Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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4,700 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PRESYO', 'SA', 'KALAMAY', ',', 'MOABOT', 'NA', 'SA', 'P100', '/', 'KILO', 'SA', 'PIPILA', 'KA', 'MERKADO', 'Moabot', 'na', 'sa', 'P100', 'kada', 'kilo', 'ang', 'presyo', 'sa', 'kalamay', 'sa', 'pipila', 'ka', 'merkado', 'sa', 'nasud', ',', 'sumala', 'pa', 'sa', 'price', 'monitoring', 'sa', 'Department', 'of', 'Agriculture', '(', 'DA', ')', 'niadtong', 'niaging', 'semana.', 'Gibutyag', 'sa', 'DA', 'nga', 'kapin', 'P100', '/', 'kilo', 'ang', 'presyo', 'sa', 'kalamay', 'sa', 'Muñoz', 'Market', 'ug', 'Mega', 'Q', 'Mart', 'sa', 'Quezon', 'City', ',', 'Pasay', 'City', 'Market', 'sa', 'Pasay', 'City', ',', 'ug', 'Malabon', 'City', 'Market', 'Kakulangon', 'sa', 'suplay', 'sa', 'kalamay', 'ang', 'hinungdan', 'sa', 'pagsaka', 'sa', 'presyo', 'sa', 'kalamay', ',', 'matud', 'pa', 'ni', 'DA', 'Undersecretary', 'Kristine', 'Evangelista.', 'Gikompirmar', 'sab', 'niya', 'ang', 'naunang', 'pamahayag', 'sa', 'Sugar', 'Regulatory', 'Administraton', '(', 'SRA', ')', 'nga', 'kutob', 'na', 'lang', 'karong', 'Agosto', '19', 'ang', 'nabiling', 'suplay', 'sa', 'kalamay', 'sa', 'nasud.', 'Tungod', 'niini', ',', 'niawhag', 'si', 'SRA', 'administrator', 'ngadto', 'sa', 'publiko', 'nga', 'hinay-hinayon', 'lang', 'sa', 'pagkakaron', 'ang', 'pagkonsumo', 'sa', 'kalamay', '.'] 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 6, 0, 5, 6, 6, 6, 6, 6, 0, 5, 6, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,701 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['183', 'KA', 'STREET', 'FOOD', 'VENDORS', 'SA', 'DGTE', ',', 'GIHATAGAN', 'OG', 'NEGO-KART', 'ALANG', 'SA', 'DUGANG', 'INCOME', 'Pormal', 'nga', 'gihatagan', 'sa', 'NEGO-KART', 'karong', 'semanaha', 'ang', '183', 'ka', 'mamaligyaay', 'og', 'street', 'food', 'ning', 'dakbayan', 'sa', 'Dumaguete', ',', 'aron', 'mahatagan', 'sila', 'og', 'dugang', 'kinitaan', 'taliwala', 'sa', 'pandemya', 'sa', 'COVID-19.', 'Gipanguluhan', 'ni', 'Dumaguete', 'City', 'Mayor', 'Felipe', 'Remollo', 'ug', 'sa', 'pangulo', 'sa', 'Department', 'of', 'Labor', 'and', 'Employment', '(', 'DOLE', ')', '-', 'Negros', 'Oriental', 'nga', 'si', 'Maria', 'Teresa', 'Tanquiamco', 'ang', 'pag-apod-apod', 'sa', 'NEGO-KART', 'niadtong', 'Biyernes', ',', 'Agosto', '5', ',', '2022.', 'Lakip', 'sa', 'mga', 'NEGO-KART', 'mao', 'ang', 'mga', 'bike', 'ug', 'mga', 'kahimanan', 'sama', 'sa', 'burner', 'aron', 'sila', 'makasugod', 'sa', 'ilang', 'gagmay', 'nga', 'negosyo.', 'Ang', 'NEGO-KART', 'usa', 'ka', 'bunga', 'sa', 'P25', 'milyon', 'nga', 'livelihood', 'grant', 'nga', 'gihatag', 'ni', 'kanhing', 'DOLE', 'Secretary', 'Silvestre', 'Bello', 'III', 'ngadto', 'sa', 'Dumaguete', 'City', 'Public', 'Employment', 'Service', 'Office', ',', 'human', 'kini', 'gipasidunggan', 'isip', 'Best', 'PESO', 'sa', 'tibuok', 'Central', 'Visayas', '.'] 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, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 2, 0, 0, 0, 7, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 6, 0] | cebuaner |
4,702 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'ka', 'magtutudlo', 'sa', 'Negros', 'Oriental', 'ang', 'nalakip', 'sa', '28', 'nga', 'gipasidunggan', 'isip', 'Most', 'Inspiring', 'Teachers', 'of', 'the', 'Philippines', 'niadtong', 'Biyernes', ',', 'Agosto', '5', ',', '2022.', 'Gipasidunggan', 'sila', 'si', 'Nellen', 'B.', 'Coronado', 'gikan', 'sa', 'DepEd', 'Tanjay', 'City', 'ug', 'Dr.', 'Elizabeth', 'Susan', 'Vista-Suarez', 'gikan', 'sa', 'Silliman', 'University.', 'Gawas', 'nila', ',', 'gihatagan', 'sab', 'og', 'pasidungog', 'ang', 'mga', 'mosunod', 'nga', 'magtutudlo', 'tungod', 'sa', 'ilang', 'dedikasyon', 'sa', 'ilang', 'propesyon', ':', 'Manuel', 'P.', 'Albano', ',', 'Maria', 'Anthonette', 'V.', 'Allones', ',', 'Loreta', 'Baltazar', ',', 'Ludilyn', 'T.', 'Bardon', ',', 'Marilyn', 'Cardoso', ',', 'Nellen', 'Coronado', ',', 'Filomena', 'Dayagbil', ',', 'Jaypee', 'Del', 'Rosario', ',', 'Lisa', 'Macuja-Elizalde', ',', 'Rechila', 'Enojas', ',', 'Eric', 'John', 'Estoque', ',', 'Imelda', 'Gealon', ',', 'Cesar', 'Legaspi', ',', 'Chaney', 'Gulfan', ',', 'Sheina', 'Balabaran', 'Kusain', ',', 'Moises', 'Labian', ',', 'Fay', 'Luarez', ',', 'Ramil', 'Magunot', ',', 'Henrietta', 'Mangbanag', ',', 'John', 'Mauro', 'Manuel', ',', 'Noraida', 'Mecampong', ',', 'Levi', 'Mendoza', ',', 'Jose', 'Mari', 'Roa', ',', 'Raymond', 'Salas', ',', 'Elizabeth', 'Susan', 'Vista-Suarez', ',', 'Charisma', 'Ututalum', ',', 'ug', 'Prose', 'Ivy', 'Yepes.', 'Ang', 'maong', 'mga', 'magtutudlo', 'naggikan', 'sa', 'nagkalain-laing', 'bahin', 'sa', 'nasud', 'sama', 'sa', 'kauluhan', ',', 'Cebu', ',', 'ug', 'Cotabato', '.'] 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 3, 4, 4, 0, 0, 1, 2, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 2, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0] | cebuaner |
4,703 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', ',', 'NALUMOS', 'SA', 'LUNGSOD', 'SA', 'SIBULAN', 'Nalumos', 'ang', 'usa', 'ka', 'lalaki', 'didto', 'sa', 'Purok', '5', ',', 'Barangay', 'Poblacion', 'sa', 'lungsod', 'Sibulan', 'karong', 'hapon', ',', 'Agosto', '5', ',', '2022.', 'Giila', 'ang', 'nalumos', 'nga', 'si', 'Richard', 'Fortin', 'Diongco', ',', '53', 'anyos', ',', 'minyo', 'ug', 'residente', 'sa', 'naasoy', 'nga', 'lugar.', 'Matud', 'pa', 'sa', 'inisyal', 'nga', 'imbestigasyon', 'sa', 'kapulisan', ',', 'nag-inom', 'si', 'Diongco', 'kauban', 'ang', 'usa', 'ka', 'Ermelito', 'Villaflor', 'sa', 'dihang', 'niadto', 'kini', 'sa', 'baybay', 'sa', 'ilang', 'dapit.', 'Pag-abot', 'sa', 'baybay', ',', 'giingong', 'nalumos', 'ang', 'biktima', 'sa', 'wala', 'pa', 'matino', 'nga', 'hinungdan.', 'Gidala', 'sa', 'usa', 'ka', 'pribadong', 'ospital', 'diri', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'si', 'Diongco', ',', 'apan', 'comatose', 'pa', 'kini', 'sa', 'pagkakaron', ',', 'matud', 'pa', 'sa', 'kapulisan.', 'Nagpadayon', 'ang', 'imbestigasyon', 'sa', 'Sibulan', 'PNP', 'sa', 'maong', '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, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 5, 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, 1, 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, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0] | cebuaner |
4,704 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ANG', 'GIINGONG', 'BITUON', ',', 'CHORIZO', 'RA', 'DIAY', '!', 'Nangayo', 'og', 'pasaylo', 'ang', 'French', 'physicist', 'nga', 'si', 'Etienne', 'Klein', 'human', 'siya', 'nag-post', 'og', 'hulagway', 'sa', 'usa', 'ka', 'bituon', 'nga', 'gikuha', 'kuno', 'sa', 'NASA.', 'Apan', 'ang', 'giingong', 'bituon', ',', 'hiniwa', 'ra', 'diay', 'sa', 'chorizo', '!', 'Gi-tweet', 'ni', 'Klein', 'niadtong', 'Hulyo', '31', 'ang', 'maong', 'hulagway.', 'Sa', 'iyang', 'caption', ',', 'gibutyag', 'niya', 'nga', 'kini', 'bag-o', 'kuno', 'nga', 'kuha', 'sa', 'bituong', 'Proxima', 'Centauri.', 'Apan', 'nasayran', 'dayon', 'sa', 'mga', 'netizen', 'nga', 'chorizo', 'ra', 'gyud', 'diay', 'ang', 'iyang', 'gi-post', 'nga', 'hulagway', 'ug', 'dili', 'bituon.', 'Kini', 'human', 'nga', 'gi-tweet', 'sab', 'sa', 'usa', 'ka', 'netizen', 'ang', 'tinuod', 'nga', 'hulagway', 'sa', 'Proxima', 'Centauri.', 'Gibadlong', 'sa', 'usa', 'ka', 'researcher', 'ang', 'gibuhat', 'ni', 'Klein', 'kay', 'bisan', 'pa', 'man', 'og', 'kini', 'pasiaw', 'ra', 'niya', ',', 'nagpakatap', 'ra', 'kuno', 'gihapon', 'siya', 'og', 'mini', 'ug', 'sayop', 'nga', 'impormasyon', '.'] 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, 0, 0, 0, 1, 2, 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, 1, 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, 0, 0, 7, 8, 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] | cebuaner |
4,705 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INFLATION', 'RATE', 'SA', 'PILIPINAS', ',', 'NIBUROT', 'NGADTO', 'SA', '6.4', '%', 'NIADTONG', 'HULYO', 'Nisaka', 'ang', 'inflation', 'rate', 'sa', 'Pilipinas', 'ngadto', 'sa', '6.4', '%', 'niadtong', 'Hulyo', ',', 'mas', 'taas', 'kini', 'kung', 'itandi', 'sa', 'rate', 'niadtong', 'Hunyo', 'nga', 'aduna'y', '6.1', '%', '.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'Biyernes', ',', 'August', '5', ',', '2022.', 'Sumala', 'pa', 'sa', 'PSA', ',', 'ang', 'rason', 'sa', 'maong', 'inflation', 'rate', 'tungod', 'sa', 'taas', 'nga', 'gasto', 'sa', 'transportasyon', 'ug', 'pagpili', 'sa', 'pagkaon', 'ug', 'dili', 'makahubog', 'nga', 'mga', 'ilimnon.', 'Natala', 'sa', 'industriya', 'sa', 'pagkaon', 'ug', 'ilimnon', 'ang', '6.9', '%', 'nga', 'inflation', 'rate', ',', 'samtang', 'anaa', 'sa', '18.1', '%', 'ang', 'inflation', 'rate', 'sa', 'transportasyon.', 'Natala', 'sab', 'sa', 'restaurant', 'ug', 'accomodation', 'services', 'ang', '3.4', '%', 'inflation', 'rate', 'karong', 'bulana.', 'Sa', 'laing', 'bahin', ',', 'anaa', 'sa', '4.7', '%', 'ang', 'inflation', 'average', 'sa', 'nasud', 'gikan', 'sa', 'Enero', 'hangtod', 'Hulyo', ',', 'mas', 'taas', 'kini', 'sa', 'target', 'sa', 'gobyerno', 'nga', '2', 'hangtod', '4', '%', '.'] 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, 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, 3, 4, 4, 4, 4, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,706 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['9', 'KA', 'KABANAY', 'NAHILO', 'HUMAN', 'NIKAON', 'OG', 'BUTETE', ';', '1', 'PATAY', 'Patay', 'ang', 'usa', 'ka', 'inahan', 'samtang', 'gidala', 'sa', 'ospital', 'ang', 'walo', 'niya', 'ka', 'mga', 'kabanay', 'human', 'mahilo', 'sa', 'ilang', 'gikaon', 'nga', 'pufferfish', 'o', 'butete', 'sa', 'Caubian', 'Island', 'sa', 'dakbayan', 'sa', 'Lapu-Lapu', 'Miyerkules', ',', 'Agosto', '3', ',', '2022.', 'Gawas', 'sa', 'namatay', 'nga', 'inahan', ',', 'nahilo', 'sab', 'ang', 'iyang', 'mister', ',', 'upat', 'ka', 'anak', 'ug', 'tulo', 'ka', 'apo', 'nga', 'nag-edad', 'og', '4', ',', '5', 'ug', '9', 'anyos.', 'Anaa', 'na', 'sa', 'maayong', 'kahimtang', 'ang', 'walo', 'ka', 'mga', 'pasyente', 'apan', 'kinahanglan', 'gihapon', 'silang', 'obserbahan', 'sulod', 'sa', '24', 'ka', 'oras.', 'Sumala', 'pa', 'ni', 'Dr.', 'Eris', 'Augusto', ',', 'medical', 'officer', 'sa', 'Sta.', 'Rosa', 'Community', 'Hospital', 'sa', 'Olango', 'Island', ',', 'nikaon', 'og', 'butete', 'ang', 'mga', 'pasyente', 'sa', 'Miyerkules', 'sa', 'buntag.', 'Nakasinati', 'na', 'sila', 'og', 'pagkaluya', 'sa', 'ilang', 'kalawasan', 'ug', 'pagkalipong', 'mga', 'udto', 'na', 'sa', 'naasoy', 'nga', 'adlaw', 'ilabi', 'na', 'ang', 'inahan', 'nga', 'nikaon', 'sa', 'sulod', 'sa', 'maong', 'isda.', 'Nagpahimangno', 'sab', 'si', 'Dr.', 'Augusto', 'sa', 'publiko', 'nga', 'likayan', 'ang', 'pagkaon', 'og', 'butete', 'tungod', 'sa', 'hilo', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 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, 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] | cebuaner |
4,707 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAGDILI', 'SA', 'PAGBALIGYA', 'OG', 'PAGKAON', 'SA', 'VIRGIN', 'ISLAND', ',', 'GIPANAWAGAN', 'Gidili', 'sa', 'lokal', 'nga', 'kagamhanan', 'sa', 'lungsod', 'sa', 'Panglao', 'sa', 'Bohol', 'ang', 'tigbaligya', 'og', 'pagkaon', 'ug', 'uban', 'pang', 'aktibidad', 'sa', 'turismo', 'gawas', 'sa', 'sightseeing', 'sa', 'sikat', 'nga', 'Virgin', 'Island', 'sugod', 'August', '3', ',', '2022.', 'Kini', 'human', 'nag-viral', 'ang', 'post', 'sa', 'usa', 'ka', 'netizen', 'bahin', 'sa', '"', 'overpricing', '"', 'sa', 'pagkaon', 'nga', 'aduna'y', 'bill', 'nga', 'nagkantidad', 'og', 'kapin', 'P26,000', 'alang', 'sa', 'usa', 'ka', 'grupo', 'nga', 'aduna'y', '19', 'ka', 'indibidwal', 'kinsa', 'nibisita', 'sa', 'maong', 'tourist', 'spot.', 'Gianunsyo', 'ni', 'Panglao', 'Mayor', 'Edgardo', 'Arcay', 'sa', 'iyang', 'FB', 'page', 'nga', 'giaprobahan', 'na', 'sa', 'lokal', 'nga', 'kagamhanan', 'sa', 'Panglao', 'ang', 'maong', 'sugyot', 'atol', 'sa', 'ilang', 'emergency', 'meeting', 'uban', 'ang', 'lokal', 'nga', 'Department', 'of', 'Interior', 'and', 'Local', 'Government', '(', 'DILG', ')', 'ug', 'Municipal', 'Tourism', 'Council', 'niadtong', 'August', '2', ',', '2022.', 'Apan', 'nanawagan', 'si', 'Department', 'of', 'Tourism', 'secretary', 'Christina', 'Garcia-Frasco', 'ug', 'Department', 'of', 'Environment', 'and', 'National', 'Resources', '-', 'Region', '7', 'nga', 'imbestigahan', 'ang', 'maong', 'isyu', ',', 'samtang', 'ang', 'tourism', 'council', 'magbutang', 'og', 'night', 'market', 'sa', 'Panglao', 'alang', 'sa', 'mga', 'displaced', 'vendors', 'nga', 'maapektaran', 'sa', 'maong', 'pagdili', '.'] 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, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 1, 2, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 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] | cebuaner |
4,708 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAN-ANAN', ',', 'NAGTANYAG', 'OG', 'LIBRENG', 'SAMGYUPSAL', 'SA', 'MGA', 'GINGANLAN', 'OG', 'NICOLE', 'Nagtanyag', 'og', 'Free', 'Unli', 'Meal', 'ang', 'Samgyeopsal', 'House', 'sa', 'Valenzuela', 'City', 'alang', 'sa', 'mga', 'indibidwal', 'nga', 'aduna'y', 'pangalan', 'nga', '"', 'Nicole', '"', '.', 'Gihimo', 'nila', 'ang', '"', 'Libre', 'si', 'Nicole', '"', 'promo', 'aron', 'ipakita', 'ang', 'ilang', 'suporta', 'ni', 'Binibining', 'Pilipinas', 'International', '2022', 'nga', 'si', 'Nicole', 'Borromeo.', 'Aron', 'maka-avail', 'sa', 'maong', 'promo', ',', 'kinahanglan', 'nga', 'mopakita', 'og', 'pagpamatuod', 'nga', 'Nicole', 'ang', 'imong', 'pangalan', 'ug', 'magdala', 'og', 'tulo', 'ka', 'full-paying', 'adults.', 'Magsugod', 'kini', 'sa', 'Agosto', '1', 'hangtod', '31', ',', '2022.', 'Dili', 'valid', 'ang', 'maong', 'promo', 'uban', 'sa', 'bisan', 'unsang', 'ubang', 'mga', 'in-store', 'nga', 'promo', 'ug', 'diskwento', '.'] 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, 1, 0, 0, 7, 8, 8, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 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] | cebuaner |
4,709 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SWS', ':', '48', '%', 'SA', 'MGA', 'PAMILYANG', 'PILIPINO', 'GIKONSIDERAR', 'ANG', 'ILANG', 'KAUGALINGON', 'NGA', 'KABUS', 'Nisaka', 'ang', 'porsyento', 'sa', 'mga', 'pamilyang', 'Pilipino', 'kinsa', 'gikonsiderar', 'ang', 'ilang', 'kaugalingon', 'nga', 'kabus', 'gikan', 'sa', '43', '%', 'niadtong', 'Abril', ',', 'nahimo', 'kining', '48', '%', 'niadtong', 'Hunyo.', 'Sumala', 'pa', 'kini', 'sa', 'pinakabag-ong', 'survey', 'nga', 'gipagawas', 'sa', 'Social', 'Weather', 'System', '(', 'SWS', ')', '.', 'Matud', 'pa', 'sa', 'SWS', ',', 'ang', 'mga', 'pamilyang', 'Pilipino', 'kinsa', 'gikonsiderar', 'ang', 'ilang', 'kaugalingon', 'nga', 'kabus', 'anaa', 'sa', 'gibanabanang', 'numero', 'nga', '10.9', 'milyon', 'sa', 'Abril', 'ug', '12.2', 'milyon', 'sa', 'Hunyo', 'ning', 'tuiga.', 'Gipahigayon', 'ang', 'survery', 'niadtong', 'Hunyo', '26-29', ',', '2022', 'nga', 'nagpakita', 'nga', '48', '%', 'sa', 'mga', 'Pilipino', 'ang', 'gi-rate', 'nila', 'ang', 'ilang', 'kaugalingon', 'nga', '"', 'mahirap', 'o', 'poor', ',', '"', '31', '%', 'ang', 'nag-ingon', 'nga', 'sila', '"', 'borderline', 'poor', ',', '"', 'ug', '21', '%', 'ang', 'nagbutang', 'sa', 'ilang', 'kaugalingon', 'nga', '"', 'hindi', 'mahirap', 'o', 'not', 'poor.', '"', '1,500', 'ka', 'mga', 'respondente', 'sa', 'tibuok', 'Pilipinas', 'ang', 'niapil', 'ani', 'nga', 'survey', 'nga', 'aduna'y', 'tag', '300', 'ka', 'mga', 'respondente', 'sa', 'Metro', 'Manila', ',', 'Visayas', 'ug', 'Mindanao', ',', 'ug', '600', 'sa', 'Luzon', '.'] 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, 7, 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 3, 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, 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, 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, 5, 6, 0, 5, 0, 5, 0, 0, 0, 0, 5, 0] | cebuaner |
4,710 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['REMOLLO', 'NAMES', '5', 'COUNCILORS', 'AS', 'DEPUTY', 'MAYORS', 'OF', 'DUMAGUETE', 'Dumaguete', 'City', 'Mayor', 'Felipe', 'Remollo', 'has', 'appointed', 'five', 'city', 'councilors', 'as', 'deputy', 'mayors.', 'For', 'their', 'new', 'positions', ',', 'Remollo', 'assigned', 'councilors', 'Dionie', 'Amores', ',', 'Rey', 'Lawas', ',', 'Karissa', 'Tolentino-Maxino', ',', 'Renz', 'Macion', 'and', 'Franklin', 'Esmena', 'to', 'manage', 'different', 'sectors', 'of', 'the', 'city.', 'Amores', ',', 'who', 'is', 'also', 'the', 'chairman', 'for', 'Barangay', 'Motong', ',', 'was', 'assigned', 'as', 'deputy', 'mayor', 'for', 'concern', 'regarding', 'barangay', 'affairs', ',', 'economic', 'enterprises', ',', 'trade', 'and', 'industries', ',', 'public', 'utilities', ',', 'games', 'and', 'franchises', '(', 'except', 'for', 'franchises', 'for', 'motorcycles', 'for', 'hire', ')', '.', 'Sangguniang', 'Kabataan', 'president', 'Renz', 'Macion', 'was', 'assigned', 'as', 'deputy', 'mayor', 'for', 'concerns', 'for', 'youth', 'affair', ',', 'tourism', ',', 'sports', ',', 'environment', ',', 'fisheries', 'and', 'employment.', 'Councilor', 'Franklin', 'Esmeña', 'will', 'be', 'assigned', 'for', 'concerns', 'on', 'agriculture', ',', 'engineering', 'public', 'works', 'and', 'urban', 'development', ',', 'among', 'others.', 'Councilor', 'Rey', 'Lyndon', 'Lawas', ',', 'a', 'retired', 'police', 'general', ',', 'was', 'also', 'assigned', 'for', 'peace', 'and', 'order', ',', 'drug', 'abuse', ',', 'safety', ',', 'transportation', 'and', 'traffic.', 'Councilor', 'Karissa', 'Tolentino-Maxino', ',', 'meanwhile', ',', 'will', 'be', 'in', 'the', 'helm', 'for', 'concerns', 'on', 'education', ',', 'finance', ',', 'livelihood', ',', 'social', 'services', ',', 'poverty', 'alleviation', 'and', 'special', 'needs.', 'All', 'five', 'councilors', 'will', 'do', 'their', 'duties', 'as', 'deputy', 'mayors', 'on', 'a', 'voluntary', 'basis', '.'] 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, 5, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 1, 2, 0, 0, 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, 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, 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] | cebuaner |
4,711 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['VALIDITY', 'SA', 'BIRTH', ',', 'DEATH', ',', 'UG', 'MARRIAGE', 'CERTIFICATES', ',', 'HINGPIT', 'NGA', 'PERMANENTE', 'NA', 'Permanente', 'na', 'ang', 'validity', 'sa', 'tanang', 'mga', 'birth', ',', 'death', ',', 'ug', 'marriage', 'certificates', 'human', 'nga', 'gipirmahan', 'na', 'ang', 'balaod', 'nga', 'nagmando', 'niini.', 'Hingpit', 'nga', 'nahimong', 'balaod', 'ang', 'Republic', 'Act', 'No.', '11909', 'o', 'ang', '"', 'Permanent', 'Validity', 'of', 'the', 'Certificates', 'of', 'Live', 'Birth', ',', 'Death', ',', 'and', 'Marriage', 'Act', '"', 'niadtong', 'Lunes', ',', 'Agosto', '1.', 'Ang', 'maong', 'balaod', ',', 'gipanday', 'ni', 'Sen.', 'Ramon', '"', 'Bong', '"', 'Revilla', 'Jr.', 'Sigon', 'sa', 'naasoy', 'nga', 'balaod', ',', 'permanente', 'na', 'nga', 'valid', 'ang', 'tanang', 'birth', ',', 'death', ',', 'ug', 'marriage', 'certificates', 'nga', 'giluwatan', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', ',', 'National', 'Statistics', 'Office', '(', 'NSO', ')', 'og', 'ang', 'ubang', 'civil', 'registries—bisan', 'kanus-a', 'pa', 'kini', 'giisyu.', 'Gilaomang', 'dako', 'og', 'tabang', 'ang', 'maong', 'balaod', 'sa', 'mga', 'aplikanteng', 'ginapangayuan', 'og', 'mga', 'dokumento', 'nga', 'dunay', 'validity', 'nga', 'unom', 'ka', 'bulan', 'o', 'mas', 'dugay', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 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, 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] | cebuaner |
4,712 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['77-ANYOS', 'NGA', 'LALAKE', ',', 'SAMARAN', 'SA', 'PAGPANIGBAS', 'NGA', 'NAHITABO', 'SA', 'GUIHULNGAN', 'CITY', 'Samaran', 'ang', 'usa', 'ka', 'lalake', 'human', 'sa', 'pagpanigbas', 'nga', 'nahitabo', 'sa', 'Purok', 'Memong', 'sa', 'Barangay', 'Hibaiyo', 'sa', 'dakbayan', 'sa', 'Guihulngan', 'mga', '5:20', 'sa', 'hapon', 'niadtong', 'Agosto', '1', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Fermin', 'Colegio', 'Amara', ',', '77-anyos', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'anaa', 'sa', 'sulod', 'sa', 'iyang', 'panimalay', 'ang', 'biktima', 'sa', 'dihang', 'kalit', 'nga', 'nitungha', 'ang', 'suspek.', 'Sa', 'wala'y', 'igong', 'rason', ',', 'gitigbas', 'niini', 'ang', 'biktima', 'sa', 'makadaghang', 'higayon', 'gamit', 'ang', 'wala', 'pa', 'matino', 'nga', 'hinagiban', 'ug', 'naigo', 'ang', 'biktima', 'sa', 'iyang', 'ulo', 'ug', 'liog.', 'Giila', 'sa', 'kapulisan', 'ang', 'suspek', 'nga', 'si', 'Romy', 'Bocog', 'Cagoscos', ',', 'hingkod', 'ang', 'pangidaron', ',', 'ug', 'residente', 'sab', 'sa', 'naasoy', 'nga', 'lugar.', 'Human', 'sa', 'insidente', ',', 'dali', 'nga', 'miikyas', 'ang', 'suspek', 'sa', 'wala', 'mahibal-ang', 'direksyon.', 'Dali', 'sab', 'nga', 'gidala', 'ang', 'biktima', 'sa', 'Governor', 'Willy', 'Villigas', 'Memorial', 'Hospital', 'alang', 'atensyong', 'medical.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', 'follow-up', 'investigation', 'sa', 'kapulisan', 'alang', 'sa', 'maong', 'insidente.', 'Nagpahigayon', 'na', 'sab', 'sila', 'og', 'hot', 'pursuit', 'operation', 'aron', 'masikop', 'ang', 'suspetsado', '.'] 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 5, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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] | cebuaner |
4,713 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BABAYE', 'NANGANAK', 'SA', 'USA', 'KA', 'PAWNSHOP', 'SA', 'CEBU', 'Nanganak', 'si', 'Malyn', 'Rufo', ',', '27-anyos', ',', 'sa', 'iyang', 'ikalima', 'nga', 'anak', 'ngadto', 'sa', 'usa', 'ka', 'pawnshop', 'sa', 'Argao', 'sa', 'Cebu', 'niadtong', 'Lunes', 'sa', 'buntag', ',', 'Agosto', '1', ',', '2022.', 'Sumala', 'pa', 'ni', 'Janice', 'Aballe', ',', 'ig-agaw', 'ni', 'Rufo', ',', 'padulong', 'na', 'sila', 'sa', 'ospital', 'ug', 'nidalikyat', 'pag-adto', 'sa', 'usa', 'ka', 'pawnshop', 'aron', 'kuhaon', 'ang', 'kwarta', 'nga', 'gipadala', 'sa', 'father-in-law', 'ni', 'Rufo', 'apan', 'wala', 'na', 'niya', 'napunggan', 'ug', 'nanganak', 'na', 'sa', 'maong', 'pawnshop.', 'Usa', 'ka', 'personnel', 'sa', 'disaster', ''s', 'response', 'team', 'sa', 'maong', 'lungsod', 'ang', 'nitabang', 'ni', 'Rufo', 'pagpa-anak', 'sa', 'usa', 'ka', 'batang', 'lalaki.', 'Anaa', 'sab', 'sa', 'maong', 'hitabo', 'ang', 'bana', 'ni', 'Rufo.', 'Sa', 'pagkakaron', ',', 'anaa', 'na', 'sa', 'Isidro', 'C.', 'Kintanar', 'Memorial', 'Hospital', 'ang', 'inahan', 'ug', 'anak', 'niini.', 'Gipanan-aw', 'sab', 'ni', 'Rufo', 'nga', 'nganlan', 'ang', 'iyang', 'anak', 'sa', 'tag-iya', 'sa', 'maong', 'pawnshop', '.'] 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, 5, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,714 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'PATAY', 'HUMAN', 'MALUMOS', 'SA', 'BAYAWAN', 'CITY', 'Usa', 'ka', 'lalaki', 'ang', 'patay', 'human', 'nalumos', 'sa', 'Barangay', 'Nangka', 'sa', 'dakbayan', 'sa', 'Bayawan', 'mga', 'alas-2', 'sa', 'hapon', 'karong', 'adlawa', ',', 'Agosto', '1', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Sanny', 'Amantoy', 'Embang', ',', '66-anyos', ',', 'usa', 'ka', 'mag-uuma', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'papauli', 'na', 'ang', 'biktima', 'samtang', 'nitabok', 'sa', 'Canalum', 'River', 'sa', 'dihang', 'nalumos', 'kini.', 'Nadala', 'pa', 'ang', 'biktima', 'sa', 'Bayawan', 'District', 'Hospital', 'apan', 'gideklarar', 'kini', 'nga', 'dead', 'on', 'arrival', '.'] 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 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, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,715 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['#', '4', 'MOST', 'WANTED', 'PERSON', 'SA', 'BAYAWAN', ',', 'NASIKOP', 'SA', 'KAPULISAN', 'Nasikop', 'sa', 'kapulisan', 'ang', 'ika-upat', 'sa', 'listahan', 'sa', 'Most', 'Wanted', 'Person', 'sa', 'dakbayan', 'sa', 'Bayawan', 'niadtong', 'July', '22', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'nadakpan', 'nga', 'si', 'Jayven', 'Olivo', ',', '24-anyos', ',', 'usa', 'ka', 'mag-uuma', ',', 'ug', 'lumulupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Nadakpan', 'si', 'Olivio', 'sa', 'Sitio', 'Bagongot', 'sa', 'Barangay', 'Mandu-ao', 'sa', 'maong', 'dakbayan', 'mga', 'alas', '10:10', 'sa', 'buntag', 'sa', 'maong', 'adlaw.', 'Sumala', 'pa', 'sa', 'report', ',', 'nasikop', 'si', 'Olivio', 'sa', 'gipahigayong', 'sa', 'oplan', 'tracker', 'operation', 'pinaagi', 'sa', 'usa', 'ka', 'warrant', 'of', 'arrest', 'nga', 'aduna'y', 'Criminal', 'Case', 'No.', '3431', 'tungod', 'sa', 'paglapas', 'Section', '11', ',', 'Article', '2', 'sa', 'Republic', 'Act', '9165', 'o', 'ang', 'Comprehensive', 'Dangerous', 'Drugs', 'Act', 'of', '2002.', 'Anaa', 'na', 'sa', 'kustodiya', 'sa', 'kapulisan', 'ang', 'maong', 'akusado', 'alang', 'sa', 'tukmang', 'disposisyon', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 1, 0, 5, 6, 0, 5, 6, 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, 7, 8, 8, 8, 0, 0, 0, 7, 8, 8, 8, 8, 0, 7, 8, 8, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,716 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'PATAY', 'SA', 'GIINGONG', 'PAGPAMUSIL', 'SA', 'VALLEHERMOSO', 'Usa', 'ang', 'patay', 'sa', 'giingong', 'pagpamusil', 'nga', 'nahitabo', 'sa', 'Barangay', 'Poblacion', 'sa', 'lungsod', 'sa', 'Vallehermoso', 'mga', 'alas', '11', 'sa', 'gabie', 'niadtong', 'July', '30', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'John', 'Patrick', 'Banjec', ',', 'hingkod', 'ang', 'pangidaron', ',', 'ug', 'lumulupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'report', ',', 'personal', 'nga', 'niadto', 'sa', 'kapulisan', 'niadtong', 'July', '31', ',', '2022', 'ang', 'usa', 'ka', 'concerned', 'citizen', 'aron', 'ireport', 'ang', 'giingon', 'pagpamusil.', 'Diha-diha', 'nga', 'miresponde', 'ang', 'kapulisan', 'aron', 'mopahigayon', 'og', 'imbestigasyon.', 'Napalgan', 'nila', 'ang', 'patay'ng', 'lawas', 'sa', 'biktima', 'nga', 'nagbuy-od', 'sa', 'naasoy', 'nga', 'lugar.', 'Narekober', 'sa', 'kapolisan', 'ang', 'usa', 'ka', 'bala', 'ug', 'upat', 'ka', 'basiyo', 'sa', 'bala', 'sa', 'kalibre', '45.', 'Gidala', 'ang', 'patay'ng', 'lawas', 'sa', 'biktima', 'ngadto', 'sa', 'RHU', 'Vallehermoso', 'alang', 'sa', 'post', 'mortem', 'examination.', 'Nagpadayon', 'pa', 'sab', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'sa', 'maong', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,717 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakatala', 'na', 'ang', 'Phivolcs', 'og', '2,010', 'ka', 'aftershocks', 'sa', 'nagkalain-laing', 'bahin', 'sa', 'Luzon', 'human', 'ang', 'kusog', 'nga', 'magnitude', '7', 'nga', 'linog', 'nga', 'nitay-og', 'didto', 'niadtong', 'Hulyo', '27', ',', '2022.', 'Kadaghanan', 'sa', 'mga', 'aftershock', ',', 'na-record', 'sa', 'northwestern', 'Luzon', ',', 'ilabi', 'na', 'sa', 'mga', 'lalawigan', 'sa', 'Abra', 'ug', 'Ilocos', 'Sur.', 'Labing', 'naigo', 'sa', 'magnitude', '7', 'nga', 'linog', 'niadtong', 'niaging', 'semana', 'ang', 'duha', 'ka', 'naasoy', 'nga', 'probinsya', '.'] 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, 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, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,718 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitaliwan', 'na', 'sa', 'laing', 'kalibutan', 'si', 'kanhing', 'Presidente', 'Fidel', 'V.', 'Ramos.', 'Mao', 'kini', 'ang', 'gikompirmar', 'sa', 'Malacañang', 'karong', 'adlawa', ',', 'July', '31', ',', '2022.', 'Si', 'Ramos', 'maoy', 'nahimong', 'Presidente', 'sa', 'nasud', 'niadtong', 'tuig', '1992', 'hangtod', '1998.', 'Mahinumduman', 'ang', 'administrasyon', 'ni', 'Ramos', 'sa', 'programa', 'niini', 'nga', '"', 'Philippines', '2000', ',', '"', 'nga', 'giingong', 'usa', 'sa', 'mga', 'nagbuhi', 'pag-usab', 'sa', 'ekonomiya', 'sa', 'Pilipinas', 'niadtong', 'panahona', '.'] 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, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
4,719 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Magpabilin', 'gihapon', 'ang', 'Negros', 'Oriental', 'ubos', 'sa', 'Alert', 'Level', '2', 'karong', 'Agosto', '1', 'hangtod', '15', ',', '2022', ',', 'sumala', 'pa', 'sa', 'Department', 'of', 'Health.', 'Ubos', 'sa', 'Alert', 'Level', '2', ',', 'gitugot', 'ang', 'face-to-face', 'classes', ',', 'dine-in', ',', 'mga', 'panagtigom', 'sa', 'mga', 'simbahan', ',', 'ug', 'uban', 'pang', 'personal', 'care', 'services', 'hangtod', '50', 'porsyento', 'sa', 'ilang', 'normal', 'nga', 'kapasidad.', '#', 'NewsBite'] 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, 6, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,720 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dakong', 'garbo', 'karon', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'ang', 'mga', 'batan-ong', 'arnisador', 'nga', 'nag-uli', 'og', 'mga', 'nagkalain-laing', 'medalya', 'gikan', 'sa', '16th', 'WEKAF', 'World', 'Championship', 'bag-ohay', 'lang.', 'Lima', 'ka', 'gold', ',', 'duha', 'ka', 'silver', ',', 'ug', 'pito', 'ka', 'bronze', 'medal', 'ang', 'nakuha', 'sa', 'mga', 'arnisador', 'ning', 'dakbayan', 'gikan', 'sa', 'naasoy', 'nga', 'sangka', 'nga', 'gipahigayon', 'sa', 'Mandaue', 'City', 'niadtong', 'July', '16-24', ',', '2022.', 'Giila', 'ang', 'mga', 'nidaog', 'na', 'si', 'Chris', 'John', 'Paul', 'Dalagan', '(', '2', 'gold', ',', '1', 'silver', ')', ',', 'Shilloh', 'Kimberly', 'Flores', '(', '2', 'gold', ',', '1', 'bronze', ')', ',', 'John', 'Wenzel', 'Ganza', '(', '1', 'gold', ',', '1', 'silver', ')', ',', 'Kira', 'Jean', 'Ostan', '(', '3', 'bronze', ')', ',', 'Spencer', 'Flores', '(', '1', 'bronze', ')', ',', 'Gwen', 'Lherie', 'Lopez', '(', '1', 'bronze', ')', 'ug', 'si', 'Lorie', 'Ann', 'Esmaela', '(', '1', 'bronze', ')', '.'] 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, 0, 0, 0, 7, 8, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0] | cebuaner |
4,721 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KONSUMIDOR', ',', 'NAKURATAN', 'SA', 'IYANG', '₱6', 'MILLION', 'NGA', 'BAYRONON', 'SA', 'KURYENTE', 'Nag-viral', 'sa', 'social', 'media', 'ang', 'post', 'sa', 'usa', 'ka', 'konsumidor', 'sa', 'Nueva', 'Vizcaya', 'human', 'nga', 'niabot', 'og', 'kapin', '₱6', 'million', 'ang', 'bayronon', 'niini', 'sa', 'kuryente.', 'Bisan', 'pa', 'og', 'aminado', 'ang', 'konsumidor', 'nga', 'nagdahom', 'kining', 'taas', 'ang', 'iyang', 'bill', 'sa', 'kuryente', ',', 'wala', 'niya', 'damha', 'mga', 'moabot', 'kini', 'og', 'minilyon.', 'Tungod', 'niini', ',', 'gireklamo', 'sa', 'konsumidor', 'ngadto', 'sa', 'buhatan', 'sa', 'Nueva', 'Vizcaya', 'Electric', 'Cooperative', '(', 'Nuvelco', ')', 'ang', 'iyang', 'taas', 'nga', 'bayronon.', 'Dali', 'ra', 'sab', 'nga', 'gisolusyonan', 'sa', 'Nuvelco', 'ang', 'maong', 'problema', 'human', 'masayran', 'nga', 'dunay', 'kakulian', 'sa', 'metro', 'sa', 'konsumidor', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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] | cebuaner |
4,722 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', ',', 'GIPUSIL', 'PATAY', 'SA', 'GIINGONG', 'RIDING-IN-TANDEM', 'LUNGSOD', 'SA', 'ZAMBOANGUITA', 'Gipusil-patay', 'ang', 'usa', 'ka', 'lalaki', 'sa', 'giingong', 'riding-in-tandem', 'kilid', 'sa', 'national', 'highway', 'dapit', 'sa', 'Barangay', 'Basak', 'sa', 'lungsod', 'sa', 'Zamboanguita', 'karong', 'Biyernes', 'sa', 'hapon', ',', 'July', '29', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Arnold', 'Lagradilla', 'Tahitit', ',', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'kapulisan', ',', 'pauli', 'na', 'unta', 'ang', 'biktima', 'sakay', 'ang', 'iyang', 'motor', 'sa', 'dihang', 'gipusil', 'kini', 'sa', 'ulo', 'sa', 'wala', 'pa', 'mailhing', 'gunman', 'nga', 'nakamotor', 'sab.', 'Upat', 'ka', 'bala', 'gikan', 'sa', 'kalibre', '.45', 'nga', 'pistola', 'ang', 'narekober', 'gikan', 'sa', 'crime', 'scene.', 'Naglusad', 'na', 'karon', 'og', 'hot', 'pursuit', 'operation', 'ang', 'Zamboanguita', 'PNP', 'aron', 'madakpan', 'ang', 'mga', 'mamumuno.', 'Padayon', 'sab', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'sa', 'maong', 'pagpamusil', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,723 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['20-ANYOS', 'NGA', 'LALAKI', 'SA', 'SIATON', ',', 'NALUMOS', 'PATAY', 'Natapos', 'sa', 'trahedya', 'ang', 'sadya', 'unta', 'nga', 'pag-inom', 'sa', 'mga', 'paryente', 'human', 'nalumos-patay', 'ang', 'usa', 'ka', '20', 'anyos', 'nga', 'lalaki', 'sa', 'Brgy.', 'Giligaon', 'sa', 'lungsod', 'sa', 'Siaton', 'karong', 'Biyernes', 'sa', 'hapon', ',', 'Hulyo', '29', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Jerome', 'Jaos', 'Japinan', ',', 'lumolupyo', 'sa', 'Barangay', 'Bonawon', 'sa', 'naasoy', 'nga', 'lungsod.', 'Sumala', 'pa', 'sa', 'kapulisan', ',', 'giingong', 'hubog', 'kuno', 'ang', 'biktima', 'sa', 'dihang', 'nalumos', 'kini', 'tungod', 'kay', 'gikan', 'kini', 'nag-inom', 'sa', 'ilang', 'balay', 'kauban', 'ang', 'iyang', 'mga', 'paryente.', 'Human', 'niini', ',', 'nagdesisyon', 'ang', 'biktima', 'nga', 'maligo', 'sa', 'dagat.', 'Matud', 'pa', 'sa', 'nakasaksi', ',', 'nanghagad', 'pa', 'kuno', 'ang', 'biktima', 'nga', 'mouban', 'og', 'salom', 'niya', 'sa', 'lalom', 'nga', 'bahin', 'sa', 'dagat.', 'Apan', 'nakuratan', 'na', 'lang', 'sila', 'sa', 'dihang', 'wala', 'na', 'kini', 'nibalik', 'sa', 'ibabaw', 'sa', 'tubig.', 'Dinhi', 'na', 'nanawag', 'ang', 'mga', 'nakasaksi', 'sa', 'mga', 'awtoridad', 'aron', 'unta', 'luwason', 'si', 'Japinan.', 'Daling', 'gidala', 'sa', 'usa', 'ka', 'pampublikong', 'tambalanan', 'sa', 'Siaton', 'ang', 'biktima', 'apan', 'nakabsan', 'gyud', 'kini', 'sa', 'iyang', 'kinabuhi', '.'] 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, 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, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,724 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LIBRENG', 'DIALYSIS', 'ALANG', 'SA', 'MGA', 'KABUS', ',', 'GIDUSO', 'KARON', 'SA', 'KAMARA', 'Giduso', 'karon', 'sa', 'Kamara', 'ang', 'usa', 'ka', 'balaodnon', 'pagmando', 'sa', 'tanang', 'national', ',', 'regional', ',', 'ug', 'provincial', 'nga', 'pampublikong', 'tambalanan', 'nga', 'magtukod', 'og', 'dialysis', 'ward', 'nga', 'magtanyag', 'og', 'libreng', 'dialysis', 'alang', 'sa', 'mga', 'kabus.', 'Kini', 'human', 'gipasang-at', 'ni', 'Mandaue', 'Rep.', 'Emmarie', 'Ouano', 'Dizon', 'ang', 'maong', 'balaodnon', 'atol', 'sa', 'unang', 'regular', 'nga', 'sesyon', 'sa', '19th', 'Congress.', 'Matud', 'ni', 'Dizon', ',', 'gipanday', 'niya', 'ang', 'maong', 'balaodnon', 'subay', 'sa', 'pamahayag', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'sa', 'iyang', 'unang', 'State', 'of', 'the', 'Nation', 'Address', '(', 'SONA', ')', 'niadtong', 'Hulyo', '25', ',', 'diin', 'gisulti', 'ni', 'Marcos', 'nga', 'prayoridad', 'sa', 'iyang', 'administrasyon', 'ang', 'pagtukod', 'og', 'mga', 'tambalanan.', 'Dugang', 'pa', 'sa', 'kongresista', ',', 'lakip', 'ang', 'kidney', 'failure', 'sa', 'mga', 'nag-unang', 'hinungdan', 'sa', 'kamatayon', 'sa', 'mga', 'Pilipino.', 'Kung', 'hingpit', 'na', 'kining', 'mahimong', 'balaod', ',', 'hatagan', 'og', 'duha', 'ka', 'tuig', 'ang', 'tanang', 'mga', 'pampublikong', 'ospital', 'pagtukod', 'og', 'dialysis', 'ward.', 'Posibleng', 'multahan', 'og', 'P50,000', 'hangtod', 'P100,000', 'ang', 'mga', 'tagdumala', 'sa', 'mga', 'tambalanan', 'nga', 'dili', 'motamod', 'sa', 'maong', 'balaodnon', 'kung', 'hingpit', 'na', 'gyud', 'kining', 'mapirmahan', 'ug', 'mahimong', 'balaod', '.'] 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, 3, 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, 5, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 7, 8, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,725 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SUGOD', 'KARONG', 'OKTUBRE', '2022', ':', 'NORECO', 'II', ',', 'MAMUTOL', 'NA', 'OG', 'ACCOUNTS', 'NGA', '1', 'KA', 'BULAN', 'NANG', 'WALA', 'GIBAYRAN', 'Ipatuman', 'na'g', 'balik', 'sa', 'Negros', 'Oriental', 'II', 'Electric', 'Cooperative', '(', 'NORECO', 'II', ')', 'ang', 'diskoneksyon', 'sa', 'electric', 'service', 'sa', 'mga', 'accounts', 'nga', 'aduna'y', 'usa', 'ka', 'bulan', 'nga', 'wala', 'nabayaran', 'nga', 'bill', ',', 'base', 'sa', 'polisiya', 'sa', 'maong', 'kooperatiba.', 'Ipatuman', 'kini', 'sugod', 'karong', 'Oktubre', '2022.', 'Sumala', 'pa', 'sa', 'NORECO', 'II', ',', 'kini', 'tungod', 'mahuman', 'na', 'sa', 'Setyembre', '12', ',', '2022', 'ang', 'deklarasyon', 'nga', 'gipailalom', 'ang', 'tibuok', 'nasud', 'sa', 'state', 'of', 'calamity', 'tungod', 'sa', 'pandemya', 'sa', 'Covid-19', '.'] 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, 3, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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] | cebuaner |
4,726 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'PATAY', 'HUMAN', 'GITIGBAS', 'SA', 'PAMPLONA', 'Usa', 'ka', 'lalaki', 'ang', 'gitigbas', 'patay', 'sa', 'Barangay', 'Abante', 'sa', 'lungsod', 'sa', 'Pamplona', 'niadtong', 'Martes', 'sa', 'gabii', ',', 'Hulyo', '26', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Reymark', 'Torres', ',', '19', 'anyos', ',', 'ug', 'lumolupyo', 'sa', 'Sitio', 'Talay', 'sa', 'naasoy', 'nga', 'lugar.', 'Padayon', 'pang', 'gipangita', 'karon', 'ang', 'suspek', 'sa', 'krimen', 'nga', 'giila', 'nga', 'si', 'Rotchel', 'Andaya', ',', '23', 'anyos.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'nakadawat', 'og', 'text', 'ang', '16-anyos', 'nga', 'uyab', 'ni', 'Torres', 'gikan', 'sa', 'iyang', 'amahan', 'nga', 'papaulion', 'na', 'ang', 'biktima', 'kay', 'basin', 'patyon', 'kini', 'ni', 'Andaya.', 'Tungod', 'niini', ',', 'nipauli', 'si', 'Torres', 'uban', 'ang', 'iyang', 'uyab', 'gamit', 'ang', 'kaugalingong', 'motor.', 'Samtang', 'nagbiyahe', 'sila', ',', 'gipanaog', 'ni', 'Torres', 'ang', 'iyang', 'uyab', 'tungod', 'kay', 'danlog', 'kuno', 'ang', 'dalan.', 'Dinhi', 'na', 'sila', 'gibanhigan', 'sa', 'suspek', 'kinsa', 'nagpagawas', 'og', 'pinuti', 'ug', 'gitigbas', 'ang', 'liog', 'ni', 'Torres.', 'Dead', 'on', 'the', 'spot', 'ang', 'biktima.', 'Sa', 'pagkakaron', ',', 'padayon', 'pa', 'ang', 'hot', 'pursuit', 'operation', 'sa', 'kapulisan', 'aron', 'madakpan', 'ang', 'suspek', '.'] 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, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 1, 2, 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, 1, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,727 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['USA', 'KA', 'ORGANISASYON', 'SA', 'NEGOR', ',', 'NANGAYO', 'OG', 'HUSTISYA', 'SA', 'PAMILYANG', 'JACOLBE', 'Nanawagan', 'og', 'hustisya', 'ang', 'grupong', 'Kabataan', 'Para', 'sa', 'Karapatan', '(', 'KPK', ')', '-', 'Negros', 'Oriental', 'alang', 'sa', 'kanhing', 'daycare', 'worker', 'nga', 'si', 'Christina', 'Jacolbe', ',', 'iyang', '16-anyos', 'nga', 'anak', 'nga', 'babaye', ',', 'ug', 'ni', 'Rodan', 'Montero', 'kinsa', 'napatay', 'sa', 'mga', 'sundalo', 'sa', 'Philippine', 'Army', 'samtang', 'natulog', 'sa', 'ilang', 'payag', 'sa', 'Sitio', 'Banderahan', 'sa', 'Barangay', 'Trinidad', 'sa', 'dakbayan', 'sa', 'Guihulngan', 'niadtong', 'Hulyo', '26', ',', '2022.', 'Ang', 'lugar', 'diin', 'nahitabo', 'ang', 'insidente', ',', 'sukwahi', 'sa', 'unang', 'taho', 'sa', 'militar', 'kinsa', 'nag-ingon', 'nga', 'nahitabo', 'ang', 'insidente', 'sa', 'Canlaon', 'City.', 'Sumala', 'pa', 'sa', 'KPK', '-', 'NegOr', ',', 'gi-redtag', 'na', 'kuno', 'kaniadto', 'si', 'Jacolbe', 'sa', 'mga', 'awtoridad.', 'Buot', 'ipasabot', 'niini', ',', 'una', 'nang', 'gialegar', 'si', 'Jacolbe', 'nga', 'sakop', 'sa', 'rebeldeng', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', '.', 'Dugang', 'pa', 'sa', 'grupo', ',', 'gihimakak', 'sa', 'mga', 'residente', 'sa', 'maong', 'dapit', 'nga', 'dunay', 'engkuwentro', 'nga', 'nahitabo', ',', 'sukwahi', 'gihapon', 'sa', 'unang', 'taho', 'sa', 'militar', 'nga', 'duna', 'kuno'y', 'pinusilay', 'didto', 'nga', 'nilungtad', 'og', '10', 'minutos.', 'Gialegar', 'sab', 'sa', 'KPK', 'nga', 'gitamnan', 'kuno', 'sa', 'militar', 'ang', 'mga', 'armas', 'sa', 'pamilya', 'Jacolbe.', 'Wala', 'pay', 'komento', 'sa', 'pagkakaron', 'ang', 'Philippine', 'Army', 'nunot', 'sa', 'maong', 'mga', 'alegasyon', 'sa', 'KPK', '.'] 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, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 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, 5, 6, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 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, 1, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
4,728 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['5', 'PATAY', ',', '64', 'NASAMDAN', 'SA', 'KUSOG', 'NGA', 'LINOG', 'SA', 'LUZON', 'Moabot', 'na', 'sa', 'lima', 'ang', 'gikatahong', 'namatay', 'ug', '64', 'ang', 'naangol', 'sa', 'kusog', 'nga', 'magnitude', '7', 'nga', 'linog', 'nga', 'nitay-og', 'sa', 'Luzon', 'karong', 'Miyerkules', ',', 'July', '27', ',', '2022.', 'Sumala', 'pa', 'sa', 'National', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Council', '(', 'NDRRMC', ')', ',', '2', 'ang', 'namatay', 'sa', 'Benguet', ',', '1', 'sa', 'Abra', ',', '1', 'sa', 'Kalinga', ',', 'ug', 'laing', '1', 'sa', 'Cagayan', 'province.', 'Lakip', 'sa', 'mga', 'nakalas', 'mao', 'ang', 'usa', 'ka', 'construction', 'worker', 'sa', 'La', 'Trinidad', ',', 'Benguet', ',', 'kinsa', 'namatay', 'human', 'nahagsaan', 'og', 'bato.', 'Kapin', '400', 'ka', 'panimalay', 'na', 'sad', 'sa', 'amihanang', 'Luzon', 'ang', 'naguba', 'tungod', 'sa', 'linog', ',', 'ilabi', 'na', 'sa', 'Cordillera', 'Administrative', 'Region', '(', 'CAR', ')', '.', 'Kapin', '200', 'sab', 'ka', 'lungsod', 'sa', '15', 'ka', 'probinsya', 'ang', 'apektado', 'sa', 'linog', ',', 'sumala', 'pa', 'ni', 'Interior', 'Secretary', 'Benhur', 'Abalos', '.'] 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, 5, 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 6, 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, 0, 0, 0, 0, 5, 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, 1, 2, 0] | cebuaner |
4,729 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'NGA', 'DUNA'Y', 'KASO', 'SA', 'GIDILING', 'DRUGAS', ',', 'NASIKOP', 'SA', 'KADALANAN', 'SA', 'DUMAGUETE', 'Gi-aresto', 'sa', 'kapulisan', 'si', 'Felix', 'Adalim', 'Tubil', 'kinsa', 'aduna'y', 'Warrant', 'of', 'Arrest', '(', 'WOA', ')', 'sa', 'paglapas', 'sa', 'RA', '9165', 'o', 'Comprehensive', 'Dangerous', 'Drugs', 'Act', 'sa', 'Sitio', 'Canday-ong', 'sa', 'Barangay', 'Calindagan', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'City', 'mga', '11:05', 'sa', 'buntag', 'karong', 'adlawa', ',', 'July', '27', ',', '2022.', 'Sumala', 'pa', 'sa', 'report', ',', 'nagpahigayon', 'og', 'operation', 'on', 'service', 'o', 'WOA', 'ang', 'kapulisan', 'batok', 'sa', 'maong', 'akusado', 'apan', 'miikyas', 'kini', 'sakay', 'sa', 'Ford', 'Ranger', 'nga', 'niabot', 'sa', 'Dumaguete', 'City.', 'Nadakpan', 'ang', 'akusado', 'pinaagi', 'sa', 'pursuit', 'operation', 'sa', 'naasoy', 'nga', 'lugar', 'ug', 'gipahibalo', 'sa', 'hinungdan', 'ug', 'kinaiya', 'sa', 'iyang', 'pagkasikop.', 'Anaa', 'sa', 'sab', 'kini', 'sa', 'kustodiya', 'sa', 'kapulisan', '.'] 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, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 8, 8, 0, 5, 6, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 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, 0, 0, 0, 0] | cebuaner |
4,730 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PILIPINAS', 'GIILA', 'ISIP', 'USA', 'SA', 'MGA', 'PINAKA-STRESSED', ',', ''ANGRIEST', ',', ''', 'UG', ''SADDEST', ''', 'NGA', 'NASUD', 'SA', 'ASEAN', 'Giila', 'ang', 'Pilipinas', 'isip', '"', 'most', 'stressed', '"', 'ug', 'ikaduha', 'nga', '"', 'angriest', 'and', 'saddest', '"', 'nga', 'nasud', 'sa', 'tibuok', 'Southeast', 'Asia', 'sa', 'niaging', 'tuig', ',', 'sumala', 'sa', 'report', 'nga', 'gipahigayon', 'sa', 'global', 'analytic', 'firm', 'nga', 'Gallup.', 'Nakuha', 'ang', 'mga', 'resulta', 'sa', 'report', 'gikan', 'sa', 'mga', 'survey', 'nga', 'gihimo', 'sa', '2021', 'hangtod', 'sa', 'sayo', 'nga', 'bahin', 'sa', '2022.', 'Gipangutana', 'ang', 'mga', 'respondents', 'kung', 'gibati', 'ba', 'nila', 'ang', 'stress', ',', 'kasuko', ',', 'o', 'kasubo', 'sa', 'adlaw', 'sa', 'wala', 'pa', 'ang', 'survey.', 'Nagkalain-lain', 'ang', 'ilang', 'mga', 'tubag', 'gikan', 'sa', '"', 'oo', '"', ',', '"', 'dili', '"', ',', 'ug', '"', 'wala', 'kahibalo', 'o', 'nagdumili', 'sa', 'pagtubag.', '"', 'Sumala', 'pa', 'sa', 'report', ',', 'ang', 'Pilipinas', 'mao', 'ang', 'pinaka-stressed', 'nga', 'nasud', 'sa', 'tibuok', 'Southest', 'Asia', 'nga', 'aduna'y', '48', '%', ',', 'samtang', 'ang', 'Indonesia', 'mao', 'ang', 'aduna'y', 'pinakalabing', 'gamay', 'nga', 'stress', 'rate', 'nga', 'aduna', 'lamang', '13', '%', '.', 'Ang', 'Lao', 'PDR', 'mao', 'ang', 'pinakasuko', 'nga', 'nasud', 'nga', 'aduna'y', '29', '%', 'ug', 'gisundan', 'sa', 'Pilipinas', 'nga', 'aduna'y', '27', '%', '.', 'Pinakasubo', 'nga', 'nasud', 'ang', 'Cambodia', 'nga', 'aduna'y', '42', '%', 'ug', 'gisundan', 'sa', 'Pilipinas', 'nga', 'aduna'y', '35', '%', '.', 'Ang', 'Singapore', 'mao', 'ang', '"', 'least', 'angry', '"', 'ug', '"', 'least', 'sad', '"', 'nga', 'nasud', 'sa', 'tibuok', 'rehiyon.', 'Sumala', 'pa', 'sa', 'Gallup', ',', '12', '%', 'ang', 'nag-ingon', 'nga', 'sila', 'nasuko', 'ug', '11', '%', 'ang', 'nag-ingon', 'nga', 'sila', 'nakasinati', 'og', 'kasubo', '.'] 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. | [5, 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, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,731 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KA', 'GIINGONG', 'NPA', 'PATAY', 'SA', 'PINUSILAY', 'BATOK', 'SA', 'MILITAR', 'SA', 'CANLAON', 'Patay', 'ang', 'duha', 'ka', 'giingong', 'sakop', 'sa', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', 'sa', 'pinusilay', 'tali', 'sa', 'rebeldeng', 'grupo', 'ug', 'militar', 'sa', 'Sitio', 'Natuling', ',', 'Brgy.', 'Budlasan', ',', 'Canlaon', 'City', 'karong', 'adlawa', ',', 'July', '26', ',', '2022.', 'Giila', 'sa', 'report', 'sa', 'kapulisan', 'ang', 'mga', 'nakalas', 'nga', 'sila', 'si', 'Ernie', 'ug', 'Christina', 'Jaculbe', ',', 'kinsa', 'mga', 'giingong', 'opisyal', 'sa', 'NPA', 'nga', 'nag-operate', 'sa', 'maong', 'dapit.', 'Niabot', 'og', '10', 'ka', 'minutos', 'ang', 'pinusilay', 'tali', 'sa', 'giingong', 'NPA', 'ug', 'militar.', 'Duna', 'sab', 'na-recover', 'nga', 'armas', 'ang', 'militar', 'gikan', 'sa', 'naasoy', 'nga', 'rebeldeng', 'grupo', '.'] 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, 5, 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, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 3, 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] | cebuaner |
4,732 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'NGA', 'GIINGONG', 'HUBOG', 'SA', 'GUIHULNGAN', 'CITY', ',', 'NALUMOS', 'PATAY', 'Patay', 'ang', 'usa', 'ka', 'lalaki', 'human', 'nalumos', 'samtang', 'naligo', 'sa', 'suba', 'sa', 'Bateria', 'sa', 'Barangay', 'Poblacion', 'sa', 'dakbayan', 'sa', 'Guihulngan', 'niadtong', 'Hulyo', '24', ',', '2022.', 'Sumala', 'pa', 'sa', 'kapulisan', ',', 'gituohang', 'ubos', 'sa', 'impluwensiya', 'sa', 'ilimnong', 'makahubog', 'ang', 'biktima', 'kinsa', 'giila', 'nga', 'si', 'Helarion', 'Bolhano', 'Rellen', ',', '31-anyos', ',', 'ug', 'lumulupyo', 'sa', 'Roxas', 'Street', 'sa', 'naasoy', 'nga', 'lugar.', 'Dali', 'nga', 'gidala', 'ang', 'biktima', 'sa', 'Guihulngan', 'City', 'Hospital', 'aron', 'mahatagan', 'og', 'medikal', 'nga', 'pagtagad', 'apan', 'gideklarar', 'nga', 'dead-on-arrival', 'sa', 'attending', 'physician.', 'Nagpahigayon', 'sab', 'og', 'dugang', 'nga', 'imbestigasyon', 'ang', 'kapulisan', 'ug', 'nakig-alayon', 'na', 'sa', 'pamilya', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 5, 6, 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] | cebuaner |
4,733 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPIN', 'P1.9', 'MILLION', 'SA', 'GITUOHANG', 'SHABU', ',', 'NAKUMPISKAR', 'SA', 'KAPULISAN', 'SA', 'BUY-BUST', 'OPERATION', 'SA', 'BRGY.', 'BAGACAY', 'Narekober', 'sa', 'kapulisan', 'ang', 'mokabat', 'sa', 'kapin', 'P1.9', 'million', 'sa', 'gituohang', 'shabu', 'gikan', 'sa', 'gipahigayong', 'buy-bust', 'operation', 'sa', 'Purok', 'Tres', 'Rosas', 'sa', 'Baranggay', 'Bagacay', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'mga', '12:48', 'sa', 'kadlawaon', 'niadtong', 'Hulyo', '25', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'mga', 'nadakpan', 'nga', 'sila', 'si', 'Jovic', 'Calibo', 'Nano', ',', '24-anyos', ',', 'residente', 'sa', 'Cang-Alwang', 'Siquijor', ',', 'Siquijor', ',', 'ug', 'Janjan', 'Banaldia', ',', '21-anyos', ',', 'lumulupyo', 'sa', 'Purok', 'Gumamela', ',', 'Barangay', 'Bagacay', ',', 'Dumaguete', 'City.', 'Nakumpiskar', 'sa', 'kapulisan', 'ang', 'duha', 'ka', 'large', 'size', 'transparent', 'plastic', 'sachet', 'nga', 'aduna'y', 'white', 'crystalline', 'substance', 'sa', 'gituohang', 'shabu', ';', 'aduna', 'kini', 'gibug-aton', 'nga', '280.56', 'grams', 'ug', 'SDP', 'nga', 'Php1,907,808.00', ',', 'ug', 'pipila', 'ka', 'small', 'ug', 'medium', 'size', 'nga', 'pakete', 'sa', 'gituohang', 'shabu.', 'Nakumpiskar', 'sab', 'ang', 'usa', 'ka', 'cellphone', ',', 'usa', 'ka', 'digital', 'weighing', 'scale', ',', 'usa', 'ka', 'pack', 'sa', 'ice', 'wrapper', 'plastic', ';', 'usa', 'ka', 'canister', ',', 'travelling', 'bag', ',', 'ug', 'duha', 'ka', 'piraso', 'sa', 'usa', 'ka', 'libo', 'ka', 'peso', 'bill', 'isip', 'buy', 'bust', 'money.', 'Gidala', 'ang', 'mga', 'nadakpan', 'sa', 'Police', 'Station', 'alang', 'sa', 'booking', 'ug', 'saktong', 'dispo', 'samtang', 'giandam', 'ang', 'documentary', 'requirements', 'alang', 'sa', 'pagpasaka', 'sa', 'korte', '.'] 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 0, 0, 0, 5, 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, 5, 6, 6, 6, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 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] | cebuaner |
4,734 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagtanyag', 'karon', 'ang', 'AirAsia', 'nagtanyag', 'og', 'piso', 'sale', 'sa', 'pipila', 'ka', 'domestic', 'flights', 'niini.', 'Lakip', 'sa', 'mga', 'destinasyon', 'nga', 'gilangkuban', 'sa', 'piso', 'sale', 'mao', 'ang', 'Dumaguete', ',', 'Roxas', ',', 'Caticlan', ',', 'ug', 'Tagbilaran.', 'Duna', 'puy', 'diskwento', 'sa', 'pletehan', 'nga', 'gitanyag', 'ang', 'maong', 'airline', 'alang', 'sa', 'pipila', 'ka', 'international', 'destinations', 'niini', 'sama', 'sa', 'Bangkok', 'ug', 'Singapore.', 'Magsugod', 'sa', 'P688', 'ang', 'base', 'fare', 'sa', 'maong', 'mga', 'ruta.', 'Ang', 'maong', 'promo', 'itanyag', 'sa', 'AirAsia', 'gikan', 'Hulyo', '25', 'hangtod', '31', ',', '2022.', 'Ang', 'travel', 'period', 'sa', 'promo', 'gikan', 'sa', 'Oktubre', '1', ',', '2022', 'hangtod', 'Oktubre', '28', ',', '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, 3, 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, 5, 0, 0, 5, 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, 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] | cebuaner |
4,735 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gideklarar', 'na', 'sa', 'World', 'Health', 'Organization', '(', 'WHO', ')', 'ang', 'sakit', 'nga', 'monkeypox', 'isip', 'usa', 'ka', 'public', 'health', 'emergency', 'of', 'international', 'concern.', 'Mao', 'kini', 'ang', 'kinatas-an', 'nga', 'alarma', 'sa', 'WHO', 'nunot', 'sa', 'mga', 'makatakod', 'nga', 'sakit', 'nga', 'nagkatap', 'na', 'sa', 'nagkalain-laing', 'bahin', 'sa', 'kalibutan', '.'] 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, 0, 0, 0, 7, 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] | cebuaner |
4,736 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'unang', 'higayon', 'sukad', 'nagsugod', 'ang', 'pandemya', 'sa', '#', 'COVID19', ',', 'kompirmadong', 'mobalik', 'na', 'sab', 'ang', 'mga', 'kalihokan', 'pagsaulog', 'sa', 'MassKara', 'Festival', 'sa', 'dakbayan', 'sa', 'Bacolod', 'karong', 'Oktubre', '1-23', ',', '2022.', 'Mahinumduman', 'nga', 'duha', 'ka', 'tuig', 'nga', 'wala', 'gipahigayon', 'ang', 'MassKara', 'tungod', 'sa', 'pandemya.', 'Laag', 'ta', 'puhon', 'sa', 'City', 'of', 'Smiles', ',', 'beshie', '!'] 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0] | cebuaner |
4,737 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gilaoman', 'na', 'sab', 'ang', 'lain', 'na', 'pud', 'nga', 'pag-ubos', 'sa', 'presyo', 'sa', 'lana', 'sunod', 'semana', '.'] 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] | cebuaner |
4,738 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'GIPUSIL', 'PATAY', 'SA', 'BARANGAY', 'CADAWINONAN', ',', 'DUMAGUETE', 'CITY', 'Patay', 'ang', 'usa', 'ka', 'lalaki', 'sa', 'Kalye', '2', ',', 'Housing', 'Project', ',', 'Barangay', 'Cadawinonan', ',', 'Dumaguete', 'City', 'human', 'kini', 'gipusil', 'kagabii', ',', 'Hulyo', '21', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Aaron', 'Dale', 'Sardinio', 'Tomaroy', ',', '32', 'anyos', ',', 'lumolupyo', 'sa', 'naasoy', 'nga', 'dapit.', 'Sumala', 'sa', 'live-in', 'partner', 'sa', 'biktima', 'na', 'si', 'Jonnah', 'Bagarinao', ',', 'naglingkod', 'lang', 'kini', 'dapit', 'sa', 'Barangay', 'Community', 'Building', 'sa', 'dihang', 'giduol', 'ug', 'gipusil', 'kini', 'sa', 'usa', 'ka', 'wala', 'pa', 'mailhing', 'gunman.', 'Human', 'sa', 'pagpamusil', ',', 'daling', 'nieskapo', 'ang', 'mamumuno', 'samtang', 'nagsakay', 'sa', 'iyang', 'motor.', 'Gidala', 'sa', 'tambalanan', 'si', 'Tomaroy', 'apan', 'nakabsan', 'gyud', 'kini', 'sa', 'iyang', 'kinabuhi.', 'Base', 'sa', 'inisyal', 'nga', 'imbestigasyon', 'sa', 'pulisya', ',', 'duha', 'ka', 'samad', 'pinusilan', 'ang', 'nahiagoman', 'sa', 'biktima.', 'Lima', 'ka', 'bala', 'sab', 'ang', 'na-recover', 'gikan', 'sa', 'crime', 'scene.', 'Nagpadayon', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'sa', 'maong', 'pagpamusil', '.'] 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, 6, 6, 6, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 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, 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, 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] | cebuaner |
4,739 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'KANHING', 'REBELDE', ',', 'NAKADAWAT', 'OG', 'AYUDA', 'GIKAN', 'SA', 'GOBYERNO', 'SA', 'NEGOR', 'Walo', 'ka', 'mga', 'kanhing', 'rebelde', '(', 'FRs', ')', 'ang', 'nakadawat', 'og', 'livelihood', 'assistance', 'gikan', 'sa', 'gobyerno', 'sa', 'Guihulngan', 'City.', 'Ubos', 'sa', 'programa', 'sa', 'Pangkabuhayan', 'sa', 'Pagbangon', 'at', 'Ginhawa', ',', 'gihatag', 'sa', 'Department', 'of', 'Trade', 'and', 'Industry', '(', 'DTI', ')', 'ang', 'livelihood', 'kits', 'nga', 'gilangkuban', 'sa', '10', 'ka', 'kanding', 'alang', 'sa', 'lima', 'ka', 'kanhing', 'rebelde', ',', 'duha', 'ka', 'sari-sari', 'store', 'kits', ',', 'ug', 'usa', 'ka', 'cellphone', 'nga', 'aduna'y', 'freeloader.', 'Sa', 'walo', 'ka', 'peace', 'advocate', 'recipients', 'o', 'FRs', ',', 'tulo', 'ang', 'kanhi', 'fighter', 'combatants', 'sa', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', 'ug', 'lima', 'ang', 'miyembro', 'sa', 'Militia', 'ng', 'Bayan.', 'Sumala', 'pa', 'ni', '2Lt.', 'Mary', 'Liza', 'De', 'Guzman', ',', 'civil', 'military', 'operations', 'officer', 'sa', '62nd', 'Infantry', 'Battalion', 'sa', 'Philippine', 'Army.', 'Nakadesisyon', 'kini', 'sila', 'nga', 'mo-surrender', 'tungod', 'sa', 'grabeng', 'kalisod', 'ug', 'pagpabaya', 'sa', 'ilang', 'mga', 'lider', 'sa', 'organisasyon.', 'Gibati', 'sab', 'nila', 'ang', 'pressure', 'gikan', 'sa', 'nakatutok', 'nga', 'mga', 'operasyong', 'militar', 'sa', 'Philippine', 'Army', 'ug', 'kapulisan.', 'Sa', 'pagkasayod', 'nga', 'makadawat', 'sila', 'og', 'livelihood', 'sustainment', 'programs', 'nga', 'gihatag', 'sa', 'National', 'and', 'Local', 'Task', 'Force', 'on', 'End', 'Local', 'Communist', 'Armed', 'Conflict', '(', 'ELCAC', ')', ',', 'nakombinsir', 'sila', 'nga', 'biyaan', 'ang', 'ilang', 'kanhing', 'lisud', 'nga', 'kinabuhi.', 'Niadtong', 'niaging', 'bulan', ',', 'mahinumduman', 'nga', 'nakadawat', 'sab', 'og', 'livelihood', 'assistance', 'ang', 'unang', 'batch', 'sa', '24', 'ka', 'FRs', '.'] 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 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, 0, 0, 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, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 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] | cebuaner |
4,740 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KA', 'GAMER', 'SA', 'CHINA', ',', 'NAGPAKULATA', 'ARON', 'MAKABAKASYON', 'Duha', 'ka', 'gamers', 'nga', 'aduna'y', 'sikat', 'nga', 'live', 'streams', 'sa', 'China', 'ang', 'giingong', 'nagpakulata', 'sa', 'walo', 'ka', 'lalake', 'aron', 'lang', 'makabakasyon', 'gikan', 'sa', 'ilang', 'trabaho.', 'Sumala', 'pa', 'sa', 'Jiupai', 'News', ',', 'gibayaran', 'sa', 'duha', 'ka', 'gamers', 'ang', 'walo', 'ka', 'mga', 'lalake', 'gikan', 'sa', 'Guangdon', 'province', 'aron', 'kulatahon', 'kuno', 'sila', 'para', 'makapahuway', 'sa', 'ilang', 'pag-host', 'sa', 'ilang', 'gaming', 'streams.', 'Gikuhaan', 'og', 'video', 'ang', 'peke', 'nga', 'pagkulata', 'ug', 'gi-post', 'kini', 'online', 'niadtong', 'Hulyo', '10.', 'Makita', 'sa', 'video', 'nga', 'naghubo', 'ang', 'usa', 'sa', 'mga', 'gamers', 'ug', 'nahulog', 'sa', 'yuta', 'samtang', 'gikulata.', 'Dali', 'nga', 'nitawag', 'sa', 'kapulisan', 'ang', 'mga', 'followers', 'sa', 'maong', 'mga', 'gamers', 'human', 'ma-post', 'ang', 'video.', 'Nasayran', 'sab', 'kapulisan', 'nga', 'dili', 'tinuod', 'ang', 'maong', 'insidente', 'human', 'miangkon', 'ang', 'mga', 'gamers', 'ngadto', 'sa', 'mga', 'imbestigador', '.'] 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, 5, 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, 3, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,741 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GIINGONG', 'KALABIRA', 'SA', 'TAWO', ',', 'NAPALGAN', 'SA', 'LUNGSOD', 'SA', 'SIBULAN', 'Nakuratan', 'ang', 'pipila', 'ka', 'residente', 'sa', 'Purok', '1', ',', 'Barangay', 'Bolocboloc', 'sa', 'lungsod', 'sa', 'Sibulan', 'human', 'mapalgan', 'didto', 'ang', 'giingong', 'kalabira', 'sa', 'tawo', 'karong', 'buntag', ',', 'July', '21', ',', '2022.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'napalgan', 'sa', 'trabahador', 'nga', 'si', 'Elmer', 'Bartolome', 'ang', 'maong', 'kalabira', 'samtang', 'nagbungkal', 'og', 'yuta', 'alang', 'sa', 'gihimo', 'nila', 'nga', 'swimming', 'pool', 'didto.', 'Sa', 'usa', 'ka', 'private', 'property', 'napalgan', 'ang', 'mga', 'naasoy', 'nga', 'kalabira.', 'Nagpadayon', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'sa', 'Sibulan', 'nunot', '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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 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, 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, 5, 0, 0, 0] | cebuaner |
4,742 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['IBON', ':', 'MATAG', 'PILIPINO', 'ADUNA'Y', 'UTANG', 'NGA', 'P112,000', 'TUNGOD', 'SA', 'P12.5-TRILYON', 'NGA', 'UTANG', 'SA', 'PILIPINAS', 'Aduna'y', 'utang', 'nga', 'kapin', 'P112,000', 'ang', 'matag', 'Pilipino', 'nga', 'bayaran', 'pinaagi', 'sa', 'buhis', 'kung', 'bahinon', 'sa', '110', 'milyon', 'nga', 'populasyon', 'sa', 'nasud', 'ang', 'nagdagan', 'nga', 'utang', 'sa', 'gobyerno', 'nga', 'P12.5-trilyon.', 'Sumala', 'pa', 'sa', 'economic', 'think', 'tank', 'nga', 'IBON', 'Foundation', 'niadtong', 'Miyerkules', ',', 'July', '20', ',', '2022.', 'Base', 'sa', 'datos', 'nga', 'gipresentar', 'sa', 'IBON', ',', 'aduna'y', 'utang', 'nga', 'P112,678', 'ang', 'matag', 'Pilipino', 'samtang', 'aduna'y', 'utang', 'nga', 'P474,543', 'ang', 'matag', 'pamilya', 'kung', 'bahinon', 'kini', 'sa', '26.3', 'milyones', 'nga', 'pamilya', 'sa', 'nasud.', 'Ang', 'outstanding', 'nga', 'utang', 'sa', 'gobyerno', 'niabot', 'sa', 'P12.49', 'trilyon', 'sa', 'katapusan', 'sa', 'Mayo', ',', 'diin', 'P8.66', 'trilyon', 'ang', 'gikan', 'sa', 'lokal', 'samtang', 'P3.83', 'trilyon', 'gikan', 'sa', 'langyaw', 'nga', 'tinubdan', '.'] 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, 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,743 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LANGYAW', 'NGA', 'BANA', 'SA', 'NURSE', 'NGA', 'NAPALGANG', 'PATAY', 'SA', 'VALENCIA', ',', 'NI-SURRENDER', 'SA', 'NBI', 'Boluntaryo', 'nga', 'mitahan', 'sa', 'buhatan', 'sa', 'National', 'Bureau', 'of', 'Investigation', '(', 'NBI', ')', 'ang', 'bana', 'sa', 'nurse', 'nga', 'si', 'Maribel', 'Glazier', 'kinsa', 'napalgang', 'patay', 'sa', 'Sitio', 'Ogahong', ',', 'Barangay', 'Palinpinon', ',', 'Valencia', 'niadtong', 'July', '19', ',', '2022.', 'Mi-surrender', 'sa', 'NBI', 'ang', 'American', 'national', 'nga', 'si', 'Daniel', 'Glazier', ',', '55', 'anyos', ',', 'niadtong', 'Miyerkules', ',', 'July', '20', ',', '2022.', 'Human', 'niini', ',', 'nipahigayon', 'og', 'crime', 'scene', 'investigation', '/', 'ocular', 'inspection', 'ang', 'mga', 'awtoridad', 'sa', 'pinuy-anan', 'sa', 'magtiayon', 'sa', 'Sto.', 'Rosario', 'Heights', 'Subdivision', ',', 'Barangay', 'Junob', ',', 'Dumaguete', 'City.', 'Atol', 'sa', 'imbestigasyon', ',', 'wala'y', 'nadiskubre', 'ang', 'grupo', 'og', 'mga', 'timailhan', 'sa', 'ebidensya', 'nga', 'may', 'kalabutan', 'o', 'nagpamatuod', 'sa', 'krimen', 'bisan', 'pisikal', ',', 'butang', 'o', 'forensic.', 'Apan', 'aduna'y', 'nakuha', 'nga', 'mga', 'butang', 'ang', 'SOCO', 'ug', 'gi-turn', 'over', 'ug', 'ubos', 'na', 'sa', 'kustodiya', 'sa', 'Valencia', 'MPS.', 'Anaa', 'na', 'karon', 'sa', 'kustodiya', 'sa', 'NBI', 'si', 'Glazier', 'kinsa', 'gikonsiderar', 'nga', 'person', 'of', 'interest', 'sa', 'maong', 'krimen.', 'Anaa', 'na', 'sab', 'sa', 'kustodiya', 'sa', 'DSWD', 'Dumaguete', 'ang', 'duha', 'ka', 'bata', 'kinsa', 'anak', 'sa', 'biktima', 'nga', 'nag-edad', '9', 'ug', '10', 'anyos', '.'] 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, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,744 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['VACCINATIONS', 'UG', 'COVID-19', 'TESTS', ',', 'DILI', 'NA', 'KINAHANGLAN', 'ARON', 'MAKASULOD', 'SA', 'SILLIMAN', 'CAMPUS', 'Gilibkas', 'na', 'sa', 'Silliman', 'University', '(', 'SU', ')', 'ang', 'rekisitos', 'nga', 'vaccination', 'ug', 'ang', 'negative', 'RT-PCR', 'ug', 'antigen', 'test', 'results', 'aron', 'makasulod', 'sa', 'campus', 'niini.', 'Kini', 'gianunsyo', 'sa', 'maong', 'tunghaan', 'sa', 'usa', 'ka', 'pamahayag', 'karong', 'adlawa', ',', 'July', '20', ',', '2022.', 'Apan', 'kinahanglan', 'gihapong', 'mopirma', 'og', 'waiver', 'form', 'sa', 'tunghaan', 'ang', 'mga', 'estudyante', 'ug', 'ginikanan', 'nga', 'dili', 'pa', 'bakunado', 'aron', 'sila', 'tugotan', 'pagsulod', 'sa', 'SU', 'campus.', 'Gimando', 'sab', 'sa', 'kadagkuan', 'sa', 'SU', 'ang', 'paggamit', 'sa', 'air', 'conditioner', 'nga', 'dunay', 'exhaust', 'fan', 'sa', 'tanang', 'mga', 'classroom', 'ug', 'buhatan', 'sa', 'tunghaan.', 'Kini', 'aron', 'malikayan', 'ang', 'posibleng', 'pagtakod', 'sa', '#', 'COVID19.', 'Modawat', 'na', 'sab', 'og', 'mga', 'tinun-an', 'ang', 'mga', 'dormitoryo', 'sa', 'SU', ',', 'apan', 'sila', 'gikinahanglan', 'nga', 'bakunado', 'batok', 'COVID-19', '.'] 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, 0, 5, 6, 0, 0, 0, 3, 4, 4, 4, 4, 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, 0, 0, 0, 0, 0, 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, 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, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
4,745 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MODERN', 'BUSES', 'SA', 'DUMAGUETE', ',', 'NAG-ARANGKADA', 'NA', 'Nagsugod', 'na', 'og', 'pasada', 'karon', 'ang', 'mga', 'modernong', 'public', 'utility', 'vehicles', '(', 'PUV', ')', 'nga', 'nagtuyok', 'sa', 'dakbayan', 'sa', 'Dumaguete', ',', 'ingon', 'man', 'sa', 'mga', 'lungsod', 'sa', 'Valencia', 'ug', 'Bacong.', 'Gilaumang', 'mas', 'mahimong', 'hamugaway', 'ang', 'pagbiyahe', 'sa', 'mga', 'commuter', 'ning', 'dakbayan', 'tungod', 'kay', 'air-conditioned', 'man', 'ang', 'mga', 'naasoy', 'nga', 'bus', '.'] 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, 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, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,746 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DEPED', 'EXEC', ':', 'ENGLISH', ',', 'FILIPINO', 'GAMITON', 'ISIP', 'MEDIUM', 'OF', 'INSTRUCTION', 'SA', 'KINDERGARTEN', 'Kinahanglan', 'nga', 'gamiton', 'ang', 'English', 'ug', 'Filipino', 'isip', 'media', 'of', 'instruction', 'sa', 'mga', 'eskwelahan', 'sukad', 'pa', 'sa', 'kindergarten', ',', 'sumala', 'pa', 'sa', 'usa', 'ka', 'opisyal', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'Miyerkules', ',', 'July', '20', ',', '2022.', 'Ubos', 'sa', 'K-12', 'nga', 'balaod', ',', 'kinahanglan', 'nga', 'gamiton', 'ang', 'mother', 'tongue', 'isip', 'medium', 'of', 'instruction', 'gikan', 'sa', 'kindergarten', 'hangtod', 'sa', 'Grade', '3.', 'Apan', 'sumala', 'pa', 'ni', 'DepEd', 'Undersecretary', 'Epimaco', 'Densing', ',', 'angay', 'lang', 'nga', 'gamiton', 'ang', 'mother', 'tongue', 'isip', 'exception', 'ug', 'English', 'ug', 'Filipino', 'ang', 'gamiton', 'isip', 'media', 'of', 'instruction.', 'Gitumbok', 'ni', 'Densing', 'ang', '2018', 'ranking', 'sa', 'Pilipinas', 'sa', 'Program', 'for', 'International', 'Student', 'Assessment', '(', 'PISA', ')', ',', 'diin', 'ang', 'mga', 'estudyanteng', 'Pilipino', 'mao'y', 'pinakagrabe', 'sa', '79', 'ka', 'nasod', 'sa', 'reading', 'comprehension', 'ug', 'ikaduha', 'sa', 'pinakaubos', 'sa', 'mathematical', 'ug', 'scientific', 'literacy.', 'Matod', 'niya', 'nga', 'ang', 'kasamtangang', 'pamaagi', 'mahimo', 'lamang', 'magamit', 'sa', 'mga', 'hilit', 'nga', 'lugar', 'sa', 'nasud', 'diin', 'ang', 'English', 'ug', 'Filipino', 'nga', 'mga', 'pinulongan', 'wala', 'gipaila', 'sa', 'mga', 'residente', ',', 'busa', 'ang', 'mother', 'tongue', 'kinahanglan', 'nga', 'gamiton', 'isip', 'pinulongan', 'alang', 'sa', 'unang', 'pagkat-on', 'sa', 'bata.', 'Si', 'Densing', 'niingon', 'nga', 'iya', 'nang', 'nahisgutan', 'ang', 'iyang', 'sugyot', 'uban', 'ni', 'Education', 'Secretary', 'ug', 'Vice', 'President', 'Sara', 'Duterte', 'apan', 'wala', 'pa'y', 'opisyal', 'nga', 'kamanduan', 'sa', 'maong', 'butang', '.'] 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. | [3, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 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, 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, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 5, 0, 7, 8, 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, 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, 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, 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] | cebuaner |
4,747 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MILITAR', 'UG', 'GIINGONG', 'MGA', 'NPA', 'NAGPINUSILAY', 'SA', 'GUIHULNGAN', 'CITY', ',', '1', 'GIKATAHONG', 'PATAY', 'Usa', 'ka', 'giingong', 'miyembro', 'sa', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', 'ang', 'gikatahong', 'napatay', 'sa', 'engkwentro', 'tali', 'sa', 'maong', 'grupo', 'ug', 'sa', 'militar', 'karong', 'adlawa', ',', 'July', '20', ',', '2022', ',', 'sa', 'Guihulngan', 'City.', 'Sumala', 'pa', 'sa', 'taho', 'sa', 'militar', ',', 'nagpinusilay', 'ang', 'mga', 'sundalo', 'sa', '62nd', 'Infantry', 'Batallion', 'sa', 'Philippine', 'Army', 'ug', 'mga', 'giingong', 'NPA', 'sa', 'Sitio', 'Taluktok', 'sa', 'Brgy.', 'Sandayao', 'sa', 'naasoy', 'nga', 'dakbayan.', 'Nilungtad', 'og', 'lima', 'ka', 'minuto', 'ang', 'sinukliay', 'sa', 'bala', 'tali', 'sa', 'duha', 'ka', 'habig.', 'Dugang', 'pa', 'sa', 'militar', ',', 'nieskapo', 'ang', 'mga', 'giingong', 'sakop', 'sa', 'NPA', 'human', 'sa', 'maong', 'engkuwentro', 'ug', 'gibilin', 'ang', 'ilang', 'patay', 'nga', 'kauban.', 'Padayon', 'pang', 'giila', 'sa', 'pagkakaron', 'ang', 'napatay', '.'] 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, 5, 6, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 0, 0, 0, 3, 0, 5, 6, 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, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,748 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAG-UYAB', 'GIATAKE', 'SAMTANG', 'NAG-DATE', ';', 'LALAKE', 'GIDUNGGAB', 'PATAY', ',', 'BABAYE', 'GIINGONG', 'GILUGOS', 'SA', 'USA', 'KA', 'SUSPEK', 'Usa', 'ka', '24-anyos', 'nga', 'delivery', 'driver', 'ang', 'gidunggab', 'patay', 'samtang', 'ang', 'iyang', '17-anyos', 'nga', 'uyab', 'giingong', 'gilugos', 'sa', 'pag-atake', 'sa', 'wala', 'pa', 'mailhing', 'mga', 'lalaki', 'samtang', 'sila', 'nag-date', 'sa', 'syudad', 'sa', 'Cagayan', 'de', 'Oro.', 'Nahitabo', 'ang', 'insidente', 'mga', 'tungang', 'gabii', 'niadtong', 'Hulyo', '17', 'sa', 'may', 'Coastal', 'Road', 'sa', 'Barangay', 'Lapasan', ',', 'sumala', 'pa', 'ni', 'Cagayan', 'de', 'Oro', 'City', 'Police', 'Office', 'city', 'director', 'Police', 'Colonel', 'Aaron', 'Mandia.', 'Giila', 'ni', 'Mandia', 'ang', 'biktima', 'nga', 'si', 'Cris', 'Sabaldana', 'Semaña', ',', 'residente', 'sa', 'Western', 'Kolambog', ',', 'Lapasan.', 'Matod', 'pa', 'sa', 'imbestigasyon', ',', 'nag-date', 'si', 'Semaña', 'ug', 'iyang', 'uyab', 'sa', 'dihang', 'giduol', 'sa', 'mga', 'suspek', 'ug', 'gitionan', 'sila', 'og', 'kutsilyo', 'ug', 'giguyod', 'sa', 'kasagbutan', 'nga', 'ngitngit', 'nga', 'bahin', 'sa', 'dalan.', 'Nakaangkon', 'og', 'samad', 'dinunggaban', 'si', 'Semaña', 'og', '10', 'ka', 'higayon', 'sa', 'lawas', 'samtang', 'ang', 'iyang', 'uyab', 'giingong', 'gilugos', 'sa', 'usa', 'sa', 'mga', 'suspek.', 'Misulay', 'pag', 'sukol', 'ang', 'babaye', 'nga', 'biktima', 'apan', 'gisumbag', 'ug', 'gisipa', 'kini', 'sa', 'suspek', ',', 'sumala', 'ni', 'Mandia.', 'Daling', 'gidala', 'sa', 'Oro', 'Rescue', 'si', 'Semaña', 'sa', 'JR', 'Borja', 'Hospital', 'apan', 'gideklarar', 'kini', 'nga', 'dead', 'on', 'arrival.', 'Gipaabot', 'pa', 'sa', 'kapolisan', 'ang', 'resulta', 'sa', 'medico-legal', 'sa', 'babayeng', 'biktima', 'alang', 'sa', 'kompirmasyon.', 'Sa', 'laing', 'bahin', ',', 'si', 'Mandia', 'niingon', 'nga', 'posibleng', 'aduna'y', 'tulo', 'ka', 'mga', 'suspek', 'sa', 'insidente', 'tungod', 'kay', 'aduna'y', 'usa', 'ka', 'lookout', 'gawas', 'sa', 'duha', 'ka', 'mga', 'tawo', 'nga', 'miduol', 'sa', 'mga', 'biktima.', 'Nagpadayon', 'ang', 'imbestigasyon', 'samtang', 'ang', 'mga', 'operatiba', 'sa', 'kapulisan', 'gimanduan', 'nga', 'mohimo', 'og', 'hot', 'pursuit', 'operation', 'aron', 'masikop', 'ang', 'mga', 'suspek.', '#', 'NewsBite', '|', 'via', 'Aelleiah', 'Kaye', 'Cortez', ',', 'Foundation', 'University', 'intern'] 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, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 5, 6, 6, 6, 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, 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, 5, 6, 0, 1, 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, 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] | cebuaner |
4,749 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UNANG', 'DUHA', 'KA', 'KASO', 'SA', 'MAKAMATAY', 'NGA', 'MARBURG', 'VIRUS', ',', 'NAILA', 'SA', 'GHANA', ',', 'AFRICA', 'Gibutyag', 'sa', 'mga', 'awtoridad', 'sa', 'kahimsog', 'sa', 'Ghana', ',', 'Africa', 'niadtong', 'Domingo', 'nga', 'nakatala', 'sila', 'sa', 'unang', 'duha', 'ka', 'kaso', 'sa', 'giingong', 'makamatay', 'nga', 'Marburg', 'virus.', 'Mao', 'kini', 'ang', 'kinaunahang', 'higayon', 'nga', 'natala', 'ang', 'maong', 'kagaw', 'sa', 'nasud.', 'Giingong', 'makamatay', 'ang', 'Marburg', 'virus', 'sama', 'sa', 'Ebola', 'virus.', 'Lakip', 'sa', 'mga', 'sintomas', 'niini', 'ang', 'taas', 'nga', 'hilanat', 'ug', 'ang', 'internal', 'ug', 'external', 'bleeding.', 'Sa', 'pagkakaron', ',', '98', 'ka', 'tawo', 'ang', 'gi-isolate', 'sa', 'Ghana', 'human', 'sila', 'giila', 'nga', 'close', 'contacts', 'sa', 'mga', 'kompirmadong', 'kaso', 'sa', 'Marburg', 'virus.', 'Wala', 'pay', 'bakuna', 'o', 'tambal', 'kining', 'Marburg', 'virus', 'sa', 'pagkakaron', '.'] 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, 7, 8, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 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, 7, 8, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0] | cebuaner |
4,750 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TULFO', ':', '1.3', 'MILYON', 'KA', 'BENEPISYARYO', 'SA', '4Ps', ',', 'TAKTAKON', 'Gitakdang', 'taktakon', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', 'ang', '1.3', 'milyon', 'ka', 'benepisyaryo', 'sa', 'Pantawid', 'Pamilyang', 'Pilipino', 'Program', '(', '4Ps', ')', '.', 'Sumala', 'pa', 'ni', 'DSWD', 'Secretary', 'Erwin', 'Tulfo', ',', 'dili', 'na', 'kuno', 'maisip', 'nga', '"', 'pobre', '"', 'ang', 'maong', 'mga', 'indibiduwal', ',', 'hinungdan', 'nga', 'dili', 'na', 'sila', 'kuwalipikado', 'nga', 'makadawat', 'og', 'ayuda', 'gikan', 'sa', '4Ps.', 'Matod', 'pa', 'ni', 'Press', 'Secretary', 'Trixie', 'Cruz-Angeles', ',', 'moabot', 'sa', 'P15', 'bilyon', 'ang', 'madaginot', 'sa', 'gobyerno', 'kung', 'taktakon', 'ang', 'mga', 'naasoy', 'nga', 'benepisyaryo.', 'Dugang', 'pa', 'ni', 'Tulfo', ',', 'dili', 'pa', 'pinal', 'ang', '1.3', 'milyon', 'nga', 'benepisyaryo', 'nga', 'taktakon', 'sa', 'pagkakaron', '.'] 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, 7, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 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, 0, 0, 0, 0, 0, 0, 7, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,751 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGLULUBI', ',', 'GIPUSIL', 'PATAY', 'SA', 'BAYAWAN', 'CITY', 'Patay', 'ang', 'usa', 'ka', 'maglulubi', 'human', 'siya', 'gipusil', 'sa', 'wala', 'pa', 'mailhing', 'maumuno', 'sa', 'Sitio', 'Tavera', ',', 'Barangay', 'Nangka', ',', 'Bayawan', 'City', 'karong', 'gabii', ',', 'July', '19', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Alberto', 'Badoy', ',', '45', 'anyos', ',', 'lumulupyo', 'sa', 'naasoy', 'nga', 'barangay.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'nagmaneho', 'si', 'Badoy', 'sa', 'iyang', 'motorsiklo', 'pauli', 'gikan', 'sa', 'iyang', 'kalubihan', 'sa', 'dihang', 'gibanhigan', 'kini', 'sa', 'wala', 'pa', 'mailhing', 'suspek.', 'Gipusil', 'sa', 'suspek', 'ang', 'biktima', 'og', 'makadaghan', 'sa', 'nagkalain-laing', 'bahin', 'sa', 'iyang', 'lawas', ',', 'ug', 'daling', 'nieskapo', 'sa', 'duol', 'nga', 'katubhan.', 'Daling', 'gidala', 'sa', 'Bayawan', 'District', 'Hospital', 'si', 'Badoy', 'apan', 'nakabsan', 'gyud', 'kini', 'sa', 'kinabuhi.', 'Sa', 'pagkakaron', ',', 'wala', 'pa', 'matino', 'sa', 'kapulisan', 'kung', 'unsay', 'motibo', 'sa', 'maong', 'pagpamusil.', 'Nagpadayon', 'na', 'sab', 'ang', 'imbestigasyon', 'sa', 'naasoy', 'nga', '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, 5, 6, 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, 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, 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, 5, 6, 6, 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] | cebuaner |
4,752 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['7', 'KA', 'BALAY', 'NAIGO', 'SA', 'SUNOG', 'SA', 'AMLAN', 'Dili', 'momenos', 'sa', '7', 'ka', 'balay', 'ang', 'naigo', 'sa', 'sunog', 'nga', 'niulbo', 'sa', 'Barangay', 'Tandayag', 'sa', 'lungsod', 'sa', 'Amlan', 'karong', 'hapon', ',', 'July', '19', ',', '2022.', 'Sumala', 'pa', 'imbestigasyon', 'sa', 'Bureau', 'of', 'Fire', 'Protection', '(', 'BFP', ')', ',', 'gipanag-iya', 'ang', 'mga', 'nasunog', 'nga', 'balay', 'nila', 'ni', 'Maria', 'Luisa', 'Estoconing', ',', 'Adela', 'Sienes', 'Singco', ',', 'Alfredo', 'Rebutazo', ',', 'Alissa', 'Salim', ',', 'April', 'Rose', 'Bungcasan', ',', 'Esther', 'Retes', 'ug', 'Carlo', 'Angelo', 'Buscato.', 'Dugang', 'pa', 'sa', 'BFP', ',', 'giingong', 'nagsugod', 'ang', 'sunog', 'sa', 'balay', 'ni', 'Maria', 'Luisa', 'Estoconing.', 'Diha-diha', 'nasunog', 'na', 'sab', 'ang', 'mga', 'panimalay', 'sa', 'iyang', 'mga', 'silingan.', 'Gibanabanang', 'moabot', 'sa', 'P450,000', 'ang', 'danyos', 'sa', 'naasoy', 'nga', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 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, 1, 2, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 2, 0, 0, 0, 3, 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] | cebuaner |
4,753 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['RAFFY', 'TULFO', 'NAGTANYAG', 'OG', 'P1', 'MILLION', 'REWARD', 'SA', 'PAGKASIKOP', 'SA', 'BOAC', 'CAMPING', 'KILLER-RAPIST', 'Usa', 'ka', 'senador', 'ang', 'nitanyag', 'og', 'P1', 'milyones', 'nga', 'reward', 'sa', 'pagkasikop', 'sa', 'suspek', 'sa', 'pagpatay', 'sa', '21-anyos', 'ug', 'paglugos', 'sa', 'iyang', '17-anyos', 'nga', 'uyab', 'atol', 'sa', 'camping', 'date', 'sa', 'Marinduque', 'niadtong', 'miaging', 'semana.', 'Gikondena', 'sab', 'ni', 'Senador', 'Raffy', 'Tulfo', 'ang', 'maong', 'krimen.', 'Patay', 'ang', 'usa', 'ka', '21-anyos', 'nga', 'lalaki', 'samtang', 'samdan', 'ug', 'gilugos', 'ang', 'iyang', '17-anyos', 'nga', 'uyab', 'human', 'sila', 'giatake', 'sa', 'nag-inusarang', 'suspek', 'samtang', 'sila', 'nagkampo', 'sa', 'Boac', ',', 'Marinduque.', 'Nahitabo', 'ang', 'pag-atake', 'pasado', 'alas-2', 'sa', 'kadlawon', 'niadtong', 'Biyernes', 'sa', 'Barangay', 'Ihatub.', 'Niitug-an', 'ang', 'babaye', 'sa', 'kapulisan', 'nga', 'ang', 'suspek', 'nisulod', 'sa', 'ilang', 'tent', 'ug', 'gidunggab', 'ang', 'iyang', 'uyab.', 'Nisulay', 'pa', 'siya', 'og', 'ikyas', 'apan', 'naabtan', 'siya', 'sa', 'suspek', 'ug', 'gilugos.', 'Nakaangkon', 'sab', 'siya', 'og', 'samad', 'dinunggaban.', 'Gidala', 'sab', 'sa', 'suspek', 'ang', 'kwarta', 'ug', 'cellphone', 'sa', 'mag-uyab', '.'] 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, 2, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 5, 6, 6, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,754 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'sa', 'Food', 'and', 'Drug', 'Administration', '(', 'FDA', ')', 'bag-ohay', 'lang', 'nga', 'dunay', 'nakitang', 'ethylene', 'oxide', 'sa', 'Lucky', 'Me', '!', 'Pancit', 'Canton', 'Kalamansi', 'flavor.', 'Sa', 'pamahayag', 'sa', 'FDA', 'niadtong', 'Biyernes', ',', 'giingon', 'sa', 'ahensya', 'nga', 'duna', 'kini', 'nakit-ang', '0.2mg', '/', 'kg', 'nga', 'ethylene', 'oxide', 'sa', 'maong', 'produkto.', 'Ang', 'ethylene', 'oxide', 'usa', 'ka', 'kemikal', 'nga', 'gigamit', 'isip', 'pesticide.', 'Hinuon', ',', 'giklaro', 'sa', 'FDA', 'nga', 'ang', 'ubang', 'produkto', 'sa', 'Lucky', 'Me', 'luwas', 'ra', 'kan-on', ',', 'sama', 'sa', 'Regular', 'Pancit', 'Canton', ',', 'Extra', 'Hot', 'Chili', 'Pancit', 'Canton', ',', 'Pancit', 'Canton', 'Chilimansi', 'ug', 'Instant', 'Mami', 'Beef', 'Regular.', 'Dugang', 'pa', 'sa', 'ahensya', ',', 'ipadayon', 'gihapon', 'niini', 'ang', 'pagtuon', 'kung', 'unsaon', 'pagdumala', 'sa', 'risgo', 'nga', 'dala', 'sa', 'ethylene', 'oxide', '.'] 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, 7, 8, 8, 8, 8, 8, 8, 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, 3, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 8, 8, 8, 8, 0, 7, 8, 8, 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 |
4,755 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KADAGHANAN', 'SA', 'MGA', 'PILIPINO', ',', 'NAGSALIG', 'SA', 'RESULTA', 'SA', 'PINILIAY', 'NIADTONG', 'MAYO', 'Gibutyag', 'sa', 'Pulse', 'Asia', 'nga', 'nagsalig', 'ang', 'kadaghanan', 'sa', 'mga', 'Pilipino', 'sa', 'nahimong', 'resulta', 'sa', 'piniliay', 'niadtong', 'Mayo.', 'Sumala', 'pa', 'sa', 'latest', 'nga', 'survey', 'sa', 'Pulse', 'Asia', ',', '82', '%', 'sa', 'mga', 'Pilipino', 'ang', 'walay', 'duda', 'sa', 'resulta', 'sa', 'niaging', 'eleksyon.', 'Kadaghanan', 'sa', 'mga', 'nag-ingong', 'nagsalig', 'sila', 'sa', 'resulta', ',', 'anaa', 'sa', 'Mindanao', '(', '96', '%', ')', 'ug', 'Luzon', '(', '73', '%', ')', '.', 'Sa', 'laing', 'bahin', ',', 'adunay', '4', '%', 'nga', 'mga', 'hamtong', 'nga', 'Pilipino', 'nga', 'niingong', 'wala', 'silay', 'salig', 'sa', 'resulta', 'sa', 'piniliay', ',', 'samtang', '14', '%', 'sa', 'mga', 'nisalmot', 'sa', 'survey', 'ang', '"', 'undecided.', '"', 'Gibutyag', 'sab', 'sa', 'survey', 'nga', 'kadaghanan', 'sa', 'mga', 'botante', ',', 'nasayonan', 'ra', 'kuno', 'sa', 'paggamit', 'sa', 'vote', 'counting', 'machines', '(', 'VCMs', ')', '.', 'Gipahigayon', 'niadtong', 'Hunyo', '24-27', 'ang', 'naasoy', 'nga', 'survey', '.'] 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, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 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, 5, 0, 0, 0, 0, 0, 5, 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, 0, 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 |
4,756 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DIARRHEA', 'OUTBREAK', 'SA', 'DAVAO', 'CITY', ':', '43', 'GIDALA', 'SA', 'OSPITAL', 'HUMAN', 'GIKALIBANGA', 'Naalarma', 'ang', 'mga', 'opisyal', 'sa', 'kahimsog', 'sa', 'Davao', 'City', 'human', 'natala', 'didto', 'ang', 'kalit', 'nga', 'pagsaka', 'sa', 'mga', 'kaso', 'sa', 'kalibanga', 'kon', 'diarrhea.', 'Nagsugod', 'ang', 'diarrhea', 'outbreak', 'niadtong', 'Biyernes', ',', 'July', '15', ',', '2022', 'sa', 'distrito', 'sa', 'Toril.', 'Sa', 'latest', 'nga', 'datos', 'sa', 'Davao', 'City', 'Health', 'Office', ',', 'moabot', 'na', 'sa', '43', 'ka', 'tawo', 'ang', 'naigo', 'sa', 'kalibanga.', 'Lakip', 'sa', 'mga', 'gi-diarrhea', 'mao', 'ang', 'usa', 'ka', 'masuso', 'nga', '6', 'ka', 'bulan', 'pa', 'lamang', 'ang', 'pangidaron.', 'Duda', 'karon', 'sa', 'mga', 'awtoridad', ',', 'pagkahugaw', 'sa', 'tubig', 'o', 'kontaminadong', 'street', 'foods', 'ang', 'mga', 'posibleng', 'hinungdan', 'sa', 'diarrhea', 'outbreak.', 'Tungod', 'niini', ',', 'nanawagan', 'ang', 'mga', 'health', 'officials', 'sa', 'Davao', 'ngadto', 'sa', 'publiko', 'nga', 'likayan', 'usa', 'ang', 'pag-inom', 'og', 'tap', 'water', 'kon', 'tubig', 'gikan', 'sa', 'gripo', '.'] 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, 6, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 5, 0, 0, 0, 0, 0, 3, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,757 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'required', 'ang', 'pagsul-ob', 'og', 'uniform', 'alang', 'sa', 'mga', 'tinun-an', 'sa', 'mga', 'pampublikong', 'eskuwelahan', 'karong', 'School', 'Year', '2022-2023', ',', 'sumala', 'pa', 'ni', 'Vice', 'President', 'Sara', 'Duterte.', 'Matud', 'pa', 'ni', 'Duterte', ',', 'bisan', 'pa', 'kadtong', 'wala', 'pay', 'pandemya', 'sa', '#', 'COVID19', ',', 'dili', 'na', 'strikto', 'ang', 'pagpasul-ob', 'og', 'uniform.', 'Kini', 'aron', 'malikayan', 'ang', 'dugang', 'nga', 'gasto', 'alang', 'sa', 'mga', 'estudyante', 'ug', 'sa', 'ilang', 'mga', 'pamilya.', 'Una', 'nang', 'gimando', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'ang', 'pagpahigayon', 'og', 'full', 'face-to-face', 'classes', 'sa', 'tanang', 'mga', 'eskuwelahan', 'sa', 'Pilipinas', 'sugod', 'karong', 'Nobyembre', '2', ',', '2022', '.'] 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, 1, 2, 0, 0, 0, 5, 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,758 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PULSE', 'ASIA', ':', 'HALOS', 'KATUNGA', 'SA', 'MGA', 'PINOY', 'ANG', ''WALA', 'NALIPAY', ''', 'SA', 'K-12', 'SYSTEM', 'Dul-an', 'sa', 'katunga', 'sa', 'mga', 'Pilipino', 'ang', '"', 'dissatisfied', '"', 'o', 'wala', 'nalipay', 'sa', 'K-12', 'system', ',', 'mao', 'kini', 'ang', 'gipakita', 'nga', 'resulta', 'sa', 'komisyon', 'sa', 'Pulse', 'Asia', 'survey', 'niadtong', 'Lunes.', 'Base', 'sa', 'survey', 'nga', 'gihimo', 'niadtong', 'June', '24-27', ',', '25', 'percent', 'sa', '1,200', 'ka', 'respondents', 'ang', 'miingon', 'nga', '"', 'somewhat', 'dissatisfied', '"', 'sila', 'sa', 'K-12', 'program', 'samtang', '19', 'percent', 'ang', '"', 'truly', 'dissatisfied.', '"', 'Nagpasabot', 'kini', 'nga', 'aduna'y', 'kinatibuk-ang', '44', 'percent', 'ang', '"', 'dissatisfied', '"', 'sa', 'basic', 'education', 'system', ',', 'sumala', 'sa', 'survey', 'nga', 'gikomisyon', 'ni', 'Senate', 'Basic', 'Education', 'Committee', 'Chairman', 'Sherwin', 'Gatchalian.', 'Ang', 'bag-o', 'nga', 'numero', 'mas', 'taas', 'kesa', 'sa', 'resulta', 'sa', 'September', '2019', 'survey', ',', 'diin', '28', 'percent', 'lamang', 'sa', 'mga', 'respondents', 'ang', 'wala', 'nakontento', 'sa', 'K-12', 'system', ',', 'matud', 'ni', 'Gatchalian.', 'Gipakita', 'sab', 'sa', 'pinakabag-ong', 'survey', 'nga', '7', 'percent', 'lang', 'ang', '“truly', 'satisfied”', 'samtang', '32', 'percent', 'ang', '“somewhat', 'satisfied', ',', '”', 'nagpasabot', 'nga', '39', 'porsiyento', 'sa', 'mga', 'respondents', 'ang', 'gikonsiderar', 'nga', '“satisfied”', 'sa', 'programa.', 'Samtang', ',', '18', 'percent', 'ang', 'dili', 'makasulti', 'kung', 'sila', 'nakontento', 'o', 'wala', 'nakontento', 'sa', 'K-12', 'system.', 'Gisubli', 'ni', 'Gatchalian', 'nga', 'ang', 'resulta', 'sa', 'survey', 'nagpasabot', 'nga', '"', 'dalian', 'ang', 'pagpahigayon', 'og', 'pagrepaso', 'ug', 'paghimo', 'og', 'mga', 'reporma', '"', 'sa', 'K-12', 'program', '.'] 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. | [3, 4, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 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, 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, 3, 4, 4, 4, 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, 7, 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, 7, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0] | cebuaner |
4,759 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Maayong', 'balita', 'alang', 'sa', 'mga', 'motorista', ':', 'magpatuman', 'og', 'dinagko', 'nga', 'oil', 'price', 'rollback', 'ang', 'mga', 'gasolinahan', 'ugma', ',', 'July', '19', ',', '2022.', 'Karong', 'Martes', ',', 'mobarato', 'og', 'P5', 'kada', 'litro', 'ang', 'presyo', 'sa', 'gasolina', ',', 'P2', 'kada', 'litro', 'alang', 'sa', 'diesel', ',', 'og', 'P0.70', 'kada', 'litro', 'alang', 'sa', 'kerosene', '.'] 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] | cebuaner |
4,760 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PHLPOST', ',', 'NIAWHAG', 'SA', 'PUBLIKO', 'NGA', 'PASENSYAHAN', 'ANG', 'DELAY', 'SA', 'NATIONAL', 'ID', 'Nihangyo', 'sa', 'publiko', 'ang', 'kadagkuan', 'sa', 'Philippine', 'Postal', 'Corporation', '(', 'PHLPOST', ')', 'nga', 'pasensyahan', 'ug', 'sabton', 'na', 'lang', 'ang', 'pagkalangan', 'sa', 'pag-deliver', 'sa', 'mga', 'national', 'ID.', 'Gipasabot', 'ni', 'PHLPOST', 'CEO', 'Norman', 'Fulgencio', 'nga', 'COVID-19', 'ang', 'usa', 'sa', 'mga', 'rason', 'sa', 'delay', 'sa', 'mga', 'national', 'ID.', 'Matud', 'pa', 'niya', ',', 'anaa', 'na', 'sa', '94', '%', 'kon', '14.8', 'milyon', 'ka', 'national', 'ID', 'ang', 'ila', 'nang', 'napadala.', 'Dugang', 'pa', 'niya', ',', 'dili', 'kuno', 'angay', 'nga', 'basulon', 'ang', 'PHLPOST', 'sa', 'kalangan', 'sa', 'mga', 'national', 'ID', 'tungod', 'kay', 'nagpadayon', 'gihapon', 'ang', 'pandemya', 'sa', 'COVID-19', '.'] 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. | [3, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
4,761 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'OBESE', 'NGA', 'PINOY', ',', 'MOABOT', 'SA', '37', 'MILLION', 'Gibutyag', 'sa', 'National', 'Nutrition', 'Council', '(', 'NNC', ')', 'niadtong', 'Biyernes', ',', 'July', '15', ',', '2022', ',', 'nga', 'moabot', 'sa', '37', 'milyon', 'ka', 'Pilipino', 'ang', 'obese', 'ug', 'overweight.', 'Sumala', 'pa', 'sa', 'NNC', ',', 'makuha', 'ang', 'obesity', 'pinaagi', 'sa', 'kanunay', 'nga', 'pagkaon', 'sa', 'mga', 'asgad', 'ug', 'mantikaon', 'nga', 'pagkaon', ',', 'ingon', 'man', 'ang', 'kakulangon', 'sa', 'tulog', 'ug', 'exercise.', 'Tungod', 'niini', ',', 'giduso', 'sa', 'NNC', 'ang', 'usa', 'ka', '“nutrient', 'profile', 'model”', 'nga', 'maoy', 'mahimong', 'basehan', 'sa', 'mga', 'kunsumidor', 'kung', 'unsay', 'dili', 'angay', 'ipakaon', 'ug', 'itanyag', 'sa', 'mga', 'bata.', 'Giawhag', 'sa', 'ahensya', 'ang', 'mga', 'ginikanan', 'nga', 'pakan-on', 'kanunay', 'og', 'utan', 'ug', 'prutas', 'ang', 'ilang', 'mga', 'anak.', 'Nipasalig', 'sab', 'ang', 'NNC', 'nga', 'padayon', 'kining', 'nagbatok', 'sa', 'malnutrition', ',', 'ilabi', 'na', 'nga', 'dugay', 'na', 'kining', 'problema', 'sa', 'atong', 'nasud', '.'] 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,762 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dokumentaryo', 'nga', 'nagsaysay', 'sa', 'kaanindot', 'sa', 'Marawi', 'City', 'sa', 'wala', 'pa', 'ang', 'Marawi', 'siege', ',', 'nasulod', 'sa', 'Top', '15', 'sa', '#', 'MSFF2022'] 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 7] | cebuaner |
4,763 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Filmmaker', 'nga', 'nakadawat', 'na', 'og', 'daghang', 'mga', 'awards', ',', 'magpakita', 'na', 'usab', 'sa', 'usa', 'sa', 'iyang', 'mga', 'dokumentaryo', 'sa', 'MSFF', '2022', '#', 'MSFF2022'] 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, 7, 0, 0, 7] | cebuaner |
4,764 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Opisyal', 'nang', 'gideklarar', 'sa', 'Food', 'and', 'Drug', 'Administration', '(', 'FDA', ')', 'nga', 'luwas', 'gyud', 'kan-on', 'ang', 'mga', 'produkto', 'sa', 'Lucky', 'Me', '!', 'dinhi', 'sa', 'Pilipinas.', 'Sa', 'usa', 'ka', 'pamahayag', 'karong', 'adlawa', '(', 'July', '15', ',', '2022', ')', ',', 'gitataw', 'sa', 'FDA', 'nga', 'gihimo', 'sa', 'Thailand', 'ug', 'dili', 'sa', 'Pilipinas', 'ang', 'mga', 'produkto', 'sa', 'Lucky', 'Me', 'nga', 'dunay', 'ethylene', 'oxide.', 'Sigon', 'sa', 'usa', 'ka', 'laboratory', 'test', 'nga', 'gipahigayon', 'sa', 'Vietnam', ',', 'gikompirmar', 'sa', 'FDA', 'nga', 'luwas', 'gyud', 'kan-on', 'ang', 'mga', 'mosunod', 'nga', 'produkto', ':', 'Lucky', 'Me', '!', 'Pancit', 'Canton', 'Extra', 'Hot', 'Chili', ',', 'Pancit', 'Canton', 'Regular', ',', 'Pancit', 'Canton', 'Chilimansi', 'ug', 'Instant', 'Mami', 'Beef', 'Regular', 'nga', 'gihimo', 'dinhi', 'sa', 'Pilipinas.', 'Apan', 'gibutyag', 'sa', 'FDA', 'nga', 'dunay', 'nakita', 'nga', '0.02', 'mg', '/', 'kg', 'nga', 'ethylene', 'oxide', 'sa', 'Pancit', 'Canton', 'Kalamansi', 'flavor.', 'Imbestigaran', 'pa', 'kini', 'sa', 'ahensya.', 'Ang', 'ethylene', 'oxide', 'usa', 'ka', 'kemikal', 'nga', 'gigamit', 'isip', 'pesticide', 'sa', 'pipila', 'ka', 'herbs', 'sama', 'sa', 'sa', 'sesame', 'seeds.', 'Bisan', 'pa', 'og', 'walay', 'makadaot', 'nga', 'epekto', 'sa', 'lawas', 'ang', 'pagkonsumo', 'sa', 'mga', 'pagkaong', 'dunay', 'ethylene', 'oxide', ',', 'posible', 'kunong', 'motumaw', 'ang', 'health', 'issues', 'kung', 'kanunayon', 'ang', 'pagkaon', '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, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 7, 8, 8, 0, 7, 8, 8, 0, 7, 8, 8, 8, 0, 0, 0, 0, 5, 0, 0, 0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,765 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PDEA', ':', '359', 'KA', 'BRGY', 'SA', 'NEGOR', ',', 'APEKTADO', 'GIHAPON', 'SA', 'DROGA', 'Gibutyag', 'sa', 'Philippine', 'Drug', 'Enforcement', 'Agency', '(', 'PDEA', ')', 'nga', '359', 'sa', '557', 'ka', 'barangay', 'sa', 'probinsiya', 'ang', 'apektado', 'sa', 'ilegal', 'nga', 'drugas', ',', 'atol', 'kini', 'sa', 'Provincial', 'Peace', 'and', 'Order', 'Council', 'special', 'meeting', 'niadtong', 'July', '7', ',', '2022.', 'Hinuon', ',', 'gisubli', 'ni', 'PDEA', 'Agent', 'Francisfil', 'Tangeres', 'nga', '145', 'ka', 'barangay', 'ang', 'gideklarar', 'nga', 'cleared', ',', 'samtang', '53', 'ang', 'gawasnon', 'na', 'sa', 'ilegal', 'nga', 'drugas', 'sa', 'probinsiya.', 'Gipresentar', 'sab', 'ni', 'Tangeres', 'ang', 'mga', 'update', 'sa', 'Barangay', 'Drug', 'Clearing', 'Program', '(', 'BDCP', ')', 'matag', 'distrito', 'nga', 'aduna'y', 'epekto', 'sa', 'droga.', 'Ang', '1st', 'District', 'aduna'y', 'kinatibuk-ang', '218', 'ka', 'mga', 'barangay', 'kung', 'diin', '72.47', '%', 'niini', 'ang', 'apektado', 'sa', 'droga', ',', '27', 'ang', 'cleared', ',', 'ug', '33', 'ka', 'mga', 'drug-free', 'barangay.', 'Ang', '2nd', 'District', 'aduna'y', '174', 'ka', 'barangay', 'diin', '64.36', '%', 'niini', 'ang', 'drug-affected', ',', '49', 'ang', 'cleared', ',', 'ug', '13', 'ang', 'gideklarar', 'nga', 'drug-free', 'barangays.', 'Sa', '3rd', 'District', ',', 'sa', '165', 'ka', 'barangay', ',', '53.39', '%', 'ang', 'drug-affected', ',', 'samtang', '69', 'ang', 'gideklarar', 'nga', 'cleared', ',', 'ug', 'pito', 'ang', 'drug-free', '.'] 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. | [3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 1, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,766 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['VP', 'SARA', ':', 'MAGSUGOD', 'ANG', 'KLASE', 'KARONG', 'AUGUST', '22', 'Gisubli', 'ni', 'Vice', 'President', 'ug', 'Education', 'Secretary', 'Sara', 'Duterte', 'nga', 'magsugod', 'ang', 'klase', 'sa', 'August', '22', 'bisan', 'pa', 'sa', 'panawagan', 'sa', 'pipila', 'ka', 'grupo', 'nga', 'ibalhin', 'ang', 'pag-abri', 'sa', 'klase', 'ngadto', 'sa', 'September.', 'Bag-ohay', 'lamang', ',', 'nipadayag', 'sa', 'ilang', 'pagsupak', 'ang', 'Teacher', ''s', 'Dignity', 'Coalition', '(', 'TDC', ')', 'bahin', 'sa', 'pag-abri', 'sa', 'klase', 'sunod', 'bulan', 'ug', 'nag-ingon', 'nga', 'nagkinahanglan', 'pa', 'ang', 'mga', 'magtutudlo', 'og', 'dugang', 'nga', 'oras', 'sa', 'pagpahuway', 'ug', 'pagpang-andam', 'sa', 'mosunod', 'nga', 'school', 'year.', 'Bisan', 'unsa', 'pa', 'ang', 'status', 'sa', 'alert', 'level', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'lugar', ',', 'kinahanglan', 'nga', 'mopatuman', 'ang', 'tanan', 'nga', 'mga', 'pampubliko', 'ug', 'pribadong', 'tunghaan', 'og', '5', 'ka', 'adlawa', 'nga', 'in-person', 'classes', 'sa', 'usa', 'ka', 'semana', 'sugod', 'November', '2', ',', 'ilalom', 'kini', 'sa', 'DepEd', 'Order', 'No.', '34', ',', 'series', 'sa', '2022.', 'Sa', 'panahon', 'sa', '"', 'transition', 'period', '"', 'hangtod', 'October', '31', ',', 'mahimo', 'silang', 'magsugod', 'sa', 'full', 'in-person', 'classes', 'nga', 'ingon', 'ka', 'sayo', 'sa', 'August', '22', 'apan', 'mahimo', 'sab', 'sila', 'nga', 'mopili', 'gikan', 'sa', 'bisan', 'asa', 'sa', 'mosunod', '—', '5', 'ka', 'adlaw', 'nga', 'in-person', 'classes', ';', 'blended', 'learning', 'modality', 'o', '3', 'ka', 'adlaw', 'nga', 'in-person', 'classes', 'ug', '2', 'ka', 'adlaw', 'nga', 'distance', 'learning', ';', 'o', 'full', 'distance', 'nga', 'pagkat-on', '.'] 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 8, 8, 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, 0, 0, 0, 0, 0, 0, 0, 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 |
4,767 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DOH', 'GITUN-AN', 'ANG', 'DENGUE', 'VACCINES', 'SAMTANG', 'NAGKATAAS', 'ANG', 'MGA', 'KASO', 'NIINI', 'SA', 'NASUD', 'Gitinguha', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'nga', 'tun-an', 'ang', 'mga', 'bakuna', 'sa', 'dengue', 'samtang', 'nagkataas', 'ang', 'mga', 'kaso', 'niini', 'sa', 'tibuok', 'nasud', ',', 'matod', 'pa', 'sa', 'officer-in-charge', 'karong', 'adlawa', ',', 'July', '15', ',', '2022.', 'Sumala', 'pa', 'ni', 'DOH', 'OIC', 'Maria', 'Rosario', 'Vergeire', ',', 'labing', 'menos', '23', 'ka', 'mga', 'bakuna', 'sa', 'dengue', 'ang', 'naa', 'sa', 'listahan', 'sa', 'emergency', 'medicine', 'sa', 'World', 'Health', 'Organization', '(', 'WHO', ')', '.', 'Base', 'sa', 'pinakabag-ong', 'datos', 'sa', 'DOH', ',', 'anaa', 'na', 'sa', '64,797', 'ang', 'natala', 'nga', 'mga', 'kaso', 'sa', 'dengue', 'sugod', 'January', '1', 'hangtod', 'July', '25.', 'Ang', 'numero', 'mas', 'taas', 'og', '90', 'percent', 'kon', 'itandi', 'sa', 'na-report', 'nga', '34,074', 'ka', 'kaso', 'sa', 'dengue', 'sa', 'samang', 'panahon', 'sa', '2021.', 'Dugang', 'pa', 'sa', 'DOH', ',', 'ang', 'tanang', 'rehiyon', 'gawas', 'sa', 'Ilocos', 'region', 'ug', 'Caraga', 'milapas', 'na', 'sa', 'alert', '/', 'epidemic', 'threshold', 'sa', 'miaging', 'upat', 'ka', 'semana', '.'] 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. | [3, 0, 0, 0, 0, 0, 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, 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,768 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['15,000', 'KA', 'PULIS', ',', 'SUNDALO', 'I-DEPLOY', 'SA', 'KINAUNAHANG', 'SONA', 'NI', 'PBBM', 'Duna'y', '15,000', 'ka', 'pulis', ',', 'sundalo', ',', 'ug', 'uban', 'pang', 'security', 'personnel', 'nga', 'gitakdang', 'i-deploy', 'sa', 'kinaunahang', 'State', 'of', 'the', 'Nation', 'Address', '(', 'SONA', ')', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'karong', 'sunod', 'Lunes', ',', 'Hulyo', '25', ',', '2022.', 'Kini', 'sumala', 'pa', 'sa', 'pamahayag', 'gikan', 'sa', 'Philippine', 'National', 'Police', '(', 'PNP', ')', '.', 'Duna', 'na', 'sab', 'giplastar', 'nga', 'security', 'plan', 'ang', 'National', 'Capital', 'Region', 'Police', 'Office', '(', 'NCRPO', ')', 'ngadto', 'kang', 'Interior', 'Secretary', 'Benhur', 'Abalos', 'niadtong', 'niaging', 'semana.', 'Matud', 'pa', 'ni', 'PNP', 'director', 'for', 'operations', 'Police', 'Major', 'General', 'Valeriano', 'de', 'Leon', ',', 'giandaman', 'na', 'sa', 'mga', 'awtoridad', 'ang', 'mga', 'posibleng', 'mga', 'rally', 'ug', 'protesta', 'nga', 'ipahigayon', 'duol', 'sa', 'Batasang', 'Pambansa', ',', 'diin', 'ibutyag', 'ni', 'Marcos', 'ang', 'mga', 'plano', 'sa', 'gobyerno', 'ubos', 'sa', 'iyang', 'administration', '.'] 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 3, 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, 3, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,769 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nag-anunsyo', 'ang', 'kagamhanang', 'lungsod', 'sa', 'La', 'Libertad', 'nga', 'maghatag', 'kini', 'og', 'libreng', 'abono', 'sa', 'mga', 'mag-uuma', 'didto.', 'Dunay', 'gigahin', 'nga', '2,500', 'ka', 'sako', 'sa', 'abono', 'ang', 'kagamhanang', 'lokal', 'alang', 'sa', 'mga', 'sakop', 'sa', 'mga', 'farmers', ''', 'associations', 'sa', 'lungsod.', 'Gibanabanang', 'nagkantidad', 'kini', 'og', 'P5.1', 'million.', 'Niadtong', 'Marso', ',', 'dunay', '1,000', 'ka', 'sako', 'nga', 'giapodapod', 'ngadto', 'sa', 'mga', 'mag-uuma.', 'Ang', 'nabilin', 'nga', '1,500', 'ka', 'sako', ',', 'gisugdan', 'na', 'og', 'panghatag', 'sukad', 'niadtong', 'Hunyo', '29.', 'Aduna', 'pay', '500', 'ka', 'sako', 'sa', 'abuno', 'nga', 'gilaomang', 'iapodapod', 'sa', 'mga', 'barangay', 'didto', 'nga', 'wa', 'pa', 'nakadawat', 'niini.', 'Ang', 'mga', 'barangay', 'nga', 'nakadawat', 'na', 'mao', 'ang', 'Pitogo', ',', 'Elicia', ',', 'Busilak', ',', 'Tala-on', ',', 'Nasungan', ',', 'Aya', ',', 'Kansumandig', ',', 'Bagtic', ',', 'Eli', ',', 'Aniniao', ',', 'Guihob', ',', 'Mabulod', ',', 'Manluminsag', ',', 'Mandapaton', ',', 'Cangabo', ',', 'Manghulyawon', ',', 'Biga-a', ',', 'Talayong', ',', 'Solonggon', ',', 'Mapalasan', 'ug', 'Maragondong', '.'] 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, 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, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0] | cebuaner |
4,770 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MOST', 'WANTED', 'SA', 'SAN', 'JOSE', 'TUNGOD', 'SA', 'KASONG', 'RAPE', ',', 'NADAKPAN', 'Nasikop', 'na', 'sa', 'kapulisan', 'sa', 'San', 'Jose', 'ang', 'most', 'wanted', 'nga', 'personalidad', 'sa', 'ilang', 'lungsod', 'tungod', 'sa', 'mga', 'kasong', 'rape.', 'Giila', 'ang', 'suspek', 'nga', 'si', 'alyas', '"', 'Ryan', ',', '"', 'lumolupyo', 'sa', 'Barangay', 'Tampi', 'sa', 'naasoy', 'nga', 'lungsod.', 'Nadakpan', 'si', 'Ryan', 'atol', 'sa', 'usa', 'ka', 'operasyon', 'sa', 'kapulisan', 'sa', 'Barangay', 'Azagra', ',', 'Tanjay', 'City', 'niadtong', 'Martes', ',', 'Hulyo', '12.', 'Kini', 'pinasikad', 'sa', 'usa', 'ka', 'warrant', 'of', 'arrest', 'nga', 'nag', 'gikan', 'sa', 'korte', 'sa', 'Tanjay.', 'Nagtingkagol', 'na', 'karon', 'sa', 'San', 'Jose', 'Police', 'Station', 'si', 'alyas', '"', 'Ryan.', '"', 'Gitakdang', 'moatubang', 'siya', 'sa', 'mga', 'kasong', 'statutory', 'rape', 'ug', 'qualified', 'rape', '.'] 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, 6, 0, 0, 0, 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, 1, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,771 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PBBM', ':', 'NATIONAL', 'ID', ',', 'POSIBLENG', 'MAGAMIT', 'NA', 'SUGOD', 'SUNOD', 'TUIG', 'Target', 'karon', 'sa', 'administrasyon', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'posibleng', 'paggamit', 'sa', 'National', 'ID', 'sugod', 'sunod', 'tuig.', 'Lakip', 'kini', 'sa', 'mga', 'gihisgotan', 'ni', 'Marcos', 'ug', 'ni', 'NEDA', 'Director-General', 'Arsenio', 'Balisacan', 'karong', 'adlawa', ',', 'July', '14', ',', '2022.', 'Gibutyag', 'ni', 'Press', 'Secretary', 'Trixie', 'Cruz-Angeles', 'nga', 'usa', 'ang', 'National', 'ID', 'System', 'sa', 'mga', 'giplanong', 'lakang', 'sa', 'gobyerno', 'aron', 'makabawi', 'ang', 'ekonomiya', 'sa', 'nasud', 'nga', 'labing', 'naapektahan', 'sa', 'pandemya', 'sa', 'COVID-19.', 'Mahinumduman', 'nga', 'gipirmahan', 'ni', 'kanhing', 'Presidente', 'Rodrigo', 'Duterte', 'ang', 'balaod', 'nga', 'nagtukod', 'sa', 'National', 'ID', 'System—ang', 'Philippine', 'System', 'Identification', 'Act', '(', 'PhilSys', 'Act', ')', '.', 'Tumong', 'sa', 'maong', 'balaod', 'ang', 'paghimo', 'sa', 'usa', 'ka', 'ID', 'nga', 'magamit', 'sa', 'mga', 'Pilipino', 'alang', 'sa', 'tanang', 'mga', 'transaksyon', 'sa', 'mga', 'buhatan', 'ug', 'ahensya', 'sa', 'gobyerno', '.'] 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 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, 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, 7, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 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] | cebuaner |
4,772 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'PATAY', 'HUMAN', 'MATUMBAHAN', 'OG', 'LUBI', 'SA', 'BAYAWAN', 'CITY', 'Patay', 'ang', 'duha', 'ka', 'biktima', ',', 'lakip', 'ang', 'usa', 'ka', '2-anyos', 'nga', 'bata', ',', 'human', 'sila', 'matumbahan', 'og', 'lubi', 'sa', 'Sitio', 'Kampanes', ',', 'Barangay', 'San', 'Roque', ',', 'Bayawan', 'City', 'kagahapong', 'adlawa', ',', 'Hulyo', '13', ',', '2022.', 'Nakalas', 'sab', 'sa', 'maong', 'insidente', 'si', 'Junrey', 'Jemoya', ',', '22', 'anyos', ',', 'lumolupyo', 'sa', 'maong', 'dakbayan.', 'Matud', 'pa', 'sa', 'inahan', 'sa', 'namatay', 'nga', 'bata', ',', 'nagsakay', 'sila', 'sa', 'motor', 'nga', 'gimaneho', 'ni', 'Jemoya', 'sa', 'dihang', 'natumbahan', 'sila', 'sa', 'punuan', 'sa', 'lubi.', 'Direktang', 'naigo', 'sa', 'maong', 'lubi', 'si', 'Jemoya', 'ug', 'ang', 'bata.', 'Daling', 'gidala', 'ang', 'mga', 'biktima', 'sa', 'Bayawan', 'District', 'Hospital', 'apan', 'nakabsan', 'gyud', 'sila', 'sa', 'kinabuhi', '.'] 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, 5, 6, 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, 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, 1, 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] | cebuaner |
4,773 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naugdaw', 'ang', 'usa', 'ka', 'balay', 'sa', 'Purok', 'Orchids', ',', 'Daang', 'Taytayan', ',', 'Brgy', 'Calindagan', ',', 'Dumaguete', 'City', 'karong', 'hapona', ',', 'July', '14', ',', '2022.', 'Giila', 'ang', 'nasunogan', 'nga', 'si', 'Josephine', 'Salonoy.', 'Matud', 'ni', 'Salonoy', ',', 'iya', 'kunong', 'gisigurado', 'nga', 'gipalong', 'niya', 'ang', 'ilang', 'telebisyon', 'sa', 'wala', 'pa', 'siya', 'nilakaw.', 'Apan', 'nakuratan', 'na', 'lamang', 'kuno', 'siya', 'pag-uli', 'sa', 'iyang', 'balay', 'ug', 'naugdaw', 'na', 'lang', 'kini.', 'Sa', 'pagkakaron', 'gisayran', 'pa', 'sa', 'Bureau', 'of', 'Fire', 'Protection', '(', 'BFP', ')', 'ang', 'hinungdan', 'sa', 'naasoy', 'nga', '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, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,774 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sunog', 'sa', 'Purok', 'Orchids', ',', 'Daang', 'Taytayan', ',', 'Sitio', 'Canday-ong', ',', 'Barangay', 'Calindagan', ',', 'Dumaguete', 'City', 'Live', 'karon', 'ang', 'atong', 'Silliman', 'University', 'intern', 'nga', 'si', 'Gilmore', 'Leaño'] 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, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2] | cebuaner |
4,775 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGBABALAOD', ':', 'MGA', 'TEACHER', ',', 'WALA'Y', 'IGONG', 'PAHUWAY', 'SA', 'BAG-ONG', 'KALENDARYO', 'SA', 'DEPED', 'Nabalaka', 'si', 'Alliance', 'of', 'Concerned', 'Teachers', '(', 'ACT', ')', 'party-list', 'Rep.', 'France', 'Castro', 'nga', 'mahimong', 'wala'y', 'igong', 'oras', 'sa', 'pagpahuway', 'ang', 'mga', 'magtutudlo', 'tungod', 'sa', 'DepEd', 'calendar', 'alang', 'sa', 'school', 'year', '2022-23', 'uban', 'ang', 'enrollment', 'nga', 'magsugod', 'sa', 'July', '25', 'ug', 'academic', 'year', 'nga', 'magsugod', 'sa', 'August', '25.', 'Sumala', 'pa', 'ni', 'Castro', ',', 'daghan', 'gihapon', 'ang', 'mga', 'magtutudlo', 'nga', 'nag-atiman', 'pa', 'sa', 'paperworks', 'sa', 'niaging', 'school', 'year.', 'Gihimug-atan', 'sab', 'niya', 'nga', 'ang', 'Department', 'Order', 'No.', '34', ',', 'wala', 'maghisgot', 'og', 'kompensasyon', 'sa', 'mga', 'magtutudlo', 'nga', 'moadto', 'sa', 'eskwelahan', 'aron', 'makapangandam', 'sa', 'sunod', 'nga', 'school', 'year', 'bisan', 'bakasyon', 'unta', 'ni', 'nila', 'nga', 'panahona.', 'Gisubli', 'ni', 'Castro', 'nga', 'nabalaka', 'siya', 'nga', 'dili', 'mahatagan', 'og', 'insakto', 'nga', 'kompensasyon', 'ang', 'mga', 'magtutudlo', 'nga', 'mo-overtime', 'sa', 'trabaho', 'tungod', 'sa', 'mubo', 'nga', 'panahon', 'tali', 'sa', 'katapusan', 'nga', 'school', 'year', 'ug', 'pagsugod', 'sa', 'sunod', 'nga', 'tuig', '.'] 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, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 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, 1, 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, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,776 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'sa', '85', 'ka', 'bags', 'sa', 'dugo', 'ang', 'gidonar', 'sa', 'mga', 'sakop', 'sa', 'Negros', 'Oriental', 'Provincial', 'Police', 'Office', '(', 'NORPPO', ')', 'atol', 'sa', 'blood', 'letting', 'program', 'nga', 'gipahigayon', 'niini', 'kagahapong', 'adlawa', ',', 'July', '12', ',', '2022.', 'Sumala', 'pa', 'sa', 'NORPPO', ',', 'niabot', 'og', '143', 'ka', 'blood', 'donors', 'ang', 'naghatag', 'sa', 'ilang', 'dugo', 'sa', 'maong', 'kalihokan.', 'Kini', 'gilangkuban', 'sa', 'mga', 'sakop', 'sa', 'kapulisan', ',', 'ingon', 'man', 'sa', 'mga', 'miyembro', 'sa', 'Triskellion', '(', 'Tau', 'Gamma', ')', ',', 'Skpetron', '(', 'Akhro', ')', ',', 'ug', 'mga', 'sibilyan', 'gikan', 'sa', 'PNP', 'Advocacy', 'Support', 'Groups.', 'Gipahigayon', 'ang', 'maong', 'kalihokan', 'isip', 'pagsaulog', 'sa', 'tinuig', 'nga', 'Police', 'Community', 'Relations', '(', 'PCR', ')', 'Month', ',', 'diin', 'naghatag', 'og', 'lahi-lahing', 'serbisyo', 'ang', 'kapulisan', 'ngadto', 'sa', 'publiko', '.'] 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, 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, 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, 3, 4, 4, 4, 4, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,777 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PRES.', 'MARCOS', ',', 'WALA', 'NA'Y', 'SINTOMAS', 'SA', 'COVID-19', 'Wala', 'nay', 'gipakita', 'nga', 'sintomas', 'sa', 'COVID-19', 'si', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ug', 'posibleng', 'makagawas', 'na', 'kini', 'gikan', 'sa', 'iyang', 'isolation', 'karong', 'Biyernes.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'iyang', 'physician', 'nga', 'si', 'Dr.', 'Samuel', 'Zacate', 'karong', 'adlawa', ',', 'July', '13', ',', '2022.', 'Base', 'sa', 'report', 'ni', 'Zacate', ',', 'duha', 'na', 'kuno', 'ka', 'adlaw', 'nga', 'wala'y', 'sintomas', 'sa', 'COVID-19', 'si', 'Marcos.', 'Kini', 'human', 'nga', 'gisusi', 'ni', 'Zacate', 'ang', 'kahimtang', 'Marcos', 'ganinang', 'buntag.', 'Gani', ',', 'gibutyag', 'sab', 'sa', 'doktor', 'nga', 'puwede', 'nang', 'mobalik', 'si', 'Marcos', 'sa', 'iyang', 'mga', 'face-to-face', 'nga', 'lakaw.', 'Samtang', 'gi-isolate', 'pa', 'si', 'Marcos', ',', 'nagpadayon', 'kuno', 'kining', 'nagtrabaho', ',', 'ug', 'nakigpulong', 'pa', 'sa', 'iyang', 'gabinete', 'kagahapong', 'adlawa', '.'] 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, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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] | cebuaner |
4,778 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CHED', 'DILI', 'MAGMANDO', 'OG', '100', '%', 'F2F', 'CLASSES', 'SA', 'MGA', 'UNIBERSIDAD', ',', 'KOLEHIYO', 'Dili', 'magmando', 'ang', 'Commission', 'on', 'Higher', 'Education', '(', 'CHED', ')', 'sa', 'mga', 'unibersidad', 'ug', 'kolehiyo', 'nga', 'mobalhin', 'sa', 'pagpahigayon', 'og', 'face-to-face', 'nga', 'klase', 'sa', 'umalabot', 'nga', 'academic', 'year.', 'Sumala', 'pa', 'ni', 'Commissioner', 'Prospero', 'de', 'Vera', ',', 'gitugyan', 'sa', 'CHED', 'ngadto', 'sa', 'mga', 'tagdumala', 'sa', 'tunghaan', 'ang', 'paghimo', 'ug', 'learning', 'set-up', 'nga', 'labing', 'maayo', 'sa', 'ilang', 'mga', 'degree', 'program.', 'Apan', 'nagtuo', 'siya', 'nga', 'aduna'y', ''very', 'significantly', 'shift', ''', 'o', 'dako', 'nga', 'kaayo', 'sa', 'pagbalhin', 'sa', 'f2f', 'classes', 'sa', 'mga', 'unibersidad', 'ug', 'kolehiyo', 'tungod', 'kadaghanan', 'sa', 'mga', 'eskwelahan', 'ug', 'mga', 'estudyante', 'aduna', 'na'y', 'maayong', 'mga', 'pasilidad', 'ug', 'kahimanan', 'sa', 'pagpahigayon', 'sa', 'flexible', 'learning.', 'Niadtong', 'Martes', ',', 'gi-require', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'ang', 'mga', 'eskwelahan', 'nga', 'hingpit', 'nga', 'ipatuman', 'ang', '5', 'ka', 'adlaw', 'nga', 'in-person', 'classes', 'sa', 'November', '2', 'bisan', 'pa', 'sa', 'lebel', 'sa', 'alerto', 'sa', 'COVID-19', '.'] 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. | [3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 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, 0, 0, 0, 0, 0, 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, 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] | cebuaner |
4,779 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PIMENTEL', ',', 'NIHANGYO', 'SA', 'BSP', 'NGA', 'SUSPENSUHON', 'ANG', ''IMPRACTICAL', ''', 'NGA', 'P1,000', 'POLYMER', 'BILLS', 'Nihangyo', 'si', 'Senador', 'Aquilino', '“Koko”', 'Pimentel', 'III', 'ngadto', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', '(', 'BSP', ')', 'nga', 'suspensuhon', 'dayon', 'ang', 'produksyon', 'sa', '"', 'impractical', '"', 'nga', 'P1,000', 'nga', 'polymer', 'bills.', 'Sumala', 'pa', 'ni', 'Pimentel', ',', 'ang', 'desisyon', 'sa', 'BSP', 'nga', 'gamiton', 'ang', 'polymer', 'imbes', 'ang', 'lumad', 'nga', 'abaca', 'sa', 'bag-ong', 'P1,000', 'bills', 'niini', 'dili', 'lang', 'makadaot', 'sa', 'panginabuhi', 'sa', 'mga', 'lokal', 'nga', 'tiggama', 'sa', 'abaca', 'apan', 'dili', 'sab', 'praktikal', 'sa', 'daghang', 'mga', 'Pilipino', 'nga', 'naanad', 'na', 'sa', 'pagtago', 'sa', 'ilang', 'kwarta', 'sa', 'ilang', 'mga', 'bulsa', ',', 'pitaka', ',', 'o', 'ilang', 'tri-fold', 'nga', 'pitaka.', 'Gisubli', 'niya', 'nga', 'bisan', 'paman', 'ang', 'polymer', 'mas', 'lig-on', 'kay', 'sa', 'abaca', ',', 'wala', 'kini', 'gikinahanglan', 'nga', 'flexibility', 'aron', 'tugotan', 'ang', 'mga', 'tawo', 'sa', 'pagtipig', 'niini', 'sa', 'ilang', 'mga', 'bulsa', ',', 'pitaka', ',', 'money', 'clip', 'o', 'bisan', 'gamay', 'nga', 'pitaka.', 'Dugang', 'pa', 'niya', ',', 'sensitibo', 'sab', 'kaayo', 'ang', 'mga', 'polymer', 'sa', 'mga', 'kemikal.', 'Nabalaka', 'sab', 'si', 'Pimentel', 'sa', 'posibleng', 'negatibong', 'epekto', 'sa', 'pag-ilis', 'sa', 'abaca', 'isip', 'materyal', 'sa', 'paghimo', 'og', 'P1,000', 'nga', 'bills', ',', 'ilabi', 'na', 'sa', 'kita', 'sa', 'nasod', 'gikan', 'sa', 'pag-export', 'sa', 'abaca', 'fiber', 'ug', 'mga', 'manupaktura', ',', 'nga', 'mokabat', 'sa', 'US', '$', '97.1', 'milyones', 'kada', 'tuig', '.'] 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 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, 1, 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, 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, 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, 0, 0, 0, 0, 0, 0, 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 |
4,780 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', ',', 'NAKATALA', 'OG', '4', 'KA', 'BAG-ONG', 'KASO', 'SA', 'COVID-19', 'Gikumpirma', 'sa', 'City', 'Health', 'Office', 'ang', 'pagkaayo', 'sa', 'usa', 'ka', '61-anyos', 'nga', 'negosyante', 'gikan', 'sa', 'COVID-19', ',', 'apan', '4', 'ka', 'bag-ong', 'kaso', 'sab', 'ang', 'natala', 'niadtong', 'July', '12', ',', '2022.', 'Ang', '4', 'ka', 'bag-ong', 'kaso', 'sa', 'COVID-19', 'naglakip', 'sa', 'usa', 'ka', '6-anyos', 'nga', 'bata', 'kinsa', 'wala', 'pa', 'nabakunahan', ',', 'usa', 'ka', '56-anyos', 'nga', 'engineer', 'ug', 'usa', 'ka', 'magtiayon', 'nga', 'hingpit', 'na', 'nga', 'nabakunahan.', 'Sa', 'pagkakaron', ',', 'aduna'y', '8', 'ka', 'aktibong', 'kaso', 'sa', 'COVID-19', 'ang', 'dakbayan', 'sa', 'Dumaguete.', 'Anaa', 'na', 'sa', '5,674', 'ang', 'nangaayo', 'ug', '166', 'ang', 'namatay', 'tungod', 'sa', 'maong', 'virus', 'sukad', 'ang', 'pandemya', 'nakaapekto', 'sa', 'dakbayan', 'gikan', 'January', '30', ',', '2020', 'hangtod', 'July', '12', ',', '2022.', 'Ilalom', 'sa', 'guidelines', ',', 'mahimong', 'makagawas', 'sa', 'isolation', 'ug', 'ikonsiderar', 'nga', 'naayo', 'na', 'ang', 'usa', 'ka', 'indibidwal', 'kinsa', 'nagpositibo', 'sa', 'Covid-19', 'apan', 'hingpit', 'na', 'nga', 'nabakunahan', ',', '7', 'ka', 'adlaw', 'human', 'sa', 'unang', 'pagpakita', 'niini', 'sa', 'mga', 'sintomas', 'ug', 'dili', 'na', 'siya', 'symptomatic', '.'] 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. | [5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 3, 4, 4, 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, 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, 7, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,781 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Sen.', 'Sonny', 'Angara', 'maoy', 'tagsulat', 'sa', 'Integrated', 'Filipino-Muslim', 'and', 'Indigenous', 'Peoples', 'History', 'Act', ',', 'nga', 'nagpadayag', 'sa', 'kabatan-onan', 'sa', 'pagsabot', 'bisan', 'sa', 'lain-laing', 'relihiyon', ',', 'pinaagi', 'sa', 'pagtudlo', 'sa', 'eskwelahan', 'sa', 'kultura', 'ug', 'kasaysayan', 'sa', 'mga', 'Muslim', 'ug', 'mga', 'lumad', '.'] 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, 1, 2, 0, 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, 0, 0, 0, 0, 7, 0, 0, 0, 0] | cebuaner |
4,782 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Victorias', 'City', 'Environmental', 'and', 'Natural', 'Resources', 'Office', 'ug', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'Traffic', 'Enforcement', 'Management', 'nagtinabangay', 'alang', 'sa', 'pagpatuman', 'sa', 'CAR-FREE', 'ZONES', 'sa', 'mga', 'giila', 'nga', 'lugar', 'sa', 'palibot', 'sa', 'city', 'proper.', 'Ang', 'kalihokan', 'nagtumong', 'sa', 'pagdasig', 'sa', 'mga', 'indibidwal', 'sa', 'pagpakunhod', 'sa', 'adlaw-adlaw', 'nga', 'carbon', 'emissions', 'sa', 'siyudad', 'ug', 'ang', 'bug-os', 'nga', 'paggamit', 'sa', 'gihatag', 'nga', 'pedestrian', 'lane', 'ug', 'gitudlo', 'nga', 'mga', 'agianan', 'sa', 'dalan.', 'Kini', 'ang', 'unang', 'higayon', 'nga', 'ang', 'kalihokan', 'sa', 'Victorias', 'City', 'ipahigayon', 'isip', 'kabahin', 'sa', 'tinuig', 'nga', 'selebrasyon', 'sa', 'Environment', 'Week', '.'] 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, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
4,783 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'kataas', 'sa', 'COVID-19', 'pandemic', 'dihang', 'gipatuman', 'ang', 'RA', '11561', 'o', 'Increasing', 'the', 'Bed', 'Capacity', 'sa', 'East', 'Ave.', 'Medical', 'Center', 'Act', ',', 'nga', 'gimugna', 'ni', 'Sen.', 'Sonny', 'Angara', ',', 'isip', 'tubag', 'sa', 'problema', 'sa', 'pagdagsa', 'sa', 'mga', 'pasyente', 'sa', 'nahisgutang', 'tambalanan', 'sa', 'Barangay', 'Diliman', ',', 'Quezon', 'City.', 'Ang', 'balaod', 'nag-ingon', 'nga', 'ang', 'kapasidad', 'sa', 'higdaanan', 'madugangan', 'gikan', 'sa', '600', 'ngadto', 'sa', '1,000', ',', 'uban', 'ang', 'pagdugang', 'ug', 'pag-upgrade', 'sa', 'mga', 'serbisyo', 'sa', 'mga', 'doktor', ',', 'nars', ',', 'ug', 'uban', 'pang', 'mga', 'healthcare', 'workers', 'sa', 'East', 'Avenue', 'Medical', 'Center', '.'] 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, 7, 8, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 5, 6, 6, 6, 0] | cebuaner |
4,784 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'lapida', 'ay', 'minarkahan', 'hindi', 'lamang', 'ang', '"', 'katapusan', '"', 'ng', 'kanilang', 'buhay', 'kolehiyo', ',', 'kundi', 'pati', 'na', 'rin', 'ang', 'kanilang', '"', 'walang', 'kamatayan', '"', 'na', 'pagkakaibigan.', 'Ang', 'mag-besties', 'ay', 'kumuha', 'ng', 'Bachelor', 'of', 'Arts', 'in', 'Broadcasting', 'sa', 'Bulacan', 'State', 'University', 'at', 'naging', 'magkaibigan', 'mula', 'noong', '2019.', 'Si', 'Sarondo', 'ay', 'nagtapos', 'ng', 'Summa', 'Cum', 'Laude', 'habang', 'si', 'Bamba', 'ay', 'nagtapos', 'ng', 'Magna', 'Cum', 'Laude.', 'Saad', 'ni', 'Sarondo', 'na', 'gumastos', 'siya', 'ng', 'humigit-kumulang', 'P1,500', 'para', 'sa', 'kanyang', 'regalo', ',', 'at', 'halatang', 'nagulat', 'ang', 'gumagawa', 'ng', 'lapida', 'sa', 'kanyang', 'kahilingan.', 'anini', 'ni', 'Sarondo', '.'] 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, 7, 8, 8, 8, 8, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 8, 8, 0, 0, 1, 0, 0, 0, 7, 8, 8, 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, 1, 0] | cebuaner |
4,785 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'low', 'pressure', 'area', '(', 'LPA', ')', 'padayong', 'nagdala', 'og', 'ulan', 'ug', 'pagpanugdog-kilat', 'ilabi', 'na', 'sa', 'Visayas', ',', 'Mindanao', 'ug', 'Southern', 'Luzon', ',', 'matod', 'sa', 'Philippine', 'Atmospheric', ',', 'Geophysical', 'and', 'Astronomical', 'Services', '(', 'PAGASA', ')', '.'] 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, 5, 0, 5, 0, 5, 6, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0] | cebuaner |
4,786 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['WALAY', 'BULAK', 'BISAN', 'Trending', 'karon', 'ang', 'post', 'sa', 'Facebook', 'sa', 'netizen', 'nga', 'si', 'Crizza', 'May', 'Lazaga', 'sa', 'komedya', 'sa', 'iyang', 'mga', 'higala', 'sa', 'adlaw', 'sa', 'iyang', 'graduation', 'tungod', 'kay', 'imbes', 'bouquet', 'of', 'flowers', 'ang', 'iyang', 'nadawat', 'ang', 'mga', 'bulak', 'nga', 'sagad', 'makita', 'matag', 'Undas.', 'Nangatawa', 'ang', 'mga', 'higala', 'ni', 'Crizza', 'May', 'Lazaga', 'dihang', 'gihatagan', 'siya', 'og', 'mga', 'bulak', 'tungod', 'kay', 'wala', 'niya', 'gitumbok', 'ang', 'matang', 'sa', 'bulak', 'nga', 'iyang', 'gusto.', 'ni', 'Crizza', '.'] 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, 7, 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, 0, 0, 0, 0, 7, 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, 1, 0] | cebuaner |
4,787 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'nakumpiska', 'nga', 'ilegal', 'nga', 'drugas', 'nagkantidad', 'og', 'P18.2', 'milyones', 'sa', 'Task', 'Force', 'Davao', 'checkpoint', 'sa', 'Barangay', 'Sirawan', ',', 'Davao', 'City.', 'Nasikop', 'ang', 'drayber', 'ug', 'pasahero', 'sa', 'maong', 'sakyanan', 'ug', 'gitanggong', 'na', 'karon', 'sa', 'Toril', 'Police', 'Station', '.'] 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, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0] | cebuaner |
4,788 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JUST', 'IN', ':', 'Ang', 'Philippine', 'Institute', 'of', 'Volcanology', 'and', 'Seismology', '(', 'PHIVOLCS', ')', 'nakakita', 'sa', 'pagsaka', 'sa', 'seismic', 'activity', 'sa', 'Mayon', 'Volcano', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
4,789 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Huna-hunaa', 'ABC', 'ang', 'choices', ',', 'tapos', 'wa', 'ka', 'gipili', '.'] 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 |
4,790 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'social', 'media', 'personality', 'nga', 'si', 'Toni', 'Fowler', 'mipaambit', 'sa', 'hulagway', 'uban', 'sa', ''Unkabogable', 'Star', ''', 'nga', 'si', 'Vice', 'Ganda', 'sa', 'iyang', '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, 1, 2, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0] | cebuaner |
4,791 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'report', 'ni', 'Western', 'Visayas', 'Coast', 'Guard', 'District', 'spokesperson', 'Commander', 'Jansen', 'Benjamin', ',', 'duha', 'ka', 'insidente', 'ang', 'na-record', 'sa', 'probinsya', 'sang', 'Iloilo', ',', 'tag', 'isa', 'halin', 'sa', 'Aklan', ',', 'Negros', 'Occidental', ',', 'Capiz', ',', 'kag', 'Guimaras', '.'] 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 0, 0, 5, 0] | cebuaner |
4,792 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pinakatigulang', 'sa', 'tropa', ',', 'naa', 'nay', 'early', 'signs…'] 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 |
4,793 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NGANONG', 'GI-UNA', 'NIMO', 'ANG', 'CONCERT', '?', 'Mao', 'kini', 'ang', 'pamahayag', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'karong', 'Domingo', ',', 'Hunyo', '25', ',', 'sa', 'usa', 'ka', 'post', 'nga', 'nag-share', 'sa', 'tweet', 'sa', 'TV', 'Host-actress', 'nga', 'si', 'Maine', 'Mendoza', 'gikan', 'sa', 'concert', 'sa', 'Filipino-American', 'singer', 'nga', 'si', 'Bruno', '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, 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, 1, 2, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0] | cebuaner |
4,794 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'Lunes', ',', 'Hunyo', '26', ',', 'adunay', 'adjustment', 'sa', 'presyo', 'sa', 'diesel', 'ug', 'gasolina', ',', 'matod', 'sa', 'taho.', '⬆️', 'Gasoline', '+', '0.20', '/', 'L', '(', 'Increase', ')', '⬆️', 'Diesel', '+1.05', '/', 'L', '(', 'Increase', ')'] 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] | cebuaner |
4,795 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Department', 'of', 'Education-Region', '7', '(', 'DEPED-7', ')', 'Director', 'Salustiano', 'Jimenez', 'kaniadtong', 'Biyernes', ',', 'Hunyo', '23', ',', 'kini', 'magsilbi', 'nga', 'milestone', 'anniversary', 'bonus', 'nga', 'mahitabo', 'matag', 'lima', 'ka', 'tuig', 'sa', 'mga', 'kwalipikadong', 'empleyado.', 'matod', 'niya', ',', 'ug', 'midugang', 'nga', 'kini', 'usa', 'ka', 'paagi', 'sa', 'pag-ila', 'sa', 'ilang', 'mga', 'paningkamot.', 'Sa', 'kinatibuk-ang', 'ihap', 'sa', 'mga', 'benepisyaryo', 'sa', 'rehiyon', ',', '85', 'porsyento', 'o', '70,000', 'ang', 'mga', 'kawani', 'sa', 'pagtudlo', '.'] 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, 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, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,796 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Selos', 'na', 'selos', 'na', 'yern', '?', 'Tanawa', 'ko', 'sa', 'karapatan', 'bi', ',', 'naa', '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, 0, 0] | cebuaner |
4,797 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['gihisgutan', 'karon', 'ang', 'pagkanselar', 'sa', 'Pride', 'PH', 'sa', 'performance', 'sa', 'OPM', 'band', 'nga', 'Silent', 'Sanctuary', 'karong', 'Sabado', ',', 'Hunyo', '24', 'sa', 'Pride', 'Festival', 'nga', ''Love', 'Laban', 'sa', 'QC', ''', 'nga', 'gipahigayon', 'sa', 'Quezon', 'Memorial', 'Circle', 'sa', 'Quezon', 'City.', 'Nasayran', 'sa', 'Pride', 'PH', 'nga', 'gipugngan', 'kuno', 'sa', 'banda', 'nga', 'Silent', 'Sanctuary', 'ang', 'ilang', 'kanhi', 'bokalista', 'nga', 'si', 'Ian', 'Carandang', 'nga', 'mag-out', 'kon', 'gusto', 'siyang', 'magpabilin', 'sa', 'banda.', 'tweet', 'ni', 'Ian', 'sa', 'Hunyo', '24', '.'] 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, 0, 0, 0, 7, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 0, 5, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,798 | 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', 'gisulti', 'ni', 'Presidente', 'Bongbong', 'Marcos', 'atol', 'sa', 'paghandum', 'sa', ''Pride', 'Festival', ''', 'sa', 'miaging', 'Sabado', ',', 'Hunyo', '24.', 'Matod', 'niya', ',', 'dako', 'ang', 'kontribusyon', 'sa', 'LGBTQ', 'sa', 'atong', 'katilingban'] 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, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0] | cebuaner |
4,799 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PADAYON', 'SA', 'LABAN', 'DAY', '!', '"', 'From', '3years', 'past', 'to', 'present', 'Dili', 'tanang', 'mga', 'pangit', 'nga', 'panghitaboan', 'kay', 'kaalaotan', 'na', 'o', 'kamalasan', 'sa', 'atoa.', 'Usahay', 'kinahanglan', 'lang', 'guro', 'nga', 'naay', 'mahitabo', 'arun', 'kita', 'makaamgo', 'ug', 'mag', 'bag-o', 'sa', 'tanan', 'nating', 'dli', 'maayong', 'binuhatan.', 'Sama', 'nako', 'nga', 'usa', 'ka', 'drugs', ',', 'alcohol', 'and', 'smoke', 'addict', 'sa', 'una', 'gikan', 'pagka', 'dalaga', 'ug', 'pagka', 'June', '20', ',', '2020', 'na', 'dakpan', 'ug', 'na', 'detained', 'sa', 'Makilala', 'Police', 'Station.', 'Gikan', 'anang', 'adlawa', 'sa', 'akong', 'pagkadakop', ',', 'naka', 'pag', 'huna2x', 'ako', 'ug', 'naka', 'amgo', 'nga', 'grabe', 'najud', 'diay', 'akong', 'nabuhat', 'nga', 'kamalian', 'sa', 'kinabuhi', 'ug', 'labaw', 'sa', 'tanan', 'taas', 'na', 'diay', 'kaayo', 'kog', 'panahon', 'nga', 'gisayang', 'sa', 'pag', 'pakadaotan', 'pro', 'gikan', 'sab', 'anang', 'adlawa', 'kauban', 'sa', 'mga', 'taong', 'nitambag', 'ug', 'nitabang', 'nako', ',', 'naka', 'amgo', 'ko.', 'Samtang', 'na', 'detain', 'ko', 'dghan', 'kaayo', 'ko', 'ug', 'na', 'realize', ',', 'na', 'realize', 'nako', 'nga', 'dli', 'lng', 'diay', 'kangitngit', 'anaa', 'ang', 'kalibutan', 'kundi', 'naa', 'say', 'kahayag', 'Na', 'realize', 'nako', ',', 'dli', 'lng', 'diay', 'asa', 'kutob', 'atong', 'kinabuhi', 'kay', 'mentras', 'naa', 'pa', 'diay', 'kinabuhi', 'naa', 'pay', 'paglaom', 'ug', 'pag-asa.', 'Maong', 'ako', ',', 'atong', 'pagka', 'preso', 'nako', ',', 'wala', 'nako', 'to', 'gihuna-huna', 'nga', 'usa', 'to', 'ka', 'kamalasan', 'kundi', 'usa', 'ka', 'grasyo', 'gikan', 'sa', 'Ginoo', 'nga', 'gihatag', 'niya', 'nako', 'ug', 'karun', 'na', 'usab', 'ako', 'ug', 'matarong', 'na', 'dli', 'na', 'pareho', 'sauna', 'nga', 'latagaw', 'ug', 'walay', 'direksyo.', 'Karun', 'namuyo', 'ng', 'malipayon', 'ug', 'matarong', 'kauban', 'akong', 'pamilya.', 'Ug', 'puhon', 'ugma', 'mo', 'graduate', 'na', 'sa', 'Senior', 'High', 'Sa', 'kinabuhi', 'Dli', 'nato', 'kinahanglan', 'ibutang', 'tanan', 'pangit', 'nga', 'panghitabo', 'sa', 'ato', 'sa', 'kasakit', ',', 'kagool', 'ug', 'kasubo', 'kundi', 'himoon', 'nato', 'na', 'ug', 'basehan', 'nga', 'basin', 'naa', 'paba', 'tay', 'kulang', 'o', 'sobra', 'nga', 'gibuhat', 'nga', 'gikasuko', 'sa', 'Ginoo', 'nato', 'o', 'isipong', 'usa', 'rana', 'tanan', 'nga', 'pagsulay', 'sa', 'Ginoo', 'hantud', 'asa', 'kita', 'taman', 'sa', 'pagtoo', 'kaniya', ',', 'arun', 'kita', 'malig-on', 'o', 'arun', 'kita', 'magbag-o', 'Muboon', 'ko', 'nalang', '-', 'Gusto', 'kong', 'mag', 'pasalamat', 'sa', 'balik2x', 'sa', 'tanang', 'tao', 'nga', 'nitabang', 'nako', 'gikan', 'sa', 'sinugdanan', 'sa', 'akong', 'pagka', 'preso', 'hantud', 'sa', 'karon.', 'Sa', 'tanang', 'staff', 'ug', 'personnel', 'sa', 'Makilala', 'Mps', 'atong', 'tuiga', 'salamat', 'kaayo', 'sa', 'tanang', 'tabang', 'ninyo', 'nako', 'matambag', 'man', ',', 'guidance', 'hantud', 'sa', 'financials.', 'Salamat', 'kaayo', 'sainyong', 'tanang', 'mga', 'Ate', 'ug', 'Kuya', 'nako', 'nga', 'nahimong', 'maayo', ',', 'amigo', 'ug', 'nahimo', 'jud', 'tamong', 'ikaduhang', 'pamilya', ',', 'sainyoha', 'jud', 'nag', 'sugod', 'ang', 'bagong', 'chapter', 'sa', 'akong', 'kinabuhi.', 'Ug', 'Labaw', 'sa', 'tanan', 'sa', 'atong', 'Ginoo.', 'Ginoo', ',', 'salamat', 'kaayo', 'sa', 'tanan', 'chances', 'ug', 'oportunidad', 'nga', 'gihatag', 'nimo', 'nako', ',', 'wala', 'jud', 'ko', 'nimo', 'pasagdi', ',', 'wala', 'jud', 'kay', 'kapareha.', 'Salamat', 'kaayo', 'sa', 'abundnag', 'pagmahal', 'ug', 'grasya.', 'Daygon', 'imong', 'ngalan', '(', 'Sa', 'una', 'adik', 'pa', '!', 'Karun', 'Grade12', 'graduating', 'na', ')', 'Malipayong', 'tulo', 'ka', 'tuig', 'sa', 'pagka', 'preso', 'ug', 'tulo', 'ka', 'tuig', 'sa', 'bag-ong', 'kinabuhi', 'ka', 'nako', 'So', 'blessed', 'and', 'lucky', 'to', 'have', 'you', 'all', ',', 'no', 'words', 'can', 'say', 'how', 'thankful', 'i', 'am.', 'I', 'Love', 'you', 'po', '"'] 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, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 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, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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