Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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4,900 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Jeepney', 'New', 'Meta', ':', 'Pwede', 'na', 'musakay', 'tapad', 'sa', 'driver.', ':', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,901 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'traumatic', 'injury', 'maoy', 'usa', 'sa', 'mga', 'rason', 'nganong', 'nasakitan', 'ug', 'nasakitan', 'ang', 'mga', 'bata', ',', 'busa', 'atol', 'sa', 'umaabot', 'nga', 'National', 'Safe', 'Kids', 'Week', ',', 'ang', 'Universal', 'Health', 'Care', 'Act', 'ni', 'Senador', 'Sonny', 'Angara', 'nagsiguro', 'nga', 'ang', 'mga', 'bata', 'ug', 'ilang', 'mga', 'ginikanan', 'ubanan', 'sa', 'pagmintinar', 'sa', 'ilang', 'kaluwasan', 'sa', 'panglawas.', 'Ubos', 'sa', 'Universal', 'Health', 'Care', 'Act', ',', 'ang', 'mga', 'bata', 'makakuha', 'og', 'accessible', 'ug', 'barato', 'nga', 'serbisyo', 'sa', 'panglawas', 'batok', 'sa', 'bisan', 'unsang', 'matang', 'sa', 'kadaot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 7, 8, 8, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,902 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihatagan', 'nimog', 'lato', 'lato', 'imong', 'uyab', ',', 'unya', 'gitoyo', ',', 'ikaw', 'pay', 'gilato-lato', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,903 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DECADE', 'OF', 'BEING', 'ARMY', 'Ang', 'Seoul', 'ug', 'ubang', 'mga', 'siyudad', 'sa', 'tibuok', 'kalibotan', 'nahimong', 'purpura', 'sa', 'pagsaulog', 'sa', '10', 'ka', 'tuig', 'nga', 'anibersaryo', 'sa', 'award-winning', 'nga', 'South', 'Korean', 'boy', 'band', 'nga', 'BTS.', 'June', '13', ',', 'year', '2013', 'dihang', 'naporma', 'ang', 'BTS', 'nga', 'naglakip', 'ni', 'Jungkook', ',', 'V', ',', 'Jimin', ',', 'SUGA', ',', 'Jin', ',', 'RM', ',', 'J-hope', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] | cebuaner |
4,904 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAMI', 'NA', 'LANG', 'ANG', 'I-BASH', 'NIYO'', 'Giingong', 'wala', 'makapugong', 'sa', 'pagka-emosyonal', 'ang', 'aktor', 'ug', 'bag-ong', 'TV', 'Host', 'sa', ''Eat', 'Bulaga', ''', 'nga', 'si', 'Paolo', 'Contis', 'sa', 'episode', 'sa', ''Eat', 'Bulaga', ''', 'karong', 'Sabado', ',', 'Hunyo', '10', ',', 'tungod', 'sa', 'giingong', 'pagpang-bash', 'sa', 'mga', 'netizen', 'sa', 'staff', 'sa', ''Eat', 'Bulaga.', ''', 'Matod', 'ni', 'Paolo', ',', 'ang', 'mga', 'host', 'lang', 'ang', 'angay', 'i-bash', 'ug', 'dili', 'ang', 'ordinaryo', 'ug', 'inosente', 'nga', 'mga', 'kawani', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,905 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-mingaw', 'nako', 'niya', ',', 'unsang', 'wrong', 'sent', 'message', 'idea', 'kaha', ',', 'ang', 'pwedeng', 'i-send', 'para', 'replyan', 'ko', 'niya.', '?', '?', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,906 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'ka', 'bettors', 'ang', 'nakadaog', 'sa', 'jackpot', 'prize', 'sa', 'evening', 'draw', 'sa', 'Grand', 'Lotto', '6', '/', '55', 'sa', 'Philippine', 'Charity', 'Sweepstakes', 'Office', '(', 'PCSO', ')', 'karong', 'Lunes', ',', 'Mayo', '22.', 'Ang', 'winning', 'combination', 'mao', 'ang', '28', '-', '32', '-', '12', '-', '9', '-', '18', '-', '50.', 'Suno', 'sa', 'Philippine', 'Charity', 'Sweepstakes', 'Office', '(', 'PCSO', ')', ',', 'ang', 'duha', 'ka', 'nakadaog', 'nga', 'tiket', 'ginbakal', 'sa', 'Bacoor', ',', 'Cavite', ',', 'kag', 'Iloilo', 'City.', 'Ang', 'duha', 'ka', 'mananaog', 'magbahin', 'sa', '29,700,000', 'nga', 'jackpot', 'prize', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,907 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'trabahante', 'sa', 'miaging', 'buwan', 'nakadawat', 'sa', 'ilang', 'tseke', 'gikan', 'sa', 'DOLE', '7', 'sa', 'Cebu', 'City', 'isip', 'kabahin', 'sa', 'ilang', 'bayad.', 'matod', 'ni', 'DOLE', '7', 'Director', 'Lilia', 'Estillore', 'sa', 'usa', 'ka', 'pamahayag', 'niadtong', 'Lunes', ',', 'Mayo', '22', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,908 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanang', 'kapila', 'namog-'Goodnight', ',', ''', 'pero', 'sige', 'gihapon', 'mo'g', 'istorya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,909 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'bag-o', 'nga', 'co-sponsorship', ',', 'si', 'Senador', 'Sonny', 'Angara', 'mipadayag', 'og', 'paglaum', 'nga', 'ang', 'iyang', 'dekada', 'na', 'nga', 'Magna', 'Carta', 'of', 'Filipino', 'Seafarers', 'Bill', ',', 'uban', 'sa', 'suporta', 'ni', 'Sen.', 'Raffy', 'Tulfo', ',', 'chairman', 'sa', 'Senate', 'Committee', 'on', 'Migrant', 'Workers.', '“Ubos', 'sa', 'pagdumala', 'ni', 'Sen.', 'Tulfo', ',', 'ang', 'Committee', 'on', 'Migrant', 'Workers', 'nagpadayon', 'sa', 'mga', 'paningkamot', 'sa', 'nangaging', 'mga', 'Kongreso', 'sa', 'paghimo', ',', 'sa', 'pag-file', ',', 'ug', 'sa—hinaot', ',', 'sa', 'katapusan—pagpasar', 'sa', 'usa', 'ka', 'lakang', 'nga', 'mag-institutionalize', 'sa', 'mga', 'katungod', 'sa', 'Filipino', 'seafarer', ',', 'nga', 'naglakip', 'sa', 'mga', 'mekanismo', 'sa', 'pagpatuman', 'ug', 'pagpanalipod', 'niini', ',', 'ang', 'paghatag', 'sa', 'ilang', 'compulsory', 'benefits', ',', 'ug', 'ang', 'pagpatuman', 'sa', 'mga', 'sumbanan', 'nga', 'gitakda', 'sa', 'maritime', 'labor', 'convention', 'sa', '2000', 'o', 'MLC', '2006', ',', 'ug', 'sa', 'International', 'Convention', 'on', 'Standards', 'of', 'Training', ',', 'Certification', ',', 'and', 'Watchkeeping', 'for', 'Seafarers', ',', '"', 'matud', 'ni', 'Senador', 'Sonny.', 'Si', 'Senador', 'Sonny', 'usa', 'pa', 'ka', 'kongresista', 'gikan', 'sa', 'Aurora', 'sa', 'una', 'niyang', 'pagpasiugda', 'sa', 'Magna', 'Carta', 'of', 'Filipino', 'Seafarers', ',', 'ug', 'nanghinaot', 'nga', 'maaprobahan', 'kini', 'sa', 'tabang', 'ni', 'Tulfo.', 'Atubangan', 'sa', 'mga', 'kaubang', 'senador', ',', 'gidetalye', 'ni', 'Senador', 'Sonny', 'nga', 'daghang', 'mga', 'Pilipinong', 'marinero', 'ang', 'nakasinati', 'og', 'dili', 'maayong', 'kahimtang', 'sa', 'pagtrabaho', 'ug', 'diskriminasyon', 'sa', 'trabaho.', 'Pinaagi', 'sa', 'iyang', 'balaudnon', ',', 'masiguro', 'ang', 'pagpanalipod', 'sa', 'katungod', 'ug', 'benepisyo', 'sa', 'Pinoy', 'seafarers', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 1, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 7, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0] | cebuaner |
4,910 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Imohang', 'gipagahi', ',', 'unya', 'dili', 'nimo', 'kaunon.', '#', 'FilipinasBisaya'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,911 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['WELCOME', 'TO', 'THE', 'PH', '!', 'Nag-selfie', 'si', 'Samahang', 'Basketbol', 'ng', 'Pilipinas', '(', 'SBP', ')', 'Chairman', 'Sen.', 'Sonny', 'Angara', ',', 'uban', 'sa', 'NBA', 'legend', 'nga', 'si', 'Dirk', 'Nowitzki', ',', 'kinsa', 'naa', 'sa', 'nasud', 'alang', 'sa', 'FIBA', 'World', 'Cup', 'draw', 'karong', 'Abril', '29.', 'Anaa', 'usab', 'sa', 'nasud', 'ang', 'mga', 'bantugang', 'basketball', 'nga', 'sila', 'si', 'Luis', 'Scola', 'ug', 'Yao', 'Ming.', 'Si', 'Nowitzki', ',', 'ang', 'Aleman', 'nga', 'kanhi', 'magdudula', 'sa', 'Dallas', 'Mavericks', ',', 'mao', 'ang', 'tsirman', 'sa', 'FIBA', 'Players', ''', 'Commission', ',', 'samtang', 'ang', 'Argentine', 'Scola', 'mao', 'ang', 'FIBA', 'World', 'Cup', 'Global', 'Ambassador', ',', 'ug', 'ang', 'bantogan', 'nga', 'basketball', 'sa', 'China', 'nga', 'si', 'Yao', 'Ming', 'usa', 'ka', 'miyembro', 'sa', 'FIBA', 'Central', 'Board.', 'Ang', 'Pilipinas', 'usa', 'sa', 'tulo', 'ka', 'mga', 'host', 'nga', 'nasud', 'sa', 'FIBA', 'World', 'Cup', 'karong', 'tuiga.', '#', 'SonnyAngara'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 3, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 7, 1, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 1, 2, 0, 0, 0, 0, 3, 4, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0] | cebuaner |
4,912 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Grabeng', 'init', ',', 'sign', 'na', 'ni', 'nga', 'magdungan', 'ta'g', 'ligo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,913 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'hepe', 'sa', 'Abuyog', 'Police', 'Station', 'nga', 'si', 'Major', 'Luis', 'Hatton', 'niila', 'sa', 'biktima', 'nga', 'si', 'PFC', 'Aries', 'Ampoan', ',', '21', ',', 'sakop', 'sa', '14th', 'Infantry', 'Battalion', '(', 'IB', ')', '.', 'Si', 'Hatton', ',', 'sa', 'pakighinabi', ',', 'niingon', 'nga', 'nagpaabot', 'pa', 'sila', 'sa', 'resulta', 'sa', 'autopsy', 'sa', 'biktima', 'aron', 'masuta', 'ang', 'hinungdan', 'sa', 'kamatayon.', 'Sumala', 'sa', '14th', 'IB', ',', 'si', 'Ampoan', 'nagpahigayon', 'ug', 'monitoring', 'operations', 'kalabot', 'sa', 'gikatahong', 'presensya', 'sa', 'Communist', 'Terrorist', 'Group', '(', 'CTG', ')', 'niadtong', 'Abril', '2.', 'Sukad', 'niadto', ',', 'wala', 'nay', 'kontak', 'ang', '14th', 'IB', 'kang', 'Ampoan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 1, 0] | cebuaner |
4,914 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'bayronon', 'sa', 'business', 'ug', 'tourist', 'visitor', 'visas', '(', 'B1', '/', 'B2', ')', 'ug', 'uban', 'pang', 'non-petition', 'based', 'visas', 'sama', 'sa', 'student', 'ug', 'exchange', 'visitor', 'visa', 'mahimong', '$', '185', '(', 'P10,236', ')', 'sa', 'Mayo', '30', ',', 'gikan', 'sa', 'kanhi', '$', '160', '(', 'P8,853', ')', '.', 'Samtang', ',', 'ang', 'bayad', 'sa', 'pagproseso', 'sa', 'visa', 'alang', 'sa', 'temporaryo', 'nga', 'mga', 'trabahante', 'sa', 'mosunod', 'nga', 'mga', 'kategorya', 'ipasaka', 'ngadto', 'sa', '$', '205', '(', 'P11,343', ')', 'gikan', 'sa', '$', '190', '(', 'P10,513', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,915 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'cash', 'assistance', 'ipadala', 'pinaagi', 'sa', 'City', 'Social', 'Welfare', 'Services', 'office.', 'Nakadawat', 'usab', 'og', 'nutribuns', 'ang', 'mga', 'nasunogan', 'sa', 'maayong', 'kabubut-on', 'sa', 'senador.', 'Samtang', ',', 'ang', 'grupo', 'sa', 'Fraternal', 'of', 'Philippine', 'Eagles', 'sa', 'Brgy', 'nanghatag', 'usab', 'ug', 'financial', 'assistance.', 'Ermita', 'Chairman', 'Mark', 'Rizalde', 'Miral', 'sa', '600', 'ka', 'pamilya', 'sa', 'Mandaue', 'City', 'ug', 'kapin', 'sa', '200', 'ka', 'residente', 'sa', 'Brgy.', 'Hermitage', 'sa', 'samang', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 0, 1, 2, 2, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0] | cebuaner |
4,916 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Magtag-ulan', 'na'g', 'usab', ',', 'wa', 'pa', 'ta', 'gihapon', 'nabasa', 'sa', 'dagat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,917 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'pamahayag', 'ni', 'Senador', 'Sonny', 'Angara', 'karong', 'Miyerkules', ',', 'Abril', '12', ',', 'bahin', 'sa', 'gihimong', 'pagtuon', 'sa', 'Commission', 'on', 'Human', 'Rights', '(', 'CHR', ')', ',', 'nga', 'nag-ingon', 'nga', 'ang', 'mga', 'fresh', 'graduates', '"', 'tend', 'to', 'lack', 'soft', 'skills', '"', 'ug', '"', 'lack', 'job', 'readiness', '.', '"', 'Ang', 'Second', 'Congressional', 'Commission', 'on', 'Education', 'o', 'EDCOM', '2', 'mao', 'ang', 'komisyon', 'nga', 'gitahasan', 'sa', 'pagpahigayon', 'sa', 'nasudnong', 'pagrepaso', 'sa', 'sektor', 'sa', 'edukasyon', 'sa', 'nasud', 'human', 'sa', 'pandemya', 'sa', 'COVID-19', 'nga', 'nakaapekto', 'sa', 'edukasyon', 'sa', 'mga', 'estudyante', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,918 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'National', 'Aeronautics', 'and', 'Space', 'Administration', '(', 'NASA', ')', 'mipaambit', 'sa', 'upat', 'ka', 'gagmay', 'nga', 'mga', 'lawak', 'nga', 'puy-anan', 'sa', 'mga', 'boluntaryong', 'indibidwal', 'nga', 'mopuyo', 'sa', 'planetang', 'Mars', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
4,919 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ingon', 'ni', 'City', 'Mayor', 'Jerry', 'Treñas', 'sa', 'usa', 'ka', 'mensahe', 'sa', 'Viber', 'sa', 'mga', 'miyembro', 'sa', 'media', 'Martes', 'sa', 'gabii.', 'Lakip', 'sa', 'nataptan', 'sa', 'maong', 'sakit', 'si', 'Trenas', 'ug', 'ang', 'iyang', 'asawa', 'nga', 'si', 'Rosalie', 'sa', 'miaging', 'buwan', 'ug', 'nakabalik', 'na', 'sa', 'katungdanan', 'ang', 'mayor', 'karong', 'Martes.', 'miingon', 'si', 'Dr.', 'Roland', 'Jay', 'Fortuna', ',', 'City', 'Health', 'Office', 'assistant', 'department', 'head', ',', 'nga', 'sugod', 'Enero', 'hangtod', 'Marso', 'ning', 'tuiga', ',', 'usa', 'lang', 'ka', 'kaso', 'sa', 'Covid-19', 'ang', 'natala', 'sa', 'dakbayan', 'sa', 'Iloilo', 'kada', 'adlaw.', 'Bisan', 'pa', ',', 'ang', 'gidaghanon', 'misaka', 'sa', 'upat', 'sukad', 'sa', 'pagsugod', 'sa', 'Abril.', 'dugang', 'niya.', 'Ang', 'minimum', 'nga', 'mga', 'sumbanan', 'sa', 'panglawas', 'sa', 'publiko', 'naglakip', 'sa', 'physical', 'distancing', ',', 'hand', 'hygiene', ',', 'cough', 'etiquette', ',', 'ug', 'pagsul-ob', 'og', 'maskara', ',', 'bisan', 'unsa', 'pa', 'ang', 'kahimtang', 'sa', 'pagbakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,920 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mohimo', 'og', 'pahibalo', 'ang', 'Malacañang', 'sa', 'mosunod', 'nga', 'mga', 'adlaw', ',', 'ang', 'Presidential', 'Communications', 'Office', '(', 'PCO', ')', 'nagkanayon', 'karong', 'Huwebes', ',', 'Abril', '13.', 'Iaanunsiyo', 'ng', 'Malacañang', 'sa', 'mga', 'susunod', 'na', 'araw', ',', 'sinabi', 'ng', 'Presidential', 'Communications', 'Office', '(', 'PCO', ')', 'ngayong', 'Huwebes', ',', 'Abril', '13', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0] | cebuaner |
4,921 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bacolod', 'City', 'Councilor', 'Jason', 'Villarosa', ',', 'chairman', 'sa', 'komitiba', 'sa', 'turismo', 'sa', 'Sangguniang', 'Panlungsod', ',', 'niingon', 'nga', 'una', 'na', 'nilang', 'giaprobahan', 'ang', 'ordinansa', 'nga', 'nag-amendar', 'sa', 'City', 'Ordinance', 'No.', '847', ',', 'nga', 'nag-amendar', 'sa', 'CO', '482', ',', 'o', '"', 'an', 'ordinance', 'establishing', 'yearly', 'festivities', 'to', 'be', 'known', 'as', 'the', 'Manokan', 'Country', 'Inasal', 'Festival', 'in', 'the', 'City', 'of', 'Bacolod', ',', '"', 'Matod', 'niya', 'nga', 'ang', 'selebrasyon', 'adto', 'ipahigayon', 'sa', 'Upper', 'East', 'ug', 'North', 'Capitol', 'Roads', ',', 'ug', 'midugang', 'nga', 'ang', 'siyudad', 'nigahin', 'na', 'og', 'P1', 'milyones', 'alang', 'sa', 'selebrasyon', 'ug', 'mangayo', 'sila', 'og', 'dugang', 'budget', 'alang', 'sa', 'tulo', 'ka', 'adlaw', 'nga', 'selebrasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 7, 8, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,922 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naa', 'koy', 'kaila', 'nga', 'morag', 'motivational', 'speaker', 'sa', 'Facebook', ',', 'pero', 'judgmental', 'in', 'real', 'life', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,923 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Trending', 'sa', 'Twitter', 'ang', 'close-up', 'photo', 'sa', 'aktor', 'nga', 'si', 'Dingdong', 'Dantes', 'ug', 'BTS', 'member', 'nga', 'si', 'Jungkook.', 'Kini', 'ang', 'hulagway', 'ni', 'Dingdong', 'nga', 'na-brief', 'lang', 'sa', 'Bench', 'ad', 'niadtong', '2008', 'ug', 'ang', 'hulagway', 'ni', 'Jungkook', 'kay', 'para', 'kang', 'Calvin', 'Klein', ',', 'parehas', 'ang', 'posing', 'sa', 'duha', 'maong', 'nalingaw', 'niini', 'ang', 'mga', 'netizen.', 'Ang', 'mga', 'netizen', 'nihatag', 'ug', 'good', 'vibes', 'comments', ':'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,924 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Adunay', 'gibana-bana', 'nga', '97', 'ka', 'mga', 'pamilya', ',', 'nga', 'gilangkuban', 'sa', '335', 'ka', 'mga', 'indibidwal', ',', 'nga', 'makadawat', 'sa', 'pinansyal', 'nga', 'tabang.', 'Ang', 'mga', 'tag-iya', 'sa', 'balay', 'makadawat', 'og', 'P10,000', ',', 'samtang', 'ang', 'mga', 'nag-abang', 'makadawat', 'og', 'P5,000', ',', 'karong', 'umaabot', 'nga', 'Biyernes', ',', 'Abril', '14', ',', 'sa', 'Banilad', 'National', 'High', 'School.', 'Samtang', ',', 'sa', 'Sabado', ',', 'Abril', '15', ',', '2023', ',', 'ang', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', 'Region', '7', 'mag-apod-apod', 'sa', 'mga', 'pakete', 'sa', 'pagkaon', 'sa', 'mga', 'pamilya.', 'Si', 'Basaca', 'niingon', 'nga', 'dunay', 'mga', 'pamilya', 'nga', 'nibalik', 'sa', 'lugar', ',', 'samtang', 'ang', 'uban', 'anaa', 'pa', 'sa', 'mga', 'evacuation', 'site.', 'Niadtong', 'Abril', '9', ',', 'usa', 'ka', 'insidente', 'sa', 'sunog', 'ang', 'nahitabo', 'sa', 'Sitio', 'Orel', ',', 'Banilad', ',', 'Mandaue', ',', 'nga', 'naka-apekto', 'sa', '50', 'ka', 'mga', 'balay..'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,925 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hala', 'ka', ',', 'ikaw', 'ba', 'ni', 'Vivian', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,926 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Evening', 'Prayer', '|', 'Abril', '13', ',', '2023', 'Lord', ',', 'salamat', 'kaayo', 'sa', 'tanang', 'grasya', 'nga', 'imong', 'gihatag.', 'Salamat', 'usab', 'sa', 'tibuok', 'adlaw', ',', 'hinaut', 'nga', 'bantayan', 'mo', 'kami', 'karong', 'gabi-i.', 'Makamata', 'mi', 'nga', 'naay', 'panibag-ong', 'kusog', 'og', 'panibag-ong', 'paglaum.', 'Sa', 'pangalan', 'ni', 'Hesus', '...', 'Amen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
4,927 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mandaue', 'City', 'Public', 'Employment', 'Service', 'Office', '(', 'PESO', ')', 'Chief', 'Musoline', 'Suliva', 'mitug-an', 'sa', 'mga', 'tigbalita', 'karong', 'Martes', ',', 'Abril', '11', ',', 'nga', 'ang', 'job', 'fair', 'ipahigayon', 'sa', 'Mandaue', 'City', 'Sports', 'and', 'Cultural', 'Complex', 'gikan', 'sa', 'alas', '8:00', 'sa', 'buntag', 'hangtod', 'sa', 'alas', '5:00', 'sa', 'hapon.', 'Matod', 'ni', 'Suliva', 'nga', 'ilang', 'gipaabot', 'nga', 'liboan', 'ka', 'mga', 'aplikante', 'ang', 'modagsa', 'sa', 'job', 'fair', 'tungod', 'kay', '33', 'ka', 'lokal', 'nga', 'trabaho', 'ug', 'upat', 'ka', 'langyaw', 'nga', 'kompanya', 'ang', 'nipadayag', 'og', 'interes', 'nga', 'mosalmot', 'sa', 'job', 'fair.', 'Lakip', 'sa', 'mga', 'bakante', ',', 'matod', 'niya', ',', 'mao', 'ang', 'mga', 'trabahante', 'sa', 'produksiyon', 'ug', 'mall', 'workers', ',', 'nurses', ',', 'panday', ',', 'call', 'center', 'agents', ',', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,928 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['OIL', 'INTERCEPTORS', 'SA', 'CCLEX', 'Ang', 'Metro', 'Pacific', 'Tollways', 'Corporation', 'nagbutang', 'ug', '13', 'ka', 'oil', 'interceptors', 'sa', 'Cebu-Cordova', 'Link', 'Expressway.', 'Nagkolekta', 'kini', 'og', 'grasa', 'ug', 'lana', 'gikan', 'sa', 'sakyanan', 'nga', 'naula', 'o', 'naanod', 'sa', 'ulan.', 'Ilang', 'kolektahon', 'ang', 'grasa', 'ug', 'lana', 'sa', 'sakyanan', 'nga', 'nayabo', 'sa', 'karsada', 'ug', 'naanod', 'sa', 'ulan.', 'Ang', 'bertikal', 'nga', 'profile', 'sa', 'CCLEX', 'gidesinyo', 'aron', 'ang', 'tubig-ulan', 'modagayday', 'direkta', 'ngadto', 'niining', 'mga', 'oil', 'interceptor.', 'Ang', 'mga', 'interceptor', 'dayon', 'ibulag', 'ang', 'lana', 'gikan', 'sa', 'tubig.', 'Ang', 'limpyo', 'nga', 'tubig', 'gilabay', 'sa', 'dagat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,929 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'personahe', 'sa', 'Bureau', 'of', 'Fire', 'Protection', 'nagpahigayon', 'og', 'imbestigasyon', 'sa', 'sunog', 'nga', 'nahitabo', 'sa', 'merkado', 'publiko', 'sa', 'Barangay', 'Cabacungan', 'sa', 'lungsod', 'sa', 'La', 'Castellana', ',', 'Negros', 'Occidental', 'Lunes', ',', 'Abril', '10', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
4,930 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'sa', 'Metro', 'Pacific', 'Tollways', 'Corporation', '(', 'MPTC', ')', 'nga', 'gidili', 'ang', 'bisan', 'unsang', 'matang', 'sa', 'dula', ',', 'sama', 'sa', 'badminton', ',', 'sa', 'North', 'Luzon', 'Expressway', '(', 'NLEX', ')', ',', 'nunot', 'sa', 'viral', 'video', 'sa', 'duha', 'ka', 'motorista', 'nga', 'miduwa', 'og', 'badminton', 'human', 'sa', 'pipila', 'ka', 'minuto', 'nga', 'natanggong', 'sa', 'trapiko.', '.', 'Gipasabot', 'sa', 'MPTC', 'nga', 'ang', 'trapiko', 'sa', 'maong', 'bahin', 'sa', 'NLEX', 'tinuyo', 'nga', 'grabe', 'niadtong', 'Abril', '9', 'tungod', 'kay', 'adunay', 'nagdilaab', 'nga', 'tank', 'truck', 'sa', 'Candaba', 'Viaduct.', 'Sa', 'opisyal', 'nga', 'pamahayag', 'sa', 'MPTC', ',', 'nagduwa', 'og', 'badminton', 'ang', 'duha', 'ka', 'motorista.', 'dugang', 'pa', 'ng', 'MPTC', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
4,931 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Abel', 'James', 'Monteagudo', ',', 'regional', 'director', 'sa', 'Department', 'Agriculture-Davao', ',', 'niingon', 'nga', 'ang', 'farm', 'gate', 'prices', 'sa', 'bugas', 'sa', 'rehiyon', 'niabot', 'sa', 'P18', 'matag', 'kilo', '(', 'kg', ')', ',', 'usbaw', 'sa', 'P2', 'gikan', 'sa', 'naandang', 'P15', 'ngadto', 'sa', 'P16', '/', 'kg', 'sa', 'miaging', 'tuig.', 'sumala', 'ni', 'Monteagudo.', 'Apan', 'matud', 'pa', 'ni', 'Monteagudo', 'nga', 'aron', 'mapaus-os', 'ang', 'presyo', 'sa', 'bugas', ',', 'sumala', 'sa', 'gisaad', 'ni', 'DA', 'secretary', 'President', 'Ferdinand', '“Bongbong”', 'Marcos', 'Jr.', ',', 'kinahanglang', 'mas', 'barato', 'ang', 'gasto', 'sa', 'produksyon', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 2, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,932 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tabangi', 'ko', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0] | cebuaner |
4,933 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Napulog', 'pito', 'ka', 'mga', 'konsehal', 'sa', 'Davao', 'City', 'ang', 'nibotar', 'og', '"', 'yes', '"', 'sa', 'Anti-Bullying', 'Bill', 'ni', 'Konsehal', 'Enzo', 'Villafuerte', ',', 'duha', 'ang', 'mibotar', 'og', '"', 'no', ',', '"', 'samtang', 'ang', 'usa', 'ni-abstain', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,934 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UNSAY', 'NAA', '?', 'Sa', 'Instagram', 'Story', 'ni', '2022', 'Miss', 'Universe', 'Philippines', 'Celeste', 'Cortesi', 'karong', 'Martes', ',', 'Abril', '11', ',', 'iyang', 'gipaambit', 'ang', 'iyang', 'litrato', 'sa', 'Siargaoi', 'kauban', 'ang', 'basketball', 'player', 'nga', 'si', 'Kobe', 'Paras', 'ug', 'laing', 'duha', 'ka', 'lalaki', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 0, 0, 0, 3, 4, 4, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0] | cebuaner |
4,935 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TRIGGER', 'WARNING', ':', 'RAPE', 'SLAY', 'Makaluluoy', 'ang', 'nahitabo', 'sa', 'usa', 'ka', '10-anyos', 'nga', 'batang', 'babaye', ',', 'kinsa', 'napalgang', 'patay', 'ug', 'walay', 'underwear', 'sa', 'punoan', 'sa', 'saging', 'sa', 'Barangay', 'Dalagdag', ',', 'Calinan', 'District', ',', 'Davao', 'City.', 'Sa', 'report', 'sa', 'One', 'Mindanao', 'sa', 'GMA', 'Regional', 'TV', ',', 'nasikop', 'sa', 'kapulisan', 'ang', 'suspek', ',', 'kinsa', 'giingong', 'nitug-an', 'sa', 'paglugos', 'ug', 'pagpatay', 'sa', 'bata', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 3, 4, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,936 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Sen.', 'Ronald', ''Bato', ''', 'dela', 'Rosa', 'karong', 'Huwebes', ',', 'Abril', '13', ',', 'nga', 'gisuspenso', 'si', 'Negros', 'Oriental', '3rd', 'District', 'Rep.', 'Arnolfo', 'Teves', 'Jr.', 'nga', 'mag-atubang', '"', 'virtually', '"', 'sa', 'imbestigasyon', 'sa', 'iyang', 'komite', 'sunod', 'semana', 'kalabot', 'sa', 'pagpatay', 'sa', 'mga', 'piniling', 'opisyal', ',', 'apil', 'na', 'si', 'kanhi', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo.', 'Una', 'nang', 'gikompirmar', 'ni', 'Justice', 'Secretary', 'Boying', 'Remulla', 'nga', 'si', 'Teves', 'maoy', 'usa', 'sa', 'gikonsiderar', 'nga', 'utok', 'sa', 'pagpatay', 'kang', 'Degamo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,937 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Non-bailable', 'ang', 'kasong', 'pagpatay', 'nga', 'giatubang', 'nila', 'ni', 'kanhi', 'BuCor', 'Chief', 'Gerald', 'Bantag', 'ug', 'kanhi', 'deputy', 'Ricardo', 'Zulueta', 'sa', 'Muntinlupa', 'City', 'Regional', 'Trial', 'Court', '(', 'RTC', ')', 'Branch', '206', ',', 'busa', 'sa', 'higayon', 'nga', 'madakpan', 'ug', 'mapriso', ',', 'dili', 'na', 'temporaryong', 'makagawas', 'ang', 'duha', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0, 0, 0, 1, 2, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,938 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Muntinlupa', 'Regional', 'Trial', 'Court', 'Branch', '206', 'nimando', 'usab', 'sa', 'pagdakop', 'kang', 'kanhi', 'Bureau', 'of', 'Corrections', '(', 'BuCor', ')', 'deputy', 'for', 'security', 'Ricardo', 'Zulueta', 'sa', 'samang', 'kaso.', 'Walay', 'piyansa', 'nga', 'girekomendar', 'nilang', 'Zulueta', 'ug', 'kanhi', 'BuCor', 'Chief', 'Gerald', 'Bantag', 'tungod', 'kay', 'non-bailable', 'ang', 'kasong', 'pagpatay.', 'Dili', 'makompirmar', 'sa', 'media', 'kon', 'duna', 'bay', 'warrant', 'of', 'arrest', 'sila', 'si', 'Bantag', 'ug', 'Zulueta', 'sa', 'managlahing', 'kaso', 'nga', 'ilang', 'giatubang', 'sa', 'korte', 'sa', 'Las', 'Piñas', 'City', 'kalabot', 'sa', 'pagpatay', 'sa', 'beteranong', 'broadcaster', 'nga', 'si', 'Percy', 'Lapid', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,939 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Malipayon', 'ang', 'fans', 'ni', 'Bea', 'Alonzo', 'sa', 'bonding', 'nila', 'ni', 'Heart', 'Evangelista.', 'Si', 'Bea', 'ug', 'Heart', 'bag-o', 'lang', 'nagkita', 'og', 'balik', 'sa', 'pipila', 'nila', 'ka', 'close', 'showbiz', 'friends', 'nga', 'sila', 'si', 'Diether', 'Ocampo', ',', 'Jericho', 'Rosales', ',', 'John', 'Lloyd', 'Cruz', ',', 'Piolo', 'Pascual', ',', 'Maja', 'Salvador', ',', 'ug', 'Kyle', 'Echarri', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 0, 1, 2, 0] | cebuaner |
4,940 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Love', 'kuno', 'ko', 'niya', ',', 'kalit', 'nalang', 'ka-cold', ',', 'unsa', 'na', 'climate', 'change', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,941 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingon', 'sa', 'ACT', 'kaniadtong', 'Lunes', ',', 'Abril', '10', 'nga', 'kini', 'nga', 'sugyot', 'nagsiguro', 'sa', 'duha', 'ka', 'bulan', 'nga', 'bakasyon', 'sa', 'eskuylahan', 'alang', 'sa', 'mga', 'magtutudlo', 'ug', 'mga', 'estudyante', 'nga', 'gihikawan', 'sa', 'igong', 'pahuway', 'ug', 'oras', 'aron', 'makabangon', 'gikan', 'sa', 'makahahadlok', 'nga', 'trabaho', 'sa', 'miaging', 'mga', 'tuig', 'sa', 'pagtungha.', 'Ang', 'ACT', 'midugang', 'nga', 'ang', 'matag', 'school', 'year', 'motapos', 'sa', 'duha', 'ngadto', 'sa', 'tulo', 'ka', 'semana', 'nga', 'mas', 'sayo', 'kay', 'sa', 'naandan', 'nga', 'eskedyul', 'ug', 'ibalik', 'ang', 'Abril', 'ug', 'Mayo', 'nga', 'school', 'holidays', 'human', 'sa', 'lima', 'ka', 'tuig.', 'ingon', 'sa', 'ACT', 'chairperson', 'Vladimer', 'Quetua'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2] | cebuaner |
4,942 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Evening', 'Prayer', '|', 'Abril', '12', ',', '2023', 'Lord', ',', 'salamat', 'kaayo', 'sa', 'tanang', 'grasya', 'nga', 'imong', 'gihatag.', 'Salamat', 'usab', 'sa', 'tibuok', 'adlaw', ',', 'hinaut', 'nga', 'bantayan', 'mo', 'kami', 'karong', 'gabi-i.', 'Makamata', 'mi', 'nga', 'naay', 'panibag-ong', 'kusog', 'og', 'panibag-ong', 'paglaum.', 'Sa', 'pangalan', 'ni', 'Hesus', '...', 'Amen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
4,943 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'kay', 'hapit', 'na', 'mabutang', 'sa', 'peligro', 'ang', 'Tukomi', 'pranksters', ',', 'giawhag', 'sa', 'Davao', 'City', 'Public', 'Safety', 'and', 'Security', 'Office', '(', 'PSSO', ')', 'ang', 'publiko', 'nga', 'likayan', 'ang', 'mga', 'komedya', 'o', 'mga', 'prank', 'nga', 'makadaot', 'sa', 'ilang', 'kaluwasan', ',', '"', 'So', 'kini', 'unta', 'ba', 'maging', 'responsible', 'ang', 'mga', 'nagahimo', 'ani', '?', '(', 'I', 'hope', 'these', 'people', 'will', 'be', 'responsible', 'for', 'their', 'actions', ')', ',', '”', 'matod', 'ni', 'PSSO', 'Head', 'Angel', 'Sumagaysay', 'sa', 'pakighinabi', 'sa', 'SunStar', 'Davao', 'niadtong', 'Abril', '9', ',', '2023.', 'Matod', 'niya', ',', 'dili', 'kini', 'ang', 'unang', 'higayon', 'nga', 'gibinuangan', 'sa', 'social', 'media', 'influencer', 'ang', 'usa', 'ka', 'indibidwal', 'nga', 'naglambigit', 'sa', 'peace', 'and', 'order', 'personnel', ',', 'ug', 'adunay', 'insidente', 'nga', 'nasakitan', 'ang', 'maong', 'prankster', 'human', 'gidepensahan', 'sa', 'prankster', 'ang', 'iyang', 'kaugalingon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,944 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'post', 'sa', 'Facebook', 'nagpakita', 'sa', 'litrato', 'sa', 'babaye', 'uban', 'sa', 'iyang', 'ex-boyfriend', ',', 'nga', 'grayscaled—bisan', 'dili', 'patay—sama', 'sa', 'uso', 'karon.', 'Sa', 'iyang', 'taas', 'nga', 'post', ',', 'gidetalye', 'sa', 'babaye', 'nga', 'lima', 'na', 'ka', 'tuig', 'ang', 'iyang', 'relasyon', 'sa', 'iyang', 'ex', 'ug', 'mabdos', 'sa', 'ilang', 'unang', 'anak', 'dihang', 'nahibaw-an', 'ang', 'giingong', 'pagpangilad', 'niini.', 'ingon', 'sa', 'babaye', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,945 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Lapulapu', 'City', 'Councilor', 'Annabeth', 'Cuizon', ',', 'chairperson', 'sa', 'Committee', 'on', 'Social', 'Services', ',', 'niingon', 'nga', 'ang', 'educational', 'assistance', 'nga', 'gipang-apod-apod', 'gikan', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', '-Region', '7', ',', 'pinaagi', 'ni', 'Senador', 'Risa', 'Hontiveros.', 'Gawas', 'pa', ',', 'niingon', 'si', 'Cuizon', 'nga', 'aduna', 'usab', 'educational', 'assistance', 'si', 'Lapulapu', 'City', 'Rep.', 'Cindi', 'Chan', 'nga', 'makabenepisyo', 'ang', 'mga', 'Oponganon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 7, 0] | cebuaner |
4,946 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidudahan', 'nga', 'selos', 'ang', 'hinungdan', 'sa', 'pag-atake', 'sa', 'suspek', ',', 'nadestino', 'sa', 'Magpet', 'Municipal', 'Police', ',', 'sa', 'iyang', 'kauban', 'karong', 'Martes', 'sa', 'buntag', ',', 'Abril', '11', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,947 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'kuha', 'sa', 'CCTV', ',', 'makita', 'nga', 'niagi', 'ang', 'saag', 'nga', 'iro', 'luyo', 'sa', 'ginikanan', 'sa', 'bata', 'nga', 'parehong', 'nag-duty', 'sa', 'gasolinahan.', 'Midiretso', 'ang', 'iro', 'sa', 'kwarto', 'sa', 'kilid', 'sa', 'gasolinahan', 'diin', 'natulog', 'ang', 'bata', 'ug', 'gipaak.', 'Gidala', 'sa', 'tambalanan', 'ang', 'bata', 'apan', 'namatay', 'kini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,948 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'report', 'sa', 'kapulisan', ',', 'ang', 'unom', 'ka', 'managhigala', 'gikan', 'sa', 'piknik', 'paingon', 'na', 'untang', 'mopauli', 'dihang', 'mibundak', 'ang', 'kusog', 'nga', 'uwan', 'hinungdan', 'nga', 'mipasilong', 'sila', 'apan', 'naigo', 'kini', 'sa', 'kusog', 'nga', 'kilat', 'samtang', 'nagkupot', 'sa', 'ilang', 'mga', 'cellphone', ',', 'mga', 'alas', '6:00', 'sa', 'gabii', 'niadtong', 'Abril', '9.', 'Upat', 'ang', 'dead', 'on', 'the', 'spot', ',', 'samtang', 'ang', 'usa', 'wala', 'na', 'makaabot', 'sa', 'tambalanan', 'ug', 'na-suffocate', 'atol', 'sa', 'biyahe.', 'Ang', 'nag-inusarang', 'survivor', 'nagpaalim', 'sa', 'provincial', 'hospital', 'sa', 'Digos', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
4,949 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nganong', 'mangita', 'paman', 'ko'g', 'mag', 'pakatawa', 'nako', ',', 'nga', 'mo', 'katawa', 'raman', 'ko'g', 'ako', 'ra', 'isa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,950 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Amerika', 'ug', 'Pilipinas', 'nagsugod', 'karong', 'Martes', ',', 'Abril', '11', ',', 'ang', 'kinadak-ang', 'combat', 'exercises', 'tali', 'sa', 'duha', 'ka', 'nasod', ',', 'nga', 'kabahin', 'sa', 'tinuig', 'nga', 'pagbansay', 'sa', 'dugay', 'nang', 'mga', 'kaalyado', 'sa', 'tratado', ',', 'ang', 'Balikatan', 'Exercises', ',', 'nga', 'molungtad', 'hangtod', 'Abril', '28', ',', 'ug', 'moapil', 'sa', 'kapin', '17,600', 'nga', 'mga', 'sundalo', 'sa', 'duha', 'ka', 'nasod', '.', 'Mao', 'kini', 'ang', 'pinakabag-o', 'nga', 'pagpakita', 'sa', 'kusog', 'sa', 'kalayo', 'sa', 'Amerika', 'sa', 'Asya', ',', 'diin', 'balik-balik', 'nga', 'gipasidan-an', 'sa', 'Washington', 'ang', 'China', 'sa', 'nagkadaghang', 'agresibo', 'nga', 'mga', 'aksyon', 'niini', 'sa', 'gikaaway', 'nga', 'agianan', 'sa', 'dagat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,951 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SAKTO', '!', 'Nalipay', 'ang', 'mga', 'netizen', 'sa', 'pamahayag', 'sa', 'Kapamilya', 'actress', 'nga', 'si', 'Belle', 'Mariano', 'bahin', 'sa', 'paglungtad', 'sa', 'mga', 'bashers', 'ug', 'haters', 'nunot', 'sa', 'komento', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Valentine', 'Rosales', 'nga', 'nagkanayon', 'nga', 'dili', 'gwapa', 'si', 'Belle', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,952 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Talisay', 'City', 'Mayor', 'Gerald', 'Anthony', ''Samsam', ''', 'Gullas', 'niingon', 'nga', 'bisan', 'pa', 'sa', 'kamatuoran', 'nga', 'ang', 'Metro', 'Cebu', 'dili', 'direktang', 'maapektuhan', 'sa', 'tropical', 'cyclone', ',', 'ang', 'kagamhanan', 'sa', 'dakbayan', 'motutok', 'sa', 'mga', 'dapit', 'sa', 'dakbayan', 'nga', 'daling', 'mabahaan', ',', 'nga', 'niingon', 'nga', 'si', ''Amang', ''', 'mahimong', 'magdala', 'kusog', 'nga', 'ulan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 6, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
4,953 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagplano', 'mi', 'nga', 'mag-swimming', ',', 'pero', 'wa', 'mi', 'ka-plano', 'unsaon', 'pagdayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,954 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Opisyal', 'nga', 'gianunsyo', 'sa', '19-anyos', 'nga', 'British', 'actress', 'ni', 'Millie', 'Bobby', 'Brown', 'sa', 'Instagram', 'ang', 'iyang', 'engagement', 'ngadto', 'kang', 'Jake', 'Bongiovi', ',', '20', ',', 'pinaagi', 'sa', 'lyrics', 'sa', 'kanta', 'ni', 'Taylor', 'Swift', 'nga', ''Lover'.', 'Nailhan', 'si', 'Millie', 'sa', 'iyang', 'karakter', 'sa', 'serye', 'sa', 'Netflix', 'nga', ''Stranger', 'Things', ''', ',', 'samtang', 'si', 'Jake', 'anak', 'sa', 'rocker', 'nga', 'si', 'Bon', 'Jovi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 7, 0, 0, 1, 2, 2, 0, 7, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 7, 0, 7, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,955 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LOOK', ':', 'Sa', 'pagkakaron', 'adunay', 'sunog', 'nga', 'nahitabo', 'sa', 'Bagua', '1', '(', 'Manday', 'area', ')', ',', 'Cotabato', 'City.', 'Padayon', 'nga', 'gipalong', 'sa', 'BFP', 'Cotabato', 'ang', 'sunog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 3, 4, 0, 0, 0] | cebuaner |
4,956 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Trending', 'ang', 'post', 'sa', 'Facebook', 'user', 'nga', 'si', 'Pinky', 'Uriarte', 'nga', 'nagpakita', 'sa', 'video', 'sa', 'usa', 'ka', 'ginang', 'nga', 'gitabangan', 'sa', 'pagpanganak', 'sa', 'bata', 'sa', 'cottage', 'sa', 'usa', 'ka', 'beach', 'resort', 'sa', 'Cabadbaran', 'City', ',', 'Agusan', 'del', 'Norte.', 'Makita', 'sa', 'video', 'nga', 'wala', 'nagginhawa', 'ang', 'bata', 'sa', 'dihang', 'gipanganak', 'kini', 'sa', 'ginang', ',', 'apan', 'pagkadugayan', 'daw', 'milagro', 'nga', 'nabuhi', 'ang', 'bata', 'tungod', 'sa', 'paningkamot', 'sa', 'midwife', 'nga', 'pahilakon', 'ang', 'bata.', 'Welcome', 'sa', 'kalibutan', ',', 'baby', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,957 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisurpresa', 'sa', 'aktres', 'nga', 'si', 'Kiray', 'Celis', 'ang', 'iyang', 'inahan', 'sa', 'iyang', 'adlawng', 'natawhan', 'og', 'P1', 'Million.', 'Suma', 'pa', 'sa', 'aktres', ',', 'gisaad', 'niya', 'kini', 'sa', 'inahan', 'sa', 'iyang', 'igsuon', ',', 'pero', 'sa', 'Hunyo', 'pa', 'kuno', 'niya', 'kini', 'mahatag', ',', 'pero', 'tungod', 'sa', 'blessing', 'sa', 'magulang', ',', 'napaaga', 'kini', ',', 'maong', 'nalipay', 'siya', 'ug', 'nagpasalamat', 'sa', 'Diyos.', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,958 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'advisory', 'sa', 'Municipal', 'Environment', 'and', 'Natural', 'Resources', 'Office', '(', 'MENRO', ')', 'sa', 'Balamban', ',', 'Cebu', ',', 'sugod', 'Mayo', '2', ',', 'gidili', 'ang', 'paggamit', 'og', 'plastic', 'bags', 'sa', 'tanang', 'negosyo', ',', 'apil', 'na', 'ang', 'grocery', 'ug', 'variety', 'stores', ',', 'subay', 'sa', 'Ecological', 'Solid', 'Waste', 'Management', 'Act', 'o', 'RA.', '9003.', 'Ang', 'MENRO', 'nag-awhag', 'sa', 'mga', 'kustomer', 'ug', 'mga', 'vendor', 'sa', 'pagsagop', 'sa', 'eco-friendly', 'o', 'reusable', 'nga', 'mga', 'bag', 'ug', 'uban', 'pang', 'alternatibo', 'sa', 'pagtipig', 'sa', 'pipila', 'ka', 'mga', 'gipamalit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 7, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,959 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Evening', 'Prayer', '|', 'Abril', '11', ',', '2023', 'Lord', ',', 'salamat', 'kaayo', 'sa', 'tanang', 'grasya', 'nga', 'imong', 'gihatag.', 'Salamat', 'usab', 'sa', 'pagpahulay', ',', 'hinaut', 'nga', 'bantayan', 'mo', 'kami', 'karong', 'gabi-i.', 'Makamata', 'mi', 'nga', 'naay', 'panibag-ong', 'kusog', 'og', 'panibag-ong', 'paglaum.', 'Sa', 'pangalan', 'ni', 'Hesus', '...', 'Amen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
4,960 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unsay', 'sign', 'nga', 'Rich', 'Kid', 'imong', 'ka', 'work', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,961 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Davao', 'City', 'Public', 'Safety', 'and', 'Security', 'Office', 'nagkanayon', 'nga', 'sa', 'kinatibuk-an', 'malinawon', 'ang', 'selebrasyon', 'sa', 'Semana', 'Santa', 'sa', 'siyudad.', 'Suno', 'kay', 'Angel', 'Sumagaysay', ',', 'may', 'kabilugan', 'nga', '20,000', 'ka', 'security', 'cluster', 'personnel', 'ang', 'gindeploy', 'sugod', 'Abril', '6', 'tubtob', 'Abril', '9', 'para', 'masiguro', 'ang', 'kaluwasan', 'sang', 'mga', 'Dabawenyo.', 'Matod', 'ni', 'Sumagaysay', 'nga', 'ila', 'usab', 'nga', 'gi-secure', 'ug', 'gimonitor', 'ang', 'kinatibuk-ang', '34', 'ka', 'simbahan', 'sa', 'dakbayan', 'lakip', 'na', 'ang', 'Paquibato', 'District', 'ug', 'Marilog', 'District.', 'Dugang', 'pa', 'niya', 'nga', 'giapil', 'usab', 'nila', 'ang', 'mga', 'mosque', 'sa', 'dakbayan', 'tungod', 'kay', 'nagsaulog', 'man', 'ang', 'Muslim', 'community', 'sa', 'ramadan.', 'Gawas', 'sa', 'mga', 'simbahan', 'ug', 'mosque', ',', 'siya', 'miingon', 'nga', 'sila', 'usab', 'nagbantay', 'sa', 'mga', 'bus', 'terminal', ',', 'airport', ',', 'ug', 'mga', 'pantalan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,962 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'biktima', 'giila', 'nga', 'si', 'Alvin', 'Guia', ',', 'nagtrabaho', 'isip', 'production', 'worker', 'sa', 'Mandaue', 'City.', 'Sa', 'report', 'sa', 'kapulisan', ',', 'ang', 'iyang', 'kauban', 'sa', 'trabaho', 'miadto', 'kang', 'Guia', 'sa', 'dihang', 'wala', 'kini', 'mosulod', 'niadtong', 'Martes', ',', 'Abril', '11', ',', 'apan', 'nakurat', 'ang', 'kauban', 'sa', 'trabaho', 'sa', 'pagkakita', 'ni', 'Guia', 'nga', 'nagbuy-od', 'sulod', 'sa', 'iyang', 'balay.', 'Base', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'gidudahang', 'hubak', 'ang', 'namatyan', 'ni', 'Guia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,963 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'ni', 'Police', 'Lt.', 'Si', 'Colonel', 'Randy', 'Caballes', ',', 'hepe', 'sa', 'Talisay', 'City', 'Police', 'Station', ',', 'niila', 'sa', 'mga', 'biktima', 'nga', 'sila', 'si', 'Mark', 'Emman', ',', '6', ',', 'ug', 'ang', 'iyang', 'maguwang', 'nga', 'si', 'Nathaniel', 'Llamedo', ',', '7', ',', 'nagpuyo', 'sa', 'maong', 'dapit.', 'Matod', 'ni', 'Caballes', 'nga', 'sa', 'ilang', 'imbestigasyon', ',', 'nagduwa', 'ang', 'managsuon', 'uban', 'sa', 'ilang', 'duha', 'ka', 'ig-agaw', 'nga', 'lalaki', 'nga', 'nagpangidaron', 'og', '11', 'ug', '9', 'anyos', 'nga', 'nagduwa', 'og', 'tabanog', 'duol', 'sa', 'nahitaboan', 'sa', 'insidente.', 'Giingong', 'nakahukom', 'ang', 'mga', 'bata', 'nga', 'manghimasa', 'sa', 'gihimong', 'septic', 'tank', ',', 'nagkupot', 'sila', 'og', 'pisi', 'nga', 'naglibot', 'sa', 'septic', 'tank', 'apan', 'kalit', 'lang', 'kining', 'natangtang', 'tungod', 'sa', 'ilang', 'pagkahulog', ',', 'giingong', 'wala', 'masayod', 'ang', 'duha', 'ka', 'bata', 'nga', 'namatay', 'sa', 'paglangoy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,964 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'post', 'sa', 'netizen', 'nga', 'si', 'Richard', 'Go', 'bag-o', 'lang', 'nag-viral', 'sa', 'Facebook', 'niadtong', '2019', 'nga', 'nag-feature', 'sa', 'mga', 'litrato', 'nga', 'iyang', 'kuha', 'sa', 'Adarna', 'Bird', ',', 'nga', 'iyang', 'nakuha', 'sa', 'Semirara', 'island', 'sa', 'Antique.', 'Ang', 'golden', 'pheasant', 'kay', 'endemic', 'sa', 'China.', 'Nabantog', 'kini', 'tungod', 'sa', 'hayag', 'nga', 'balhibo', 'niini', 'ug', 'taas', 'nga', 'ikog.', 'Giingon', 'nga', 'kini', 'ang', 'peg', 'sa', 'mythical', 'phoenix.', 'Ang', 'Ibong', 'Adarna', 'gibase', 'sa', 'klasiko', 'nga', 'alamat', 'sa', 'Pilipino', 'bahin', 'sa', 'usa', 'ka', 'hari', 'nga', 'masakiton', 'nga', 'nagpadala', 'sa', 'iyang', 'tulo', 'ka', 'mga', 'anak', 'nga', 'lalaki', 'sa', 'pagpangita', 'sa', '"', 'Ibong', 'Adarna', '"', 'ingon', 'ang', 'bugtong', 'tambal', 'sa', 'iyang', 'sakit', ',', 'uban', 'ang', 'saad', 'nga', 'ang', 'bisan', 'kinsa', 'nga', 'makadakop', 'sa', 'langgam', 'ug', 'magdala', 'niini', 'siya', 'makapanunod', 'sa', 'iyang', 'trono', 'og', 'bahandi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,965 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Veteran', ''s', 'Monument', 'sa', 'junction', 'sa', 'Roxas', 'Avenue-Ponciano', 'Reyes', 'Street', 'sa', 'Davao', 'City', ',', 'kanunay', 'nga', 'mapahiyumon', 'ang', '99-anyos', 'nga', 'si', 'Teofilo', 'Gamutan', ',', 'ang', 'pinaka', 'tiguwang', 'nga', 'mitambong', 'sa', 'kalihokan', 'sa', 'paghandom', 'sa', 'Araw', 'ng', 'Kagitingan', 'karong', 'Lunes', ',', 'Abril', '10.', 'Sa', 'ambush', 'interview', ',', 'giasoy', 'ni', 'Gamutan', 'ang', 'mga', 'kalisdanan', 'nga', 'naagoman', 'niya', 'ug', 'sa', 'iyang', 'mga', 'kaubang', 'sundalo', 'sa', 'panahon', 'sa', 'WWII', ',', 'ug', 'nanghinaut', 'nga', 'ang', 'karon', 'ug', 'umaabot', 'nga', 'mga', 'henerasyon', 'mahinumdom', 'ug', 'mapasalamatan', 'gihapon', 'sa', 'mga', 'pakigbisog', 'nga', 'ilang', 'gipakigbatokan', 'aron', 'mabawi', 'ang', 'soberanya', 'sa', 'Pilipinas', 'gikan', 'sa', 'mga', 'mananakop.', 'Magsaulog', 'si', 'Gamutan', 'sa', 'iyang', 'ika-100', 'nga', 'adlawng', 'natawhan', 'karong', 'Hulyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 6, 6, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,966 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Abril', '4', ',', 'si', 'Cebu', 'Governor', 'Gwendolyn', 'Garcia', 'niluwat', 'og', 'Executive', 'Order', '(', 'EO', ')', 'No.', '11', 'nga', 'molugway', 'og', 'dugang', '15', 'ka', 'adlaw', ',', 'o', 'hangtod', 'Abril', '20', ',', 'ang', 'temporaryong', 'pagdili', 'sa', 'pagsulod', 'sa', 'Sugbo', 'sa', 'mga', 'buhing', 'baboy', 'ug', 'karne', ',', 'semilya', 'sa', 'baboy', ',', 'ug', 'mga', 'produkto', 'sa', 'karne', 'sa', 'baboy', 'gikan', 'sa', 'Negros', 'Island', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 0, 1, 2, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
4,967 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'buwan', 'human', 'sa', 'kontrobersyal', 'ug', 'viral', 'nga', 'icing-smearing', 'incident', 'nga', 'naglambigit', 'sa', 'host-vlogger', 'nga', 'si', 'Alex', 'Gonzaga', ',', 'giangkon', 'niya', 'nga', 'nakahatag', 'kini', 'kaniya', 'og', 'dakong', 'leksyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,968 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanang', 'bakasyon', 'nga', 'taman', 'kalendaryo', 'lang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,969 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Capt.', 'Abegael', 'Donasco', ',', 'deputy', 'police', 'chief', 'sa', 'dakbayan', 'sa', 'Talisay', ',', 'kinsa', 'nidakop', 'sa', 'tinun-an', 'human', 'nitaho', 'ang', 'usa', 'ka', 'traysikol', 'drayber', 'nga', 'giingong', 'gihulga', 'siya', 'sa', 'tinun-an', 'gamit', 'ang', 'granada.', 'Matod', 'sa', 'mirespondeng', 'kapulisan', ',', 'usa', 'ka', 'fragmentation', 'grenade', 'ug', 'pinaugang', 'dahon', 'sa', 'marijuana', 'ang', 'giingong', 'nakuha', 'gikan', 'sa', 'estudyante.', 'Matod', 'ni', 'Donasco', 'nga', 'ang', 'tinun-an', 'nag-atubang', 'og', 'kasong', 'illegal', 'possession', 'of', 'explosives', 'ug', 'kalapasan', 'sa', 'Comprehensive', 'Dangerous', 'Drugs', 'Act', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0] | cebuaner |
4,970 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nganong', 'wala', 'nay', 'motivational', 'quotes', 'sa', 'akong', 'timeline', 'dili', 'na', 'pud', 'ba', 'ninyo', 'kaya', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,971 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BUS', 'NAGBUNGGO', 'ANG', 'JEEPNEY', 'Tulo', 'ka', 'tawo', 'ang', 'naangol', 'human', 'nabangga', 'ang', 'usa', 'ka', 'pampasaherong', 'bus', 'sa', 'usa', 'ka', 'pampasaherong', 'jeep', 'sa', 'Barangay', 'Agape', ',', 'Loboc', ',', 'Bohol', 'niadtong', 'Martes', 'sa', 'hapon', ',', 'Abril', '4', ',', '2023.', 'Ang', 'drayber', 'sa', 'bus', 'anaa', 'sa', 'prisohan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,972 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'man', 'ko', 'anad', 'nga', 'manuyo', ',', 'anad', 'man', 'ko', 'mang-away', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,973 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lima', 'ka', 'miyembro', 'sa', 'usa', 'ka', 'pamilya', 'ang', 'namatay', 'sa', 'lungsod', 'sa', 'Pozorrubio', ',', 'Pangasinan', 'niadtong', 'Lunes', ',', 'Abril', '3', ',', 'human', 'nasunog', 'ang', 'ilang', 'balay', 'tungod', 'sa', 'usa', 'ka', 'e-bike', 'nga', 'napasagdan', 'nga', 'naka-charge.', 'Niabot', 'sa', 'second', 'alarm', 'ang', 'sunog', 'nga', 'milanat', 'og', 'kapin', 'sa', 'duha', 'ka', 'oras', 'una', 'kini', 'gideklarar', 'nga', 'fire', 'out', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,974 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagbalik', 'sa', 'programa', 'nga', ''Face', '2', 'Face', ''', 'sa', 'TV5', ',', 'ang', 'host-singer', 'ba', 'nga', 'si', 'Karla', 'Estrada', 'ang', 'angayan', 'nga', 'mahimong', 'host', ',', 'o', 'si', ''Tyang', ''', 'Amy', 'Perez', 'ba', 'gyud', 'ang', 'angay', 'sa', 'maong', 'show', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,975 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Indian', 'national', 'nga', 'giilang', 'si', 'Ranjit', 'Singh', ',', '46-anyos', ',', 'sa', 'Barangay', 'Perrelos', ',', 'dakbayan', 'sa', 'Carcar', ',', 'Cebu', 'tungod', 'sa', 'giingong', 'pagsunog', 'niini', 'mga', 'alas', '11', 'sa', 'buntag.', 'Martes', ',', 'Abril', '4', ',', '2023', ',', 'usa', 'ka', 'multicab', 'ug', 'balay', 'ni', 'Jose', 'Bacatan', 'Enjambre', ',', '61.', 'Giingong', 'gipangita', 'pa', 'sa', 'Indian', 'national', 'ang', 'iyang', 'uyab', 'nga', 'anak', 'sa', 'biktima', 'apan', 'mibalibad', 'si', 'Enjambre', 'hinungdan', 'nga', 'gisunog', 'sa', 'suspek', 'ang', 'balay', 'ug', 'sakyanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [7, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,976 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“My', 'home', ',', '”', 'caption', 'ni', 'Barbie', 'Forteza', 'sa', 'iyang', 'post', ',', 'nga', 'gireplyan', 'ni', 'Jak', 'Roberto', 'og', '“The', 'best', '!', '❤️', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,977 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Isa', 'ka', 'minor', 'de', 'edad', 'nga', 'babaye', 'nga', 'naghimo', 'sa', 'April', 'Fools', ''', 'prank', 'nangayo', 'og', 'pasaylo', 'sa', 'publiko', 'nga', 'nag-ingon', 'nga', 'siya', 'kay', 'na', 'rape-slay', ',', 'nga', 'nakapaalarma', 'sa', 'mga', 'awtoridad', ',', 'kay', 'wala', 'silay', 'nadawat', 'nga', 'taho', 'bahin', 'sa', 'insidente.', 'Ang', 'dalagita', 'anaa', 'na', 'karon', 'sa', 'kustodiya', 'sa', 'Women', ''s', 'and', 'Children', ''s', 'Protection', 'Desk', '(', 'WCPD', ')', 'sa', 'Ipil', 'Municipal', 'Police', 'sa', 'Zamboanga', 'Sibugay', 'alang', 'sa', 'counseling', ',', 'samtang', 'wa', 'pa', 'matino', 'kon', 'pasakaan', 'og', 'kaso', 'sa', 'kapolisan', 'ang', 'iyang', 'mga', 'ginikanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,978 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Buyag', 'uy', 'unsa', 'mani', 'akong', 'mga', 'nakita', 'mag', 'semana', 'santa', 'na', 'ra', 'ba'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,979 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['REASON', 'WHY', 'NEVER', 'AKO', 'SUMALI', 'SA', 'MGA', 'HIGH', 'SCHOOL', 'REUNIONS'', 'DABAWENYA', 'content', 'creator', 'nga', 'si', 'Karel', 'Kat', 'Lopez', 'mipaambit', 'sa', 'iyang', 'sentimento', 'sa', 'Facebook', 'comment', 'gikan', 'sa', 'iyang', 'classmate', 'sa', 'high', 'school.', '"', '[', 'This', 'is', 'the', ']', 'reason', 'why', 'never', 'ako', 'sumali', 'sa', 'mga', 'High', 'School', 'reunions', ',', 'di', 'ko', 'alam', 'ba’t', 'ang', 'proud', 'niyo', 'sa', 'mga', 'pambubully', 'nyo.', 'Di', 'sana', 'ma', 'experience', 'ng', 'mga', 'anak', 'nyo', 'yan', 'in', 'the', 'future', '"', 'Karel', 'wrote', 'on', 'her', 'recent', 'Facebook', 'post', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 7, 0, 0] | cebuaner |
4,980 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kapag', 'terno', 'iyahang', 'hambag', 'og', 'lunch', 'box', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,981 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lord', ',', 'salamat', 'kaayo', 'sa', 'tanang', 'grasya', 'nga', 'imong', 'gihatag', 'nako', 'karon.', 'Salamat', 'usab', 'sa', 'pagpahulay', ',', 'hinaut', 'nga', 'bantayan', 'mo', 'kami', 'sa', 'amoang', 'pagtulog.', 'Makamata', 'mi', 'nga', 'naay', 'kusog', 'og', 'panibag-ong', 'paglaum.', 'Sa', 'pangalan', 'ni', 'Hesus.', 'Amen'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,982 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'lang', 'Holy', 'Week', 'akoang', 'gihuwat', ',', 'pati', 'pod', 'imohang', 'chat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,983 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanang', 'todo-kag', 'birit', 'sa', 'videoke', 'nya', 'naay', 'makig', 'tunga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,984 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INIT', 'KAAYO', 'SA', 'MACTAN', 'Ang', 'PAGASA-Mactan', 'nitala', 'og', 'heat', 'index', 'nga', '36', '°C', 'sa', 'dakbayan', 'sa', 'Sugbo', 'alas', '12', 'sa', 'udto', 'Martes', ',', 'Abril', '4', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,985 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitaas', 'ang', 'presyo', 'sa', 'sikat', 'nga', 'hikay', 'pag', 'semana', 'santa'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,986 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Meta', 'CEO', 'nga', 'si', 'Mark', 'Zuckerberg', 'maoy', 'sentro', 'sa', 'atensyon', 'online', 'human', 'ang', 'iyang', 'AI-generated', 'nga', 'mga', 'litrato', 'nga', 'nagpakita', 'kaniya', 'nga', 'nagsuroysuroy', 'sa', 'usa', 'ka', 'fashion', 'show', 'nga', 'nagsul-ob', 'og', 'mabulukon', 'nga', 'Louis', 'Vuitton', 'outfit', 'nga', 'nahimong', 'viral', 'sa', 'social', 'media.', 'Ang', 'Midjourney', 'image', 'generator', 'nagmugna', 'niining', 'mga', 'eksplosibong', 'litrato', 'ni', 'Zuckerberg', ',', 'kinsa', 'nailhan', 'nga', 'kanunay', 'magsul-ob', 'lang', 'og', 'gray', 'nga', 'T-shirt', 'ug', 'karsones', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,987 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SAN', 'KA', 'PUNTA', '?', 'TO', 'THE', 'MOON', 'LITERAL', 'Ang', 'misyon', 'naglakip', 'sa', 'tulo', 'ka', 'mga', 'astronaut', 'sa', 'NASA', ',', 'lakip', 'ang', 'unang', 'babaye', ',', 'ug', 'ang', 'unang', 'black', 'nga', 'mibiyahe', 'ngadto', 'sa', 'Buwan.', 'Ang', 'laing', 'astronaut', 'gikan', 'sa', 'Canada', ',', 'ug', 'mao', 'ang', 'unang', 'Canadian', 'nga', 'mitugpa', 'sa', 'Bulan.', 'Gibansay', 'sa', 'International', 'Space', 'Station', '(', 'ISS', ')', ',', 'ang', 'mga', 'astronaut', 'mao', 'sila', 'si', 'Christina', 'Koch', ',', 'Jeremy', 'Hansen', ',', 'Victor', 'Glover', 'ug', 'Reid', 'Wiseman.', 'Si', 'Hansen', 'mao', 'ang', 'Canadian', 'astronaut', 'ug', 'kini', 'ang', 'iyang', 'unang', 'paglupad', 'sa', 'kawanangan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 7, 0, 0, 0, 5, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,988 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'impormasiyon', ',', 'naglakaw', 'ang', 'biktima', 'nga', 'si', 'alyas', ''Dodong', ''', 'paingon', 'sa', 'merkado', 'sa', 'Bankerohan', 'dihang', 'kalit', 'lang', 'gibunalan', 'sa', 'suspek', 'nga', 'si', 'alyas', ''Romaro', ''', 'og', 'planggana', 'maong', 'mibawos', 'og', 'bunal', 'ang', 'vendor.', 'Sa', 'pag-abot', 'sa', 'barangay', ',', 'imbes', 'nga', 'mangayo', 'og', 'pasaylo', ',', 'ang', 'suspek', 'nagplano', 'sa', 'pagpatay', 'sa', 'biktima', 'hinungdan', 'nga', 'midangop', 'sa', 'kapolisan', 'ang', 'naulahi', 'aron', 'ipa-blotter', 'ang', 'insidente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,989 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'operatiba', 'sa', 'Philippine', 'Drug', 'Enforcement', 'Agency', '(', 'PDEA', ')', '-Cebu', 'Provincial', 'Office', ',', 'ug', 'Cebu', 'City', 'Police', 'Office', 'Station', '6', ',', 'nipahigayon', 'og', 'buy-bust', 'operation', 'niadtong', 'Lunes', ',', 'Abril', '3', ',', 'sa', 'Sitio', 'Gen.', 'Gines', ',', 'Barangay', 'Suba', ',', 'nga', 'nisangpot', 'sa', 'pagronda', 'sa', 'usa', 'ka', 'drug', 'den', 'ug', 'pagkasikop', 'sa', 'upat', 'ka', 'mga', 'tawo', ',', 'apil', 'na', 'ang', 'giingong', 'nagmintinar', 'sa', 'drug', 'den', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,990 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Still', 'rocking', 'at', '75', '!', 'Gawas', 'kang', 'Presidente', 'Bongbong', 'Marcos', ',', 'ang', 'ubang', 'mga', 'VIP', 'mitambong', 'usab', 'sa', 'party', 'ni', 'Finance', 'Secretary', 'Benjamin', 'Diokno', ',', 'sama', 'nila', 'ni', 'House', 'Speaker', 'Martin', 'Romualdez', ',', 'Senador', 'Mark', 'Villar', ',', 'kanhi', 'Executive', 'Secretary', 'Salvador', 'Medialdea', ',', 'Defense', 'Secretary', 'Delfin', 'Lorenzana', ',', 'kanhi', 'Communications', 'Secretary', 'Martin', 'Andanar', ',', 'ug', 'kanhi', 'Presidente', 'Joseph', 'Estrada.', 'Source', ':', 'https', ':', '/', '/', 'bit.ly', '/', '3Um3Q7D', '#', 'PilipinasToday', '#', 'PBBM'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,991 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Suma', 'pa', 'ni', 'Aljur', 'nga', 'ang', 'iyang', '"', 'biggest', 'mistake', '"', 'mao', 'ang', 'pagkapakyas', 'sa', 'paggahin', 'og', 'igong', 'panahon', 'o', 'oras', 'uban', 'ni', 'Kylie', 'Padilla', 'ug', 'sa', 'ilang', 'mga', 'anak', 'sa', 'dihang', 'sila', 'nag-uban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,992 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pagpatay', 'sa', '22', 'anyos', 'nga', 'tinun-an', 'sa', 'De', 'La', 'Salle', 'University-Dasmariñas', 'sa', 'lalawigan', 'sa', 'Cavite', 'subling', 'nakaduso', 'kang', 'Senador', 'Ramon', 'Revilla', 'Jr.', 'sa', 'pag-insister', 'sa', 'pagpasig-uli', 'sa', 'silot', 'sa', 'kamatayon', 'alang', 'sa', 'heinous', 'nga', 'mga', 'krimen.', 'Nasikop', 'sa', 'kapulisan', 'niadtong', 'weekend', 'ang', 'giingong', 'mamumuno', 'ni', 'Reyna', 'Leanne', 'Daguinsin', ',', 'usa', 'ka', 'graduating', 'nga', 'tinun-an', 'sa', 'computer', 'science', ',', 'kinsa', 'gitulis', 'ug', 'balik-balik', 'nga', 'gidunggab', 'sulod', 'sa', 'iyang', 'dormitory', 'room', 'sa', 'Barangay', 'Santa', 'Fe', ',', 'Dasmariñas.', 'Niadtong', 'Lunes', ',', 'si', 'Revilla', 'kinsa', 'nitanyag', 'og', 'P300,000', 'nga', 'reward', 'midayeg', 'sa', 'pagkasikop', 'sa', '39-anyos', 'nga', 'suspek', 'nga', 'si', 'Angelito', 'Erlano.', 'ingon', 'niya', 'sa', 'usa', 'ka', 'pahayag.', 'Subling', 'gisubli', 'sa', 'senador', 'ang', 'iyang', 'panawagan', 'nga', 'ibalik', 'ang', 'silot', 'sa', 'kamatayon', 'sa', 'mga', 'linuog', 'nga', 'krimen.', 'nga', 'nagsulong', 'usod', 'sa', 'hatol', 'sa', 'silot', 'nga', 'kamatayon', 'sa', 'plunder', 'og', 'drug', 'trafficking', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,993 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iya', 'ID', ',', 'ginkilala', 'ang', 'biktima', 'kay', 'Christine', 'Gabayeron', 'sang', 'Barangay', 'San', 'Vicente', ',', 'Jaro', ',', 'Iloilo', 'City', ',', 'apang', 'suno', 'sa', 'mga', 'report', ',', 'tumandok', 'ini', 'sang', 'Brgy.', 'Bacjawan', 'Sur', ',', 'Concepcion', ',', 'Iloilo.', 'Sa', 'report', 'sa', 'kapulisan', ',', 'duha', 'ka', 'room', 'attendant', 'ang', 'nakabantay', 'sa', 'lalaki', 'nga', 'nigawas', 'sa', 'lawak', 'diin', 'iyang', 'gi-check', 'in', 'ang', 'biktima', ',', 'mga', 'alas', '6:30', 'sa', 'buntag', 'niadtong', 'Abril', '1.', 'Gihulagway', 'ang', 'lalaki', 'nga', 'nagpangidaron', 'og', '30', 'anyos', ',', '5'5', '"', ',', 'slim', ',', 'mubo', 'nga', 'kulot', 'ang', 'buhok', ',', 'nagsul-ob', 'og', 'itom', 'nga', 'cap', 'ug', 'blue', 'nga', 'jersey.', 'Matod', 'sa', 'tagdumala', 'sa', 'spa', 'center', 'nga', 'gitrabahoan', 'ni', 'Gabayeron', 'nga', 'ang', 'lalaki', 'niadto', 'sa', 'ilang', 'establisemento', 'mga', 'alas', '3:00', 'sa', 'kaadlawon', 'aron', 'mangayo', 'og', 'massage', 'service', ',', 'hangtod', 'nga', 'nihangyo', 'ang', 'biktima', 'sa', 'manager', 'nga', 'palakton', 'siya', 'kay', 'gusto', 'kuno', 'nung', 'lalaki', 'ang', '“home', 'service', '"', '.', 'Bisan', 'wala', 'pa', 'matino', 'sa', 'mga', 'imbestigador', 'ang', 'motibo', 'sa', 'pagpatay', ',', 'giingong', 'nawala', 'ang', 'kuwarta', 'ug', 'cellphone', 'sa', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,994 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'diayng', 'init', 'kaau', 'ky', 'naay', 'nagpataka', 'og', 'ampo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,995 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“This', '(', 'inquiry', ')', 'is', 'to', 'clarify', 'issues.', 'Wala', 'itong', 'kinalaman', 'sa', 'kaso', 'ng', 'ICC', '(', 'International', 'Criminal', 'Court', ')', ',', '”', 'ani', 'Sen.', 'Francis', 'Tolentino.', 'Si', 'Tolentino', 'niingon', 'karong', 'Domingo', ',', 'Abril', '2', ',', 'nga', 'ilang', 'giimbitar', 'ang', 'mga', 'opisyal', 'sa', 'ICC', 'nga', 'moatubang', 'sa', 'inquiry', 'nga', 'ipahigayon', 'sa', 'Senate', 'committee', 'on', 'justice', 'and', 'human', 'rights', ',', 'nga', 'iyang', 'gipangulohan'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0] | cebuaner |
4,996 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Police', 'Regional', 'Office-Davao', 'Region', '(', 'PRO-Davao', ')', 'mitaho', 'nga', 'ang', 'mga', 'kaso', 'sa', 'pagpanglugos', 'sa', 'rehiyon', 'nakapaalarma', ',', 'human', 'nakatala', 'og', '604', 'ka', 'kaso', 'gikan', 'sa', 'Enero', 'hangtod', 'Disyembre', '2022.', 'Hinuon', ',', 'ang', 'tigpamaba', 'sa', 'PRO-Davao', 'nga', 'si', 'P', '/', 'Maj.', 'Eudisan', 'Gultiano', 'nga', 'sa', 'kasagaran', 'ang', 'biktima', 'usa', 'ka', 'menor', 'de', 'edad', 'ug', 'mga', 'insidente', 'sa', 'insesto', ',', 'diin', 'ang', 'mga', 'suspetsado', 'kasagaran', 'ang', 'amahan', ',', 'igsuon', ',', 'o', 'ig-agaw', 'ug', 'higala', 'sa', 'mga', 'biktima', 'o', 'ilang', 'suod', 'nga', 'kabanay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,997 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'sa', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'Directorate', 'for', 'Operation', ',', 'mokabat', 'sa', '38,387', 'ka', 'mga', 'pulis', 'ang', 'ipakatap', 'alang', 'sa', 'pagpausbaw', 'sa', 'presensya', 'sa', 'kapulisan', 'pinaagi', 'sa', 'mobile', 'ug', 'foot', 'patrols', ',', 'samtang', '39,504', 'ka', 'mga', 'pulis', 'ang', 'ibutang', 'sa', 'mga', 'lugar', 'nga', 'adunay', 'daghang', 'trapiko', ',', 'lakip', 'ang', 'mga', 'nag-unang', 'kadalanan', ',', 'transport', 'hub', ',', 'komersyal', 'nga', 'mga', 'lugar', ',', 'ug', 'mga', 'simbahan', 'sa', 'nasud.', 'Bisan', 'og', 'wala', 'pay', 'nakitang', 'seryosong', 'hulga', 'karong', 'panahona', ',', 'gipahinumdoman', 'ni', 'PNP', 'spokesperson', 'P', '/', 'Col', '/', 'Jean', 'Fajardo', 'ang', 'publiko', 'nga', 'mamauli', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'probinsiya', 'nga', 'likayan', 'ang', 'pagdala', 'og', 'mga', 'mahalong', 'butang', ',', 'ipahilayo', 'ang', 'mga', 'bata', 'ug', 'mga', 'tigulang', 'sa', 'mga', 'lugar', 'nga', 'daghang', 'tawo', ',', 'ug', 'siguroha', 'nga', 'sila', 'luwas', 'ang', 'kahimtang', 'sa', 'ilang', 'mga', 'sakyanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,998 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Delikado', 'jud', 'nang', 'reto-reto', ',', 'dira', 'man', 'ko', 'nadali.', '#', 'PilipinasToday', '#', 'Bench', '#', 'WiHajoon'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,999 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'sa', 'aktor', 'nga', 'si', 'JM', 'De', 'Guzman', 'nga', 'nag', ''self-destruct', ''', 'siya', 'human', 'sa', 'iyang', '"', 'sakit', '"', 'nga', 'panagbuwag', 'sa', 'iyang', 'kanhi', 'uyab.', 'Bisan', 'og', 'wala', 'hinganli', 'ni', 'JM', 'ang', 'maong', 'ex-girlfriend', ',', 'usa', 'ra', 'ang', 'pangagpas', 'sa', 'netizens', ':', 'ang', 'aktres', 'nga', 'si', 'Jessy', 'Mendiola', ',', 'kinsa', 'unang', 'nakarelasyon', 'ni', 'JM', 'sa', 'showbiz.', 'Si', 'Jessy', 'minyo', 'ug', 'adunay', 'anak', 'sa', 'host', 'nga', 'si', 'Luis', 'Manzano', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
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