561
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## 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
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
xDAN-datasets/ChatDoctor_chatGpt_7k | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: conversations_chatgpt
list:
- name: from
dtype: string
- name: value
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 18817244
num_examples: 7321
download_size: 10222055
dataset_size: 18817244
---
# Dataset Card for "ChatDoctor_chatGpt_7k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mrpc_regularized_reflexives | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 2968
num_examples: 10
- name: train
num_bytes: 6528
num_examples: 22
- name: validation
num_bytes: 1168
num_examples: 4
download_size: 18980
dataset_size: 10664
---
# Dataset Card for "MULTI_VALUE_mrpc_regularized_reflexives"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
communityai/apt-chat-micro-dataset-llm-v2-714k | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int64
- name: source
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 1722474450.0482376
num_examples: 713591
- name: test
num_bytes: 1206905.9517624504
num_examples: 500
download_size: 903669025
dataset_size: 1723681356.0
---
# Dataset Card for "apt-chat-micro-dataset-llm-v2-714k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seamusl/gaHealth | ---
annotations_creators:
- expert-generated
language:
- en
- ga
language_creators:
- expert-generated
license:
- mit
multilinguality:
- translation
pretty_name: gaHealth
size_categories:
- 10K<n<100K
source_datasets: []
tags: []
task_categories:
- translation
task_ids: []
configs:
- en-ga
dataset_info:
- config_name: en-ga
features:
- name: translation
dtype:
translation:
languages:
- en
- ga
--- |
Seongill/NQ_missing_3 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: has_answer
dtype: bool
splits:
- name: train
num_bytes: 7388979
num_examples: 3610
download_size: 4500772
dataset_size: 7388979
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_13_10000000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 192699
num_examples: 6699
download_size: 124923
dataset_size: 192699
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_13_10000000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/VQAv2_minival_no_image_google_flan_t5_xl_mode_D_PNP_FILTER_Q_rices_ns_25994 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_random_
num_bytes: 3681015
num_examples: 25994
download_size: 1314725
dataset_size: 3681015
---
# Dataset Card for "VQAv2_minival_no_image_google_flan_t5_xl_mode_D_PNP_FILTER_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
datahrvoje/twitter_dataset_1713165603 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 23409
num_examples: 54
download_size: 12940
dataset_size: 23409
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lighteval/natural_questions_clean | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: document
dtype: string
- name: question
dtype: string
- name: long_answers
sequence: string
- name: short_answers
sequence: string
splits:
- name: train
num_bytes: 4346873866.211105
num_examples: 106926
- name: validation
num_bytes: 175230324.62247765
num_examples: 4289
download_size: 1406784865
dataset_size: 4522104190.833583
---
# Dataset Card for "natural_questions_clean"
Created by @thomwolf on the basis of https://huggingface.co/datasets/lighteval/natural_questions but removing the questions without short answers provided.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lhallee/ssq8 | ---
dataset_info:
features:
- name: seqs
dtype: string
- name: labels
dtype: string
splits:
- name: train
num_bytes: 5373910
num_examples: 10792
- name: valid
num_bytes: 331482
num_examples: 626
- name: test
num_bytes: 22594
num_examples: 50
download_size: 3918046
dataset_size: 5727986
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
Nexdata/Chinese_News_Text_Data | ---
task_categories:
- conversational
language:
- zh
---
# Dataset Card for Nexdata/Chinese_News_Text_Data
## Description
News content data, about 35G in total; each piece of news comment content contains ID, time, news title and news body; this dataset can be used for tasks such as LLM training, chatgpt
For more details, please refer to the link: https://www.nexdata.ai/datasets/1258?source=Huggingface
# Specifications
## Data content
News content data
## Data size
About 35G
## Data fields
id,time,title,body
## Collecting time
February 1,991 - July 2,017
## Storage format
json
## Language
Chinese
## The amount of data
The amount of neutral data is not less than 1.6 hours; the amount of data with filler word is not less than 0.4 hours; and the remaining six types of emotional data is not less than 1.67 hours each
# Licensing Information
Commercial License |
Shuv001/processed_r50 | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 453988831.0
num_examples: 50000
download_size: 324957581
dataset_size: 453988831.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nlplabtdtu/Extractive-QA-type-1 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: is_impossible
dtype: bool
- name: instruction
dtype: string
- name: prompt_name
dtype: string
splits:
- name: train
num_bytes: 41943254
num_examples: 19240
download_size: 8170598
dataset_size: 41943254
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Extractive-QA-type-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
OpenGVLab/AS-Core | ---
license: apache-2.0
---
# AS-Core
AS-Core is the human-verified subset of AS-1B.
- `semantic_tag_1m.json`: the human verified annotations for semantic tags.
- `region_vqa_1m.jsonl`: the human verified annotations for region VQA.
- `region_caption_400k.jsonl`: the region captions generated base on paraphrasing the region question-answer pairs.
***NOTE***: The bbox format is `x1y1x2y2`.
## Introduction
We present the All-Seeing Project with:
[***All-Seeing 1B (AS-1B) dataset***](https://huggingface.co/datasets/Weiyun1025/AS-100M): we propose a new large-scale dataset (AS-1B) for open-world panoptic visual recognition and understanding, using an economical semi-automatic data engine that combines the power of off-the-shelf vision/language models and human feedback.
[***All-Seeing Model (ASM)***](https://huggingface.co/Weiyun1025/All-Seeing-Model-FT): we develop a unified vision-language foundation model (ASM) for open-world panoptic visual recognition and understanding. Aligning with LLMs, our ASM supports versatile image-text retrieval and generation tasks, demonstrating impressive zero-shot capability.
<img width="820" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/e43ab8db-6437-46f1-8aa1-c95f012e9147">
Figure 1: Overview and comparison of our All-Seeing project with other popular large foundation models.
<!-- ## Online Demo
**All-Seeing Model demo** is available [here](https://openxlab.org.cn/apps/detail/wangweiyun/All-Seeing-Model-Demo).
**Dataset Browser** is available [here](https://openxlab.org.cn/apps/detail/wangweiyun/All-Seeing-Dataset-Browser).
https://github.com/OpenGVLab/all-seeing/assets/47669167/9b5b32d1-863a-4579-b576-b82523f2205e -->
## Dataset Overview
AS-1B with over 1 billion regions annotated with semantic tags, question-answering pairs, and detailed captions. It covers a wide range of 3.5 million common and rare concepts in the real world, and has 132.2 billion tokens that describe the concepts and their attributes.
<img width="800" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/adac37ed-312f-4f11-ba8a-6bc62067438f">
Some examples
<img width="800" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/fcf6ab07-c4ba-441c-aa6c-111c769f75b1">
Please see our [paper](https://arxiv.org/abs/2308.01907) to learn more details.
## Model Architecture
The All-Seeing model (ASM) is a unified framework for panoptic visual recognition and understanding, including image/region-text retrieval, image/region recognition, captioning, and question-answering.
<img width="820" alt="image" src="https://github.com/OpenGVLab/all-seeing/assets/8529570/8995e88c-6381-452f-91e4-05d68a2795fc">
## License
This project is released under the [Apache 2.0 license](LICENSE).
# Citation
If you find our work useful in your research, please consider cite:
```BibTeX
@article{wang2023allseeing,
title={The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World},
author={Wang, Weiyun and Shi, Min and Li, Qingyun and Wang, Wenhai and Huang, Zhenhang and Xing, Linjie and Chen, Zhe and Li, Hao and Zhu, Xizhou and Cao, Zhiguo and others},
journal={arXiv preprint arXiv:2308.01907},
year={2023}
}
@article{wang2024allseeing_v2,
title={The All-Seeing Project V2: Towards General Relation Comprehension of the Open World},
author={Wang, Weiyun and Ren, Yiming and Luo, Haowen and Li, Tiantong and Yan, Chenxiang and Chen, Zhe and Wang, Wenhai and Li, Qingyun and Lu, Lewei and Zhu, Xizhou and others},
journal={arXiv preprint arXiv:2402.19474},
year={2024}
}
``` |
one-sec-cv12/chunk_168 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 25019927712.25
num_examples: 260494
download_size: 23914640600
dataset_size: 25019927712.25
---
# Dataset Card for "chunk_168"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-samsum-e4148a42-11205498 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
metrics: ['perplexity']
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
habixia1/emo1 | ---
license: afl-3.0
---
|
oblivisheee/ayase-saki-dataset | ---
license: creativeml-openrail-m
tags:
- art
---
<i>Idk how to publish dataset correct</i>
So, i published that dataset for public, because... idk for what, just like that.
Dataset contain 49 images and 49 tags, you could download it via zip file. |
CyberHarem/yumi_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yumi/雪泉/雪泉 (Azur Lane)
This is the dataset of yumi/雪泉/雪泉 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `breasts, blue_eyes, short_hair, bow, grey_hair, hair_bow, large_breasts, white_bow, medium_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 680.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yumi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 374.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yumi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1222 | 781.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yumi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 593.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yumi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1222 | 1.12 GiB | [Download](https://huggingface.co/datasets/CyberHarem/yumi_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/yumi_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, white_background, collarbone, simple_background, blush, bare_shoulders, navel, smile, bangs, huge_breasts, blue_bikini |
| 1 | 9 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, collarbone, looking_at_viewer, off_shoulder, simple_background, solo, white_background, white_kimono, bangs, low_neckline, blush, open_mouth, shiny_skin, huge_breasts, shiny_hair |
| 2 | 7 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, collarbone, looking_at_viewer, off_shoulder, parted_bangs, solo, white_kimono, low_neckline, upper_body, blush, closed_mouth, smile, wide_sleeves, snowflakes |
| 3 | 11 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, kimono, looking_at_viewer, off_shoulder, solo, low_neckline, collarbone, folding_fan, huge_breasts, smile |
| 4 | 23 |  |  |  |  |  | day, looking_at_viewer, cleavage, 1girl, outdoors, navel, smile, solo, blush, beach, ocean, blue_sky, cloud, blue_bikini, open_mouth, water, bare_shoulders, collarbone, side-tie_bikini_bottom |
| 5 | 12 |  |  |  |  |  | 1boy, 1girl, blush, collarbone, hetero, solo_focus, nipples, paizuri, huge_breasts, penis, breasts_squeezed_together, open_mouth, bare_shoulders, looking_at_viewer, nude, smile, sweat, bangs, mosaic_censoring |
| 6 | 7 |  |  |  |  |  | 1girl, cat_ears, looking_at_viewer, solo, navel, open_mouth, smile, blush, cat_tail, cleavage, simple_background, bare_shoulders, bell, white_background, cat_paws, gloves, white_panties |
| 7 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, open_mouth, simple_background, white_background, white_shirt, black_pantyhose, smile, black_hair, black_skirt, cleavage, long_sleeves, pencil_skirt |
| 8 | 8 |  |  |  |  |  | 1girl, blush, hetero, huge_breasts, penis, pussy, sex, shiny_hair, spread_legs, vaginal, 1boy, shiny_skin, solo_focus, bar_censor, navel, nipples, nude, open_mouth, sweat, collarbone, kimono |
| 9 | 12 |  |  |  |  |  | playboy_bunny, rabbit_ears, 1girl, fake_animal_ears, solo, detached_collar, looking_at_viewer, rabbit_tail, strapless_leotard, bare_shoulders, pantyhose, blush, cleavage, fishnets, white_background, white_leotard, simple_background, wrist_cuffs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | looking_at_viewer | solo | white_background | collarbone | simple_background | blush | bare_shoulders | navel | smile | bangs | huge_breasts | blue_bikini | off_shoulder | white_kimono | low_neckline | open_mouth | shiny_skin | shiny_hair | parted_bangs | upper_body | closed_mouth | wide_sleeves | snowflakes | kimono | folding_fan | day | outdoors | beach | ocean | blue_sky | cloud | water | side-tie_bikini_bottom | 1boy | hetero | solo_focus | nipples | paizuri | penis | breasts_squeezed_together | nude | sweat | mosaic_censoring | cat_ears | cat_tail | bell | cat_paws | gloves | white_panties | white_shirt | black_pantyhose | black_hair | black_skirt | long_sleeves | pencil_skirt | pussy | sex | spread_legs | vaginal | bar_censor | playboy_bunny | rabbit_ears | fake_animal_ears | detached_collar | rabbit_tail | strapless_leotard | pantyhose | fishnets | white_leotard | wrist_cuffs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------------|:-------|:-------------------|:-------------|:--------------------|:--------|:-----------------|:--------|:--------|:--------|:---------------|:--------------|:---------------|:---------------|:---------------|:-------------|:-------------|:-------------|:---------------|:-------------|:---------------|:---------------|:-------------|:---------|:--------------|:------|:-----------|:--------|:--------|:-----------|:--------|:--------|:-------------------------|:-------|:---------|:-------------|:----------|:----------|:--------|:----------------------------|:-------|:--------|:-------------------|:-----------|:-----------|:-------|:-----------|:---------|:----------------|:--------------|:------------------|:-------------|:--------------|:---------------|:---------------|:--------|:------|:--------------|:----------|:-------------|:----------------|:--------------|:-------------------|:------------------|:--------------|:--------------------|:------------|:-----------|:----------------|:--------------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | X | X | | X | | X | X | | X | | | | X | X | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | X | X | | X | | | X | | X | | X | | X | | X | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 23 |  |  |  |  |  | X | X | X | X | | X | | X | X | X | X | | | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 12 |  |  |  |  |  | X | | X | | | X | | X | X | | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | X | X | X | | X | X | X | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | X | X | X | | X | X | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 8 | 8 |  |  |  |  |  | X | | | | | X | | X | | X | | | X | | | | | X | X | X | | | | | | X | | | | | | | | | | X | X | X | X | | X | | X | X | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | |
| 9 | 12 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
james-burton/OrientalMuseum_min3-3Dwhite-mat | ---
dataset_info:
features:
- name: obj_num
dtype: string
- name: file
dtype: string
- name: image
dtype: image
- name: root
dtype: string
- name: description
dtype: string
- name: object_name
dtype: string
- name: other_name
dtype: string
- name: label
dtype:
class_label:
names:
'0': Actinolite
'1': Aluminium bronze alloy
'2': Animal Mummy
'3': Batik
'4': Buffalo Horn
'5': Chinese Red Rosewood
'6': Colour on Paper
'7': Flint/Chert
'8': Gouache on Paper
'9': Haematite/Red Ochre
'10': Human Bone
'11': Ink and Colour on Paper
'12': Ink and Colours on Silk
'13': Ink and Opaque Watercolour on Paper
'14': Ink on Paper
'15': Jade (Calcified)
'16': Japanese paper
'17': Microcline/Green Feldspar/Amazon-Stone
'18': Mortar
'19': Nile Mud
'20': Opaque Watercolour and Gilt on Paper
'21': Opaque Watercolour on Paper
'22': Opaque Watercolour or Gouache on Mica
'23': Pith
'24': Pith Paper
'25': Plant Product
'26': Resin/Plastic
'27': Rhinoceros Horn
'28': Rubber
'29': Shell (Ostrich Egg)
'30': Smaragdite
'31': Steatite
'32': Steatite/Soap Stone
'33': Watercolour on Rice Paper
'34': acrylic
'35': agate
'36': alabaster
'37': aluminum
'38': amber
'39': amethyst
'40': antler
'41': artificial stone
'42': balsa
'43': bamboo
'44': basalt
'45': beryl
'46': bone
'47': bowenite
'48': boxwood
'49': brass
'50': brocade
'51': bronze
'52': burnt jade
'53': canvas
'54': cardboard
'55': cards
'56': carnelian
'57': cast iron
'58': celadon
'59': cellulose acetate
'60': ceramic
'61': chalcedony
'62': cherry
'63': clay
'64': cloth
'65': coconut
'66': copper
'67': copper alloy
'68': coral
'69': cotton
'70': crystal
'71': diorite
'72': dolerite
'73': earthenware
'74': ebony
'75': emerald
'76': enamel
'77': faience
'78': felt
'79': flax
'80': flint
'81': fur
'82': gauze
'83': glass
'84': gold
'85': granite
'86': gray ware
'87': hardwood
'88': horn
'89': incense
'90': ink
'91': iron
'92': ivory
'93': jade
'94': jadeite
'95': jasper
'96': jet
'97': lacquer
'98': lapis lazuli
'99': lazurite
'100': lead
'101': lead alloy
'102': leather
'103': limestone
'104': linen
'105': mahogany
'106': malachite
'107': marble
'108': metal
'109': mineral
'110': mother of pearl
'111': muslin
'112': nephrite
'113': nylon
'114': obsidian
'115': organic material
'116': organza
'117': paint
'118': palm fiber
'119': palm leaf
'120': paper
'121': papier mâché
'122': papyrus
'123': parchment
'124': pewter
'125': photographic paper
'126': pine
'127': plant fiber
'128': plaster
'129': plastic
'130': plate
'131': polyester
'132': polystyrene
'133': porcelain
'134': pottery
'135': quartzite
'136': rattan
'137': realgar
'138': reed
'139': rice paper
'140': rock
'141': rush
'142': sandstone
'143': satin
'144': schist
'145': seashell
'146': serpentine
'147': shagreen
'148': shell
'149': silk
'150': siltstone
'151': silver
'152': silver alloy
'153': skull
'154': slate
'155': soapstone
'156': softwood
'157': stalagmites
'158': steel
'159': stone
'160': stoneware
'161': straw
'162': stucco
'163': sycamore
'164': synthetic fiber
'165': teak
'166': terracotta
'167': textiles
'168': tin
'169': tortoise shell
'170': tourmaline
'171': travertine
'172': tremolite
'173': turquoise
'174': vellum
'175': velvet
'176': wood
'177': wool
'178': wrought iron
'179': zinc alloy
- name: production.period
dtype: string
- name: production.place
dtype: string
splits:
- name: validation
num_bytes: 705070081.66
num_examples: 5437
- name: test
num_bytes: 668441888.338
num_examples: 5437
- name: train
num_bytes: 5933902936.6
num_examples: 115525
download_size: 6327412832
dataset_size: 7307414906.598001
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: train
path: data/train-*
---
|
irds/pmc_v1_trec-cds-2015 | ---
pretty_name: '`pmc/v1/trec-cds-2015`'
viewer: false
source_datasets: ['irds/pmc_v1']
task_categories:
- text-retrieval
---
# Dataset Card for `pmc/v1/trec-cds-2015`
The `pmc/v1/trec-cds-2015` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/pmc#pmc/v1/trec-cds-2015).
# Data
This dataset provides:
- `queries` (i.e., topics); count=30
- `qrels`: (relevance assessments); count=37,807
- For `docs`, use [`irds/pmc_v1`](https://huggingface.co/datasets/irds/pmc_v1)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/pmc_v1_trec-cds-2015', 'queries')
for record in queries:
record # {'query_id': ..., 'type': ..., 'description': ..., 'summary': ...}
qrels = load_dataset('irds/pmc_v1_trec-cds-2015', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Roberts2015TrecCds,
title={Overview of the TREC 2015 Clinical Decision Support Track},
author={Kirk Roberts and Matthew S. Simpson and Ellen Voorhees and William R. Hersh},
booktitle={TREC},
year={2015}
}
```
|
arbitropy/quac-full-answer | ---
dataset_info:
features:
- name: story
dtype: string
- name: questions
sequence: string
- name: answers
sequence: string
splits:
- name: train
num_bytes: 34863078
num_examples: 11567
- name: validation
num_bytes: 3317996
num_examples: 1000
download_size: 22795541
dataset_size: 38181074
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
tyranus/trisvoice | ---
license: openrail
---
|
sambhavi/test_data_ft | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: text
dtype: string
- name: prompt
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 73744405.26058757
num_examples: 24392
download_size: 30026671
dataset_size: 73744405.26058757
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/7f004595 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1326
dataset_size: 176
---
# Dataset Card for "7f004595"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/medusa_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of medusa/メデューサ/美杜莎 (Girls' Frontline)
This is the dataset of medusa/メデューサ/美杜莎 (Girls' Frontline), containing 88 images and their tags.
The core tags of this character are `purple_hair, purple_eyes, short_hair, ahoge, breasts, small_breasts, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 88 | 104.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/medusa_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 88 | 61.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/medusa_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 214 | 136.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/medusa_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 88 | 92.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/medusa_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 214 | 188.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/medusa_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/medusa_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | 1girl, blush, egyptian_clothes, solo, usekh_collar, simple_background, white_background, looking_at_viewer, open_mouth, navel, smile, bare_shoulders, armlet, bracelet, earrings, hair_between_eyes, medium_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | egyptian_clothes | solo | usekh_collar | simple_background | white_background | looking_at_viewer | open_mouth | navel | smile | bare_shoulders | armlet | bracelet | earrings | hair_between_eyes | medium_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------------|:-------|:---------------|:--------------------|:-------------------|:--------------------|:-------------|:--------|:--------|:-----------------|:---------|:-----------|:-----------|:--------------------|:-----------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
piyushvpatil/indian_food_images | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': burger
'1': butter_naan
'2': chai
'3': chapati
'4': chole_bhature
'5': dal_makhani
'6': dhokla
'7': fried_rice
'8': idli
'9': jalebi
'10': kaathi_rolls
'11': kadai_paneer
'12': kulfi
'13': masala_dosa
'14': momos
'15': paani_puri
'16': pakode
'17': pav_bhaji
'18': pizza
'19': samosa
splits:
- name: train
num_bytes: 1388776730.8794332
num_examples: 5328
- name: test
num_bytes: 221334398.3925666
num_examples: 941
download_size: 1601361373
dataset_size: 1610111129.2719998
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
kasamajin/tfex_set50 | ---
license: apache-2.0
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 1203380.0
num_examples: 16
- name: validation
num_bytes: 1203380.0
num_examples: 16
- name: test
num_bytes: 1203380.0
num_examples: 16
download_size: 3601869
dataset_size: 3610140.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
nlp-brin-id/triplets-all | ---
license: mit
task_categories:
- text-classification
language:
- id
size_categories:
- 10K<n<100K
---
We create this dataset from nlp-brin-id/id-hoax-report-merge-v2. </br>
In this subset, triplets candidates are described as
Positive sentence A; Positive Sentence B; Negative sentence C. Sampling space are defined as follows:</br>
- [HOAX label] Title Hoax; Title Hoax; Title Non-Hoax
- [HOAX label] Title Hoax; Title Hoax; Content Non-Hoax (if not empty)
- [HOAX label] Title Hoax; Content Hoax (if not empty); Title Non-Hoax -
- [HOAX label] Title Hoax; Content Hoax (if not empty); Content Non-Hoax (if not empty)
- [HOAX label] Title Hoax; Title Hoax; Fact Hoax (if Fact != null),
- [HOAX label] Title Hoax; Content Hoax (if not empty); Fact Hoax (if Fact != null),
- [NON-HOAX label] Title Non-Hoax; Title Non-Hoax; Title Hoax
- [NON-HOAX label] Title Non-Hoax; Title Non-Hoax; Content Hoax
- [NON-HOAX label] Title Non-Hoax; Content Non-Hoax (if not empty); Title Hoax
- [NON-HOAX label] Title Non-Hoax; Content Non-Hoax (if not empty); Content Hoax (if not empty)
- [NON-HOAX label] Title Non-Hoax; Fact Non-Hoax (if not empty); Title Hoax
- [NON-HOAX label] Title Non-Hoax; Fact Non-Hoax (if not empty); Content Hoax (if not empty)
- [NON-HOAX label] Content Non-Hoax (if not empty); Fact Non-Hoax (if not empty); Title Hoax
- [NON-HOAX label] Content Non-Hoax (if not empty); Fact Non-Hoax (if not empty); Content Hoax (if not empty)
For creating the subset, we permute hard negative samples for 10 epochs dependent to the class category. </br>
For each epoch, we flip coins to decide whether the triplet uses 'Title', 'Content' (a long description of claim in Title), or 'Fact'.</br>
Note that: 'Fact' represents hard negative or contradicting sentence in Hoax class samples, while in Non-Hoax subset it represents supports (Positive sentence). </br>
|
indonesian-nlp/mc4-id | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- odc-by
multilinguality:
- monolingual
size_categories:
tiny:
- 1M<n<10M
small:
- 10M<n<100M
medium:
- 10M<n<100M
large:
- 10M<n<100M
full:
- 100M<n<1B
source_datasets:
- extended
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: mc4
pretty_name: mC4-id
---
# Dataset Card for Clean(maybe) Indonesia mC4
## Dataset Description
- **Original Homepage:** [HF Hub](https://huggingface.co/datasets/allenai/c4)
- **Paper:** [ArXiv](https://arxiv.org/abs/1910.10683)
### Dataset Summary
A thoroughly cleaned version of the Indonesia split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the [Common Crawl dataset](https://commoncrawl.org). The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4).
### Data Fields
The data contains the following fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp of extraction as a string
### Data Splits
You can load any subset like this:
```python
from datasets import load_dataset
mc4_id_tiny = load_dataset("munggok/mc4-id", "tiny")
```
Since splits are quite large, you may want to traverse them using the streaming mode available starting from 🤗 Datasets v1.9.0:
```python
from datasets import load_dataset
mc4_id_full_stream = load_dataset("munggok/mc4-id", "full", split='train', streaming=True)
print(next(iter(mc4_id_full_stream))) # Prints the example presented above
```
## Dataset Creation
Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`.
## Considerations for Using the Data
### Discussion of Biases
Despite the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.
## Additional Information
### Dataset Curators
Authors at AllenAI are the original curators for the `mc4` corpus.
### Licensing Information
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
If you use this dataset in your work, please cite us and the original mC4 authors as:
```
@inproceedings{xue-etal-2021-mt5,
title = "m{T}5: A Massively Multilingual Pre-trained Text-to-Text Transformer",
author = "Xue, Linting and
Constant, Noah and
Roberts, Adam and
Kale, Mihir and
Al-Rfou, Rami and
Siddhant, Aditya and
Barua, Aditya and
Raffel, Colin",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.41",
doi = "10.18653/v1/2021.naacl-main.41",
pages = "483--498",
}
```
### Contributions
Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
|
Azma-AI/text_embeddings_dataset | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 240172223
num_examples: 55936
download_size: 198833945
dataset_size: 240172223
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
922-CA/lf2_09122023_test1 | ---
license: openrail
---
# Lora FMG-9 (LLaMA2) 09122023 test 1
* Dataset of FMG-9 dialogue from Girls' Frontline
* Manually edited to turn into multi-turn dialogue |
IanTseng/Med-term2 | ---
dataset_info:
features:
- name: TEXT
dtype: string
- name: LOCATION
dtype: string
- name: LABEL
dtype: string
splits:
- name: train
num_bytes: 4184236883
num_examples: 4000000
download_size: 2366900571
dataset_size: 4184236883
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_240 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1464600952.0
num_examples: 285386
download_size: 1501601848
dataset_size: 1464600952.0
---
# Dataset Card for "chunk_240"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Abdelkareem/zikir_detection | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 52989536
num_examples: 132
download_size: 47486353
dataset_size: 52989536
task_categories:
- audio-classification
language:
- ar
---
# Dataset Card for "zikir_detection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MedIR/roco | ---
dataset_info:
features:
- name: id
dtype: string
- name: semtypes
sequence: string
- name: cuis
sequence: string
- name: caption
dtype: string
- name: image
dtype: image
splits:
- name: test
num_bytes: 170468574.0
num_examples: 8176
download_size: 167802110
dataset_size: 170468574.0
---
# Dataset Card for "roco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.