Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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4,600 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitugtan', 'na', 'ang', 'BOLUNTARYONG', 'pagsul-ob', 'og', 'face', 'masks', 'OUTDOORS', 'sa', 'tibuok', 'Pilipinas.', 'Kini', 'human', 'giluwatan', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'Executive', 'Order', 'No.', '3', 'nga', 'naglatid', 'sa', 'maong', 'bag-ong', 'polisa', ',', 'sumala', 'pa', 'ni', 'Press', 'Secretary', 'Trixie', 'Cruz-Angeles.', 'Hingpit', 'nga', 'ipatuman', 'ang', 'maong', 'kamanduan', 'ni', 'Marcos', 'human', 'kini', 'ipagawas', 'sa', 'Official', 'Gazette', '.'] 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, 1, 2, 2, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
4,601 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['61', 'KA', 'MIGRANTE', 'NGA', 'NATANGGONG', 'SA', 'TUNGA', 'SA', 'DAGAT', 'SA', 'MALTA', ',', 'NALUWAS', 'Naluwas', 'sa', 'BBC', 'Pearl', 'ang', '61', 'ka', 'mga', 'migrante', 'kinsa', 'nilawig', 'sa', 'kadagatan', 'sa', 'Mediterranean', 'sulod', 'sa', '10', 'ka', 'adlaw', 'nga', 'wala'y', 'pagkaon', 'ug', 'tubig.', 'Ang', 'maong', 'cargo', 'vessel', ',', 'ubos', 'sa', 'mando', 'ni', 'Captain', 'Noel', 'Uy', 'kinsa', 'taga-Dumaguete.', 'Luwas', 'nga', 'naabot', 'sa', 'isla', 'sa', 'Crete', 'ang', 'usa', 'ka', 'grupo', 'sa', 'mga', 'migrante', 'kinsa', 'gikuha', 'sa', 'maong', 'cargo', 'ship', 'gikan', 'sa', 'gisakyan', 'nila', 'nga', 'gubaon', 'nga', 'sakayan', 'sa', 'pangisda.', 'Sumala', 'pa', 'sa', 'mga', 'awtodidad', 'niadtong', 'Miyerkules', ',', 'Setyembre', '7', ',', '2022.', 'Gikatahong', 'natanggong', 'sulod', 'sa', 'pila', 'ka', 'adlaw', 'ang', 'mga', 'Syrian', ',', 'Lebanese', 'ug', 'Palestinian', 'migrants', 'kinsa', 'wala', 'nakahibalo', 'nga', 'aduna'y', 'liki', 'ang', 'sakayan', 'sa', 'pangisda', 'nga', 'ilang', 'gisakyan', 'duol', 'sa', 'Malta.', 'Kini', 'human', 'sa', 'pagsulay', 'nila', 'nga', 'paglawig', 'halos', '10', 'ka', 'adlaw', 'gikan', 'sa', 'Lebanon', 'paingon', 'sa', 'Italy.', 'Niadtong', 'Martes', ',', 'usa', 'ka', 'Greek', 'navy', 'helicopter', 'ang', 'gi-airlift', 'ang', 'usa', 'ka', '4-anyos', 'nga', 'batang', 'babaye', 'kinsa', 'aduna'y', 'health', 'problems', 'ug', 'iyang', 'inahan', 'gikan', 'sa', 'BBC', 'Pearl', ',', 'apan', 'gideklara', 'nga', 'dead', 'on', 'arrival', 'ang', 'bata', 'sa', 'ospital', 'sa', 'Crete', '.'] 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, 7, 8, 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, 1, 2, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 5, 0, 0, 5, 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, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
4,602 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGTIAYONG', 'NEGOSYANTE', 'NGA', 'CHINESE', 'NATIONAL', 'SA', 'TANJAY', 'CITY', ',', 'GIPUSIL', 'Nakaangkon', 'og', 'samad', 'pinusilan', 'ang', 'mag-asawang', 'negosyante', 'nga', 'Chinese', 'nationals', 'human', 'sila', 'gitulis', 'ug', 'gipusil', 'sa', 'Lomboy', 'St.', ',', 'Barangay', 'Tugas', ',', 'Tanjay', 'City', 'niadtong', 'Domingo', ',', 'Setyembre', '11', ',', '2022.', 'Sumala', 'pa', 'sa', 'report', ',', 'giila', 'ang', 'mga', 'biktima', 'nga', 'sila', 'si', 'Zhuang', 'Jiangcheng', ',', '60', ',', 'alias', 'Mr.', 'Cheng', 'ug', 'Quiyan', 'Shi', 'alias', 'Ma'am', 'Shi', ',', 'kinsa', 'tag-iya', 'sa', 'Cheng', 'Enterprises', '(', 'kanhi', 'Red', 'Star', ')', 'nga', 'nahimutang', 'sa', 'poblacion', 'area', 'sa', 'syudad.', 'Ang', 'driver', 'sa', 'mga', 'biktima', 'kinsa', 'nihangyo', 'nga', 'dili', 'nganlan', ',', 'niingon', 'nga', 'naggikan', 'sila', 'sa', 'tindahan', 'sa', 'dihang', 'nahitabo', 'ang', 'insidente', 'mga', 'pasado', 'alas', 'siyete.', 'Pag-abot', 'nila', 'sa', 'tungod', 'sa', 'balay', 'sa', 'mga', 'biktima', ',', 'ninaog', 'sa', 'sakyanan', 'ang', 'magtiayon', 'aron', 'abrihan', 'ang', 'gikandado', 'nila', 'nga', 'gate', ',', 'nga', 'mao'y', 'buluhaton', 'nila', 'matag-adlaw.', 'Pagnaog', 'sa', 'mag-asawa', ',', 'aduna'y', 'usa', 'ka', 'wala', 'mailhing', 'lalake', 'nga', 'naka-motorsiklo', 'ang', 'nihunong', 'sa', 'ilang', 'tungod.', 'Nikanaog', 'ang', 'maong', 'lalaki', 'ug', 'giduol', 'ang', 'babayeng', 'biktima', 'ug', 'gibira', 'ang', 'dala', 'niya', 'nga', 'bag.', 'Sumala', 'pa', 'sa', 'report', ',', 'nagdumili', 'sa', 'paghatag', 'sa', 'bag', 'si', 'Ma'am', 'Shi', 'ug', 'nakigbinirahay', 'sa', 'tulisan.', 'Diri', 'na', 'gipusil', 'ang', 'babaye', 'sa', 'tiyan.', 'Nisulay', 'pagtabang', 'si', 'Mr.', 'Cheng', 'apan', 'gipusil', 'sab', 'siya', 'duol', 'sa', 'liog.', 'Upat', 'ka', 'mga', 'bala', 'sa', 'gituohang', 'cal.', '45', 'nga', 'pistola', 'ang', 'nakuha', 'sa', 'nahitabuan.', 'Sumala', 'pa', 'sa', 'saksi', ',', 'dali', 'nga', 'nisibat', 'ang', 'tulisan', 'dala', 'ang', 'bag', 'sa', 'mag-asawa', 'nga', 'aduna'y', 'sulod', 'nga', 'kwarta.', 'Gisulay', 'pa', 'kini', 'nga', 'gidam-agan', 'sa', 'driver', 'sa', 'mga', 'biktima', ',', 'apan', 'naka-ikyas', 'kini.', 'Nisibat', 'ang', 'tulisan', 'sakay', 'sa', 'iyang', 'motorsiklo', 'nga', 'naka-mask', 'ug', 'baller', 'cap.', 'Sa', 'pagkakaron', ',', 'gisusi', 'na', 'sab', 'sa', 'kapulisan', 'ang', 'mga', 'CCTV', 'camera', 'sa', 'dapit', 'kon', 'aduna'y', 'nakakuha', 'sa', 'tulisan', 'ug', 'sa', 'hitabo.', 'Matod', 'pa', 'sab', 'usa', 'ka', 'residente', 'sa', 'naasoy', 'nga', 'lugar', ',', 'aduna'y', 'kadudahan', 'nga', 'lalaki', 'ang', 'naghupo-hupo', 'atbang', 'sa', 'balay', 'sa', 'magasawa', 'niadtong', 'niaging', 'semana.', 'Apan', 'sa', 'dihang', 'namatikdan', 'kini', 'sa', 'mga', 'lumolupyo', ',', 'dali', 'kini', 'nga', 'nidagan.', 'Nagtuo', 'sila', 'nga', 'aduna', 'kini', 'kalambigitan', 'sa', 'nahitabo', 'nga', 'pagpanulis', 'ug', 'pagpamupusil', 'sa', 'mag-asawa.', '#', 'NewsBite', '|', 'with', 'reports', 'from', 'Ryan', 'Sorote', ',', 'DYWC', 'and', 'SayriNews', 'Tanjay'] 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 1, 2, 0, 0, 0, 0, 3, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 3, 4] | cebuaner |
4,603 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alang', 'sa', 'mga', 'estudyante', 'nga', 'nakasinati', 'og', 'krisis', 'ug', 'buot', 'mo-apply', 'sa', 'Educational', 'Assistance', ',', 'palihog', 'i-scan', 'ang', 'BAG-O', 'NGA', 'QR', 'Code', 'o', 'i-click', 'ang', 'link', 'alang', 'sa', 'inyong', 'application', 'https', ':', '/', '/', 'bit.ly', '/', 'EApplication7', 'Paabota', 'ang', 'tubag', 'gikan', 'sa', 'DSWD-7', 'pinaagi', 'sa', 'text', 'o', 'email', 'kung', 'asa', 'ipahigayon', 'ang', 'assessment', 'ug', 'payout', '.'] 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,604 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BAHO’G', 'ILOK', ',', 'WALA'Y', 'LIGO', 'DILI', 'KA-DRIVE', 'Nagpasa', 'og', 'resolusyon', 'ang', 'Barangay', 'Council', 'sa', 'Pooc', 'sa', 'Talisay', 'City', ',', 'Cebu', 'nga', 'nagdili', 'sa', 'mga', 'draybers', 'kinsa', 'dili', 'maayo', 'og', 'baho', 'sa', 'pagmaneho', 'og', 'e-bike'] 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, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,605 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'babaye', 'ang', 'nabiktima', 'sa', 'pagbinastos', 'sa', 'usa', 'ka', 'lalake', 'nga', 'nahitabo', 'usa', 'ka', 'coffee', 'shop', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'mga', '5:30', 'sa', 'hapon', 'niadtong', 'Huwebes', ',', 'Setyembre', '8', ',', '2022.', 'Sumala', 'pa', 'sa', 'biktima', ',', 'samtang', 'nagpasaka', 'siya', 'sa', 'hagdan', ',', 'ni-aksyon', 'na', 'og', 'hikap', 'sa', 'pribadong', 'parte', 'sa', 'iyang', 'lawas', 'ang', 'lalake', 'apan', 'iya', 'kining', 'nasagang', 'ug', 'sa', 'paa', 'lang', 'siya', 'nahikapan.', 'Dali', 'nga', 'nidalagan', 'pasaka', 'ang', 'biktima', 'ug', 'gi-on', 'ang', 'camera', 'aron', 'mailhan', 'ang', 'lalake', ',', 'apan', 'wala', 'niya', 'damha', 'nga', 'nikalit', 'og', 'saka', 'ang', 'lalake', 'ug', 'gihikap', 'ang', 'pribadong', 'parte', 'sa', 'iyang', 'lawas.', 'Na-blotter', 'na', 'sa', 'biktima', 'ngadto', 'sa', 'kapulisan', 'ang', 'suspek.', 'Gitago', 'sab', 'ang', 'pangalan', 'sa', 'biktima', 'subay', 'sa', 'hangyo', '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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,606 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETEÑO', 'GIBALIK', 'ANG', 'NAKIT-ANG', '$', '7,345', 'CANADIAN', 'DOLLARS', 'NGADTO', 'SA', 'TAG-IYA', 'Dalaygon', 'ang', 'pagkamatinud-anon', 'ni', 'Junrey', 'E.', 'Cadeliña', ',', 'usa', 'ka', 'pastor', 'ug', 'tricycle', 'driver', ',', 'human', 'iyang', 'giuli', 'ang', 'nakit-ang', '$', '7,345', 'Canadian', 'dollars', 'o', 'mokabat', 'sa', 'P304,000', 'sa', 'Philippine', 'pesos', 'ngadto', 'sa', 'tag-iya', 'nga', 'usa', 'ka', 'local', 'currency', 'trader.', 'Sumala', 'pa', 'sa', 'Dumaguete', 'City', 'Police', 'Station', ',', 'nakit-an', 'ni', 'Cadeliña', 'ang', 'bugkos', 'sa', 'kwarta', 'sa', 'kadalanan', 'sa', 'Perdices', 'Street', 'atubangan', 'sa', 'Saint', 'Catherine', 'of', 'Alexandria', 'Church', 'niadtong', 'Setyembre', '6', ',', '2022.', 'Gi-report', 'ni', 'Cadeliña', 'ngadto', 'sa', 'kapulisan', 'ang', 'iyang', 'nakit-an', 'nga', 'kwarta', 'ug', 'giimbestigahan', 'sab', 'dayon', 'kini', 'sa', 'awtoridad', 'aron', 'mahibal-an', 'kung', 'kinsa', 'ang', 'tag-iya', 'niini.', 'Pagkataod-taod', ',', 'aduna'y', 'usa', 'ka', 'local', 'currency', 'trader', 'ang', 'niduol', 'sa', 'kapulisan', 'ug', 'nangayo', 'og', 'tabang', 'aron', 'makit-an', 'ang', 'nawala', 'niya', 'nga', 'kwarta.', 'Gikompirma', 'ni', 'PEMS', 'Orlando', 'E', 'Gonzaga', 'ug', 'PSSg', 'Emelyn', 'D', 'Piñero', 'pinaagi', 'sa', 'CCTV', 'footage', 'gikan', 'sa', 'City', 'Command', 'Center', 'nga', 'naatak', 'ang', 'kwarta', 'gikan', 'sa', 'bulsa', 'sa', 'maong', 'trader', 'samtang', 'nagmaneho', 'kini', 'sa', 'iyang', 'motorsiklo.', 'Nabalik', 'ang', 'kinatibuk-ang', 'kwarta', 'nga', 'anaa', 'sa', '$', '7,345', 'Canadian', 'Dollars', '(', 'P304,000', ')', 'sa', 'tag-iya.', 'Dako', 'sab', 'ang', 'pagpasalamat', 'niini', 'sa', 'pagkamatinud-anon', 'ni', 'Pastor', 'Cadeliña', 'ug', 'sa', 'gipahigayong', 'imbestigasyon', '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. | [7, 0, 0, 0, 0, 0, 7, 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, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 1, 2, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,607 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Abrihon', 'na'g', 'balik', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', '-', 'Region', '7', 'ang', 'ONLINE', 'REGISTRATION', 'LINK', 'alang', 'sa', 'educational', 'assistance', 'sa', 'alas-12', 'sa', 'udto', 'karong', 'Sabado', ',', 'Setyembre', '10', ',', '2022.', 'Mosira', 'kini', 'kung', 'makompleto', 'na', 'ang', 'gidaghanon', 'nga', 'target', 'beneficiaries', 'alang', 'sa', 'tibuok', 'Region', '7.', 'Giawhag', 'sab', 'sa', 'DSWD', 'nga', 'dili', 'na', 'morehistro', 'pag-usab', 'kadtong', 'mga', 'nakarehistro', 'na', 'tungod', 'madoble', 'na', 'ang', 'ilang', 'registration', 'ug', 'hatagan', 'sab', 'og', 'higayon', 'ang', 'mga', 'estudyante', 'nga', 'naa', 'sa', 'krisis', 'nga', 'makarehistro', 'online', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 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, 5, 6, 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] | cebuaner |
4,608 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', ',', 'NAGTANYAG', 'OG', '₱79', 'UG', '₱199', 'FLIGHTS', 'SA', 'ILANG', '9.9', 'SEAT', 'SALE', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'holiday', '"', '9.9', 'seat', 'sale', '"', 'uban', 'sa', 'domestic', 'flights', 'nga', 'ingon', 'kaubos', 'sa', 'P79', ',', 'ug', 'international', 'flights', 'nga', 'ingon', 'kaubos', 'sa', 'P199.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'airline', 'niadtong', 'Huwebes', ',', 'Setyembre', '8', ',', '2022.', 'Sumala', 'pa', 'sa', 'Cebu', 'Pacific', ',', 'itanyag', 'ang', 'maong', 'seat', 'sale', 'sugod', 'alas-10', 'sa', 'buntag', 'sa', 'Setyembre', '9', 'hangtod', 'Setyembre', '13.', 'Aduna', 'kini', 'travel', 'period', 'gikan', 'Marso', '31', 'hangtod', 'Agosto', '31', ',', '2023.', 'Ang', 'pipila', 'ka', 'mga', 'destinasyon', 'nga', 'apil', 'sa', 'maong', 'seat', 'sale', 'mao', 'ang', 'Boracay', ',', 'Bohol', ',', 'Siargao', ',', 'Palawan', ',', 'ug', 'Cebu.', 'Apil', 'sab', 'sa', 'international', 'destinations', 'ang', 'mga', 'lugar', 'sama', 'sa', 'Ho', 'Chi', 'Minh', 'ug', 'Hanoi', 'sa', 'Vietnam', ';', 'o', 'Bali', 'ug', 'Jakarta', 'sa', 'Indonesia', ';', 'o', 'Kuala', 'Lumpur', 'ug', 'Kota', 'Kinabalu', 'sa', 'Malaysia', ';', 'o', 'Bangkok', 'sa', 'Thailand', ';', 'o', 'Seoul', 'sa', 'South', 'Korea.', 'Dugang', 'pa', 'nila', ',', 'aduna', 'sab', 'sila'y', 'nagpadayon', 'nga', '"', 'BER', 'sale', '"', 'nga', 'aduna'y', 'seats', 'alang', 'sa', 'domestic', 'destinations', 'nga', 'ingon', 'kaubos', 'sa', 'P88', 'nga', 'one-way', 'base', 'fare.', 'Aduna', 'kini', 'travel', 'dates', 'apil', 'karon', 'hangtod', 'Pebrero', '20', ',', '2023.', 'Kasamtangang', 'gipadagan', 'sa', 'kompanya', 'ang', '88', '%', 'sa', 'pre-pandemic', 'systemwide', 'capacity', '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. | [3, 4, 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, 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, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 6, 0, 5, 6, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 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] | cebuaner |
4,609 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisugdan', 'na', 'sa', 'mobile', 'wallet', 'provider', 'nga', 'GCash', 'ang', 'pagtangtang', 'sa', 'mga', 'letra', 'sa', 'pangalan', 'sa', 'account', 'kung', 'mohimo', 'og', 'mga', 'transaksyon.', 'Bag-ohay', 'lamang', ',', 'gianunsyo', 'sa', 'GCash', 'nga', 'himuon', 'ang', 'confirmation', 'notifications', 'sulod', 'sa', 'app', 'ug', 'dili', 'sa', 'SMS', ',', 'human', 'sa', 'pagdagsang', 'sa', 'mga', 'spam', 'text', 'messages', '.'] 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, 3, 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] | cebuaner |
4,610 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['1', 'KA', 'NPA', 'PATAY', 'SA', 'ENGKWENTRO', 'BATOK', 'SA', 'MILITAR', 'SA', 'GUIHULNGAN', 'CITY', 'Usa', 'ka', 'giingon', 'NPA', 'ang', 'napatay', 'sa', 'engkwento', 'batok', 'sa', 'militar', 'sa', 'Sitio', 'Manlibod', ',', 'Barangay', 'Trinidad', ',', 'Guihulngan', 'City', 'mga', 'alas-7', 'sa', 'buntag', 'niadtong', 'Miyerkules', ',', 'Setyembre', '7', ',', '2022.', 'Sumala', 'pa', 'sa', 'report', ',', 'nipahibalo', 'ang', 'nabalaka', 'nga', 'mga', 'residente', 'sa', 'mga', 'sundalo', 'sa', '62nd', 'Infantry', 'Battalion', 'ilalom', 'sa', 'operational', 'control', 'sa', '303rd', 'Brigade', 'bahin', 'sa', 'presensya', 'sa', 'mga', 'giingong', 'NPA', 'sa', 'naasoy', 'nga', 'lugar', 'ug', 'plano', 'nga', 'mangikil.', 'Dali', 'nga', 'niresponde', 'ang', 'Mobile', 'Community', 'Sustainment', 'Support', 'Team', '(', 'MCSST', ')', 'sa', '62nd', 'Infantry', 'Battalion', 'ilalom', 'sa', 'pangulo', 'ni', 'Lt', 'Colonel', 'William', 'Pesase', ',', 'Battalion', 'Commander', 'bahin', 'sa', 'reports', 'sa', 'mga', 'residente', 'sa', 'presensya', 'sa', 'armadong', 'grupo', 'nga', 'giingong', 'sakop', 'sa', 'rebelde.', 'Kinatibuk-ang', 'kapin', 'o', 'kulang', 'sa', 'walo', 'ka', 'mga', 'giingon', 'sakop', 'sa', 'rebelde', 'gikan', 'sa', 'SDG', 'Platoon', 'of', 'Central', 'Negros', '1', ',', 'Komiteng', 'Rehiyon', '-', 'Negros', ',', 'Cebu', ',', 'Bohol', 'and', 'Siquijor', '(', 'KR-NCBS', ')', 'ang', 'naengkwentro', 'sa', 'kasundaluhan', 'ug', 'nilungtad', 'og', 'halos', 'lima', 'ka', 'minuto', 'ang', 'pagpinusilay.', 'Nitakas', 'sab', 'sa', 'nagkalain-lain', 'direksyon', 'ang', 'kontra', 'ug', 'pagbiya', 'sa', 'ilang', 'personal', 'nga', 'mga', 'butang', 'sa', 'encounter', 'site.', 'Tungod', 'niini', ',', 'usa', 'ka', 'giingong', 'miyembro', 'sa', 'NPA', 'ang', 'napatay', 'sa', 'militar', 'kinsa', 'wala', 'pa', 'mailhi.', 'Nakuha', 'gikan', 'niya', 'ang', 'usa', 'ka', 'caliber', '45', 'pistol', 'nga', 'aduna'y', 'SN', 'M08113971', ',', 'usa', 'ka', 'hand', 'grenade', ',', 'duha', 'ka', 'magazines', 'alang', 'sa', 'caliber', '45', 'pistol', 'nga', 'loaded', 'sa', '12', 'live', 'rounds', ',', '34', 'ka', 'piraso', 'sa', 'live', 'ammunitions', 'alang', 'sa', '380', 'revolver', ',', 'usa', 'ka', 'binocular', ',', 'nakuha', 'sab', 'ang', 'war', 'materiel', ',', 'ug', 'nagkalain-laing', 'personal', 'nga', 'mga', 'butang.', 'Samtang', ',', 'wala'y', 'namatay', 'sa', 'kasundaluhan', '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. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 3, 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, 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, 3, 4, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 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, 3, 4, 4, 4, 4, 4, 0, 3, 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, 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] | cebuaner |
4,611 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Girekomenda', 'sa', 'Inter-Agency', 'Task', 'Force', 'nga', 'himoong', 'BOLUNTARYO', 'na', 'lamang', 'ang', 'pagsul-ob', 'og', 'face', 'mask', 'sa', 'tibuok', 'nasud.', 'Kini', 'sumala', 'pa', 'sa', 'anunsyo', 'ni', 'Press', 'Secretary', 'Trixie', 'Cruz-Angeles', 'karong', 'adlawa', ',', 'Sept.', '7', ',', '2022.', 'Hinuon', ',', 'giklaro', 'ni', 'Angeles', 'nga', 'dili', 'pa', 'kini', 'hingpit', 'nga', 'nahimong', 'polisa', 'ug', 'gipaabot', 'na', 'lang', 'ang', 'opisyal', 'nga', 'kamanduan', 'nunot', 'niini.', 'Gibutyag', 'sab', 'ni', 'Health', 'Undersecretary', 'Ma.', 'Rosario', 'Vergeire', 'nga', 'duna', 'na'y', '"', 'verbal', 'approval', '"', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'maong', 'rekomendasyon', '.'] 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, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0] | cebuaner |
4,612 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'mga', 'hulagway', 'gikan', 'sa', 'malamposon', 'nga', '2022', 'Titanic', 'expedition', 'sa', 'pagkuha', 'sa', 'world', ''s', 'first', '8K', 'footage', 'sa', 'nalunod', 'nga', 'barko', ',', 'diin', 'makita', 'ang', 'mga', 'detalye', 'sa', 'pagkaguba', 'niini', 'sa', 'ilalom', 'sa', 'dagat.', 'Mahinumduman', 'nga', 'nalunod', 'ang', 'barkong', 'RMS', 'Titanic', 'niadtong', 'April', '15', ',', '1912.', 'Liboan', 'ka', 'pasahero', 'ug', 'crew', 'ang', 'namatay', 'sa', 'maong', 'trahedya', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,613 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BABAYE', 'SA', 'CEBU', ',', 'NADAKPAN', 'HUMAN', 'GIINGONG', 'GIGUBA', 'ANG', 'TV', 'SA', 'VIDEOKEHAN', 'SAMTANG', 'HUBOG', 'Usa', 'ka', 'lalake', 'ug', 'tulo', 'ka', 'babaye', 'ang', 'nalandig', 'sa', 'prisohan', 'human', 'sa', 'pagguba', 'sa', 'usa', 'ka', 'telebisyon', 'samtang', 'sila', 'nag-videoke', 'sa', 'usa', 'ka', 'videoke', 'house', 'sa', 'Barangay', 'Bulacao', ',', 'Cebu', 'City', 'mga', '1:30am', 'niadtong', 'Martes', ',', 'Setyembre', '6', ',', '2022.', 'Ang', 'babaye', 'kinsa', 'nisumbag', 'sa', 'TV', ',', 'niingon', 'nga', 'siya', 'hubog', 'lang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 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, 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] | cebuaner |
4,614 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SR.', 'MILA', 'SILAB', ',', 'OPISYAL', 'NANG', 'NILINGKOD', 'ISIP', 'IKA-4', 'NGA', 'PRESIDENTE', 'SA', 'SPUD', 'Opisyal', 'na', 'nga', 'ikaupat', 'nga', 'Univerity', 'President', 'sa', 'St.', 'Paul', 'University', 'Dumaguete', '(', 'SPUD', ')', 'si', 'Sister', 'Mila', 'Grace', 'A.', 'Silab', 'niadtong', 'Setyembre', '5', ',', '2022', 'sa', 'usa', 'ka', 'Academic', 'Investiture', 'ug', 'Concelebrated', 'Holy', 'Mass', 'uban', 'si', 'Bishop', 'Julito', 'B.', 'Cortes', ',', 'D.D.', 'isip', 'Main', 'Celebrant.', 'Usa', 'ka', 'nurse', 'si', 'Sister', 'Mila', 'ug', 'nagkupot', 'sa', 'nagkalain-laing', 'posisyon', 'ug', 'assignment', 'sa', 'tibuok', 'nasud', 'sama', 'sa', 'St.', 'Paul’s', 'Hospital', 'Iloilo', 'City', 'ug', 'St.', 'Paul', 'University', 'Philippines', 'Tuguegarao', 'City.', 'Human', 'niini', ',', 'gibutang', 'siya', 'isip', 'Dean', 'sa', 'College', 'of', 'Nursing', 'sa', 'St.', 'Paul', 'University', 'Surigao', 'City', 'ug', 'nahimong', 'Director', 'sa', 'Nursing', 'Services', 'sa', 'Maria', 'Reyna', 'Hospital', 'sa', 'Cagayan', 'de', 'Oro', 'City.', 'Niadtong', '2010', ',', 'gibutang', 'siya', 'isip', 'Dean', 'sa', 'College', 'of', 'Nursing', 'sa', 'St.', 'Paul', 'University', 'Dumaguete', 'ug', 'sa', 'samang', 'higayon', 'siya', 'sab', 'ang', 'Local', 'Superior', 'sa', 'SPC', 'Sisters', 'sa', 'Dumaguete.', 'Pagka', '2016', ',', 'gibutang', 'sab', 'siya', 'isip', 'Second', 'President', 'sa', 'St.', 'Paul', 'University', 'Iloilo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 8, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0] | cebuaner |
4,615 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NORECO', 'II', ',', 'GI-SUSPENSO', 'ANG', 'PAGPANINGIL', 'OG', '10', '%', 'PENALTY', 'SA', 'MGA', 'LATE', 'MAMAYAD', 'OG', 'BILL', 'Hingpit', 'na', 'nga', 'ipatuman', 'sa', 'NORECO', 'II', 'ang', 'regulasyon', 'sulod', 'sa', 'Magna', 'Carta', 'for', 'Residential', 'Electricity', 'Consumers', 'apil', 'na', 'ang', 'polisiya', 'sa', 'kooperatiba', 'base', 'sa', 'maong', 'balaudnon', 'bahin', 'sa', 'diskoneksyon', 'sa', 'serbisyo', 'sa', 'kuryente', 'kung', 'dili', 'makabayad', 'sa', 'tukmang', 'panahon.', 'Sugod', 'Setyembre', ',', 'sundon', 'na', 'sa', 'kooperatiba', 'ang', 'siyam', '(', '9', ')', 'ka', 'adlaw', 'gikan', 'sa', 'pagdawat', 'sa', 'electricity', 'bill', 'o', 'pito', '(', '7', ')', 'ka', 'adlaw', 'gikan', 'sa', 'due', 'date', 'sa', 'electricity', 'bill', 'isip', 'lugway', 'sa', 'bayranan', 'sa', 'kuryente.', 'Kung', 'mulapaw', 'sa', 'gihatag', 'nga', 'lugway', 'ug', 'human', 'sa', '48', 'ka', 'oras', 'nga', 'Notice', 'of', 'Disconnection', ',', 'ipatuman', 'na', 'ang', 'diskoneksyon', 'sa', 'electric', 'service.', 'Ang', 'maong', 'lakang', ',', 'makatabang', 'aron', 'makab-ot', 'ang', 'collection', 'efficiency', 'base', 'sa', 'sukdanan', 'sa', 'National', 'Electrification', 'Administration.', 'Makatabang', 'sab', 'kini', 'aron', 'dili', 'malangay', 'ang', 'pag-remit', 'sa', 'bulanong', 'bayronon', 'sa', 'mga', 'supplier', 'sa', 'kuryente', 'ug', 'ahensiya', 'sa', 'gobyerno', 'tungod', 'mopahamtang', 'man', 'kini', 'sila', 'og', 'penalties.', 'Gi-suspendi', 'na', 'sab', 'NORECO', 'II', 'ang', 'pagpataw', 'sa', '10', '%', 'nga', 'surcharge', 'sa', 'mga', 'ulahi', 'nga', 'namayad', 'sa', 'bill', 'sa', 'kuryente.', 'Apan', 'giawhag', 'sa', 'NORECO', 'II', 'ang', 'tanan', ',', 'pribado', 'ug', 'ahensiya', 'sa', 'gobyerno', ',', 'nga', 'mobayad', 'sa', 'binulan', 'nga', 'bayronon', 'sa', 'kuryente', 'sulod', 'sa', 'lugway', 'nga', 'gihatag', 'aron', 'malikayan', 'nga', 'maputlan', 'sa', 'koneksyon', 'sa', 'kuryente', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 7, 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, 0, 0, 0, 0, 0, 0, 0, 0, 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,616 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niubos', 'na', 'sab', 'ang', 'bili', 'sa', 'atong', 'piso', 'kontra', 'dolyar', 'karong', 'adlawa', ',', 'Sept.', '6', ',', '2022.', 'Nisira', 'ang', 'peso-dollar', 'exchange', 'sa', 'P57', '=', '$', '1', 'karong', 'Martes.', 'Mao', 'na', 'kini', 'ang', 'kinaubsan', 'nga', 'bili', 'sa', 'piso', 'sulod', 'sa', '18', 'ka', 'tuig', ',', 'sukad', 'pa', 'niadtong', 'tuig', '2004.', 'Sumala', 'pa', 'ni', 'kanhing', 'Bangko', 'Sentral', 'ng', 'Pilipinas', 'Deputy', 'Governor', 'Diwa', 'Guinigundo', ',', 'makaapekto', 'ang', 'pagmaba', 'sa', 'bili', 'sa', 'piso', 'kontra', 'dolyar', 'sa', 'pagtaas', 'sa', 'presyo', 'sa', 'mga', 'palaliton', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,617 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LA', 'LIBERTAD', ',', 'NAGTANYAG', 'OG', 'MGA', 'BIKE', 'GIKAN', 'SA', 'JAPAN', 'SA', 'PRESYONG', 'P1,000', 'Nagtanyag', 'ang', 'kagamhanang', 'lungsod', 'sa', 'La', 'Libertad', 'og', 'barato', 'nga', 'mga', 'bisikleta', 'nga', 'napalit', 'nila', 'gikan', 'sa', 'nasud', 'sa', 'Japan', 'sa', 'presyong', 'P1,000.', 'Sumala', 'pa', 'ni', 'La', 'Libertad', 'Mayor', 'Emmanuel', 'Laurence', 'Daniel', '"', 'MM', '"', 'Limkaichong', 'Iway', ',', 'tumong', 'sa', 'programa', 'nga', 'makatabang', 'sa', 'mga', 'commuter', 'nga', 'makaminus', 'sa', 'plitehan', ',', 'makatabang', 'pag-amping', 'sa', 'kinaiyahan', 'ug', 'pagdasig', 'sa', 'katawhan', 'sa', 'pag-ehersisyo', 'alang', 'sa', 'maayong', 'panglawas', '500', 'ka', 'mga', 'used', 'city', 'bikes', 'ang', 'gipalit', 'sa', 'kagamhanan', 'gikan', 'sa', 'tigsuplay', 'niini', 'sa', 'Japan', 'ug', 'gitanyag', 'kini', 'sa', 'pinakaubos', 'nga', 'presyo', 'nga', 'anaa', 'lamang', 'sa', 'P1,000', 'matag', 'usa', 'ka', 'bisekleta.', 'Gawas', 'niini', ',', 'aduna', 'sab', 'mga', 'city', 'bikes', 'nga', 'gibutang', 'sa', 'mga', 'barangay', 'aron', 'magamit', 'sa', 'katawhan', 'nga', 'buot', 'manghulam', 'og', 'bisekleta.', '"', 'Naa', 'pud', 'ta'y', 'mga', 'bikrs', 'nga', 'gibilin', 'nato', 'sa', 'mga', 'barangay', 'hall', 'para', 'kung', 'naa'y', 'ganahan', 'mohulan', ',', 'adto', 'lang', 'sa', 'ilang', 'barangay', 'mohulam', ',', '"', 'matod', 'pa', 'ni', 'Mayor', 'Iway.', 'Niadtong', '2021', ',', 'nipalit', 'sab', 'og', '500', 'used', 'city', 'bikes', 'ang', 'kagamhanan', 'alang', 'sa', 'susamang', 'programa.', 'Alang', 'sa', 'mga', 'interesadong', 'mopalit', ',', 'mahimong', 'mobisita', 'sa', 'La', 'Libertad', 'Municipal', 'Treasure', ''s', 'Office', '.'] 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, 6, 0, 0, 0, 0, 0, 0, 0, 5, 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, 5, 6, 0, 1, 2, 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, 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, 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, 5, 6, 6, 6, 6, 6, 0] | cebuaner |
4,618 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ania', 'ang', 'pamahayag', 'sa', 'NORECO', 'II', 'nunot', 'sa', 'nadawat', 'nila', 'nga', 'order', 'gikan', 'sa', 'Energy', 'Regulatory', 'Commission', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0] | cebuaner |
4,619 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NTC-7', ',', 'NAGPAHIMANGNO', 'BATOK', 'SA', 'MGA', 'PERSONALIZED', 'TEXT', 'SCAM', 'NGA', 'NAGTANYAG', 'OG', 'TRABAHO', 'UG', 'KWARTA', 'Nagpahimangno', 'ang', 'National', 'Telecommunications', 'Commission', '(', 'NTC', ')', 'Region', '7', 'sa', 'mga', 'subscibers', 'niini', 'batok', 'sa', '"', 'New', 'Variants', 'of', 'Fake', 'Job', 'and', 'Other', 'Text', 'Scams', '"', 'nga', 'gi-target', 'ang', 'kinatibuk-ang', 'publiko.', 'Nagpadayon', 'ang', 'pagdagsang', 'sa', 'mga', 'fake', 'job', 'text', ',', 'lucky', 'winner', 'ug', 'susama', 'nga', 'money', 'scams', 'nga', 'gi-target', 'ang', 'kinatibuk-ang', 'publiko', 'sa', 'bulan', 'sa', 'Agosto', 'sa', 'tanang', 'telecommunications', 'networks', 'sa', 'tibuok', 'nasud', 'uban', 'sa', 'bag-ong', 'klase', 'niini', 'kung', 'diin', 'apil', 'na', 'ang', 'pangalan', 'sa', 'makadawat', '.'] 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, 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, 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 |
4,620 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NORECO', 'II', ',', 'GIMANDUAN', 'NGA', 'IPAHUNONG', 'ANG', 'PAGKOLEKTA', 'OG', '10', '%', 'PENALTY', 'SA', 'MGA', 'LATE', 'MAMAYAD', 'OG', 'BILL', 'SA', 'KURYENTE', 'Gimando', 'sa', 'Energy', 'Regulatory', 'Commission', '(', 'ERC', ')', 'ang', 'Negros', 'Oriental', 'II', 'Electric', 'Cooperative', '(', 'NORECO', 'II', ')', 'nga', 'ipahunong', 'dayon', 'ang', 'pagkolekta', 'og', '10', '%', 'interest', 'penalty', 'sa', 'mga', 'ulahi', 'nga', 'namayad', 'sa', 'ilang', 'bill', 'sa', 'kuryente.', 'Gisubli', 'sa', 'ERC', 'nga', 'sukwahi', 'kini', 'sa', 'Section', '23', 'sa', 'Electric', 'Power', 'Industry', 'Reform', 'Act', 'o', 'EPIRA', 'law.', 'Gi-isyu', 'ni', 'Commissioner', 'Floresinda', 'G.', 'Baldo-Digal', 'ang', 'order', 'niadtong', 'Setyembre', '2', ',', '2022', 'batok', 'sa', 'kooperatiba', 'alang', 'sa', 'paglapas', 'sa', 'Section', '23', 'sa', 'RA', '9136', 'o', 'EPIRA', 'law.', 'Mahinumduman', 'nga', 'gikwestiyon', 'ni', '2nd', 'Dist.', 'Rep.', 'Manuel', 'T.', 'Sagarbarria', 'ang', 'kataas', 'sa', '10', '%', 'penalty', 'apil', 'na', 'ang', 'per', 'kilowatt', 'hour', 'charges', 'sa', 'kooperatiba', 'pinaagi', 'sa', 'gipirmahan', 'nga', 'petisyon', 'sa', 'mga', 'konsumidor', 'sa', 'kuryente', 'sa', 'iyang', 'distrito', 'sa', 'wala', 'pa', 'ang', 'National', 'Electrification', 'Administration', '(', 'NEA', ')', 'ug', 'ERC.', 'Gisubli', 'sa', 'ERC', 'order', 'nga', 'samtang', 'gitugutan', 'sa', 'Sec', '23', 'sa', 'RA', '9136', 'ang', 'distribution', 'utilities', 'nga', 'ipahamtang', 'ug', 'ikolekta', 'sa', 'distribution', 'wheeling', 'charges', 'ug', 'uban', 'pang', 'charges', 'nga', 'kinahanglanon', 'sa', 'pagpatuman', 'sa', 'ilang', 'negosyo', ',', 'subject', 'for', 'approval', 'gihapon', 'sa', 'Commission', 'ang', 'ingon', 'ana', 'nga', 'mga', 'charges.', 'Gipakita', 'sa', 'records', 'sa', 'Commission', 'nga', 'wala'y', 'aprobado', 'nga', 'Other', 'Charges', 'Rate', 'ang', 'NORECO', 'II', 'alang', 'sa', 'mga', 'ulahing', 'namayad.', 'Wala', 'sab', 'ni', 'usa', 'ka', 'application', 'for', 'approval', 'ang', 'gisumite', 'nila', 'sa', 'Commission', 'bahin', 'sa', 'Other', 'Charges.', 'Subay', 'niini', ',', 'gimandoan', 'ang', 'NORECO', 'II', 'sa', 'pagsumite', 'sulod', 'sa', '15', 'ka', 'adlaw', 'gikan', 'sa', 'pagdawat', 'sa', 'maong', 'order', 'ang', 'gipamatud-an', 'nga', 'katin-awan', 'ug', 'pagpakita', 'og', 'hinungdan', 'nganong', 'wala'y', 'silot', 'nga', 'administratiba', 'ang', 'ipahamtang', 'niini', 'tungod', 'sa', 'pagpatuman', 'nila', 'sa', '10', '%', 'rate', 'isip', 'penalty', 'sa', 'mga', 'ulahi', 'nga', 'namayad', 'sa', 'bill', 'sa', 'kuryente', 'ug', 'sa', 'pagpahunong', 'dayon', 'sa', 'pagkolekta', 'niini', 'gikan', 'sa', 'mga', 'konsumidor.', '#', 'NewsBite', '|', 'with', 'reports', 'from', 'Choy', 'Gallarde'] 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, 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, 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, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 3, 4, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 0, 7, 8, 0, 0, 0, 0, 5, 6, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 7, 8, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,621 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAODNON', 'PAG-BAN', 'SA', 'MGA', 'JUNK', 'FOOD', 'SA', 'MGA', 'PAMPUBLIKONG', 'ESKWELAHAN', ',', 'GIDUSO', 'NI', 'SEN.', 'LITO', 'LAPID', 'Gipasaka', 'ni', 'Senador', 'Lito', 'Lapid', 'ang', 'usa', 'ka', 'balaodnon', 'nga', 'nagtinguha', 'nga', 'i-ban', 'ang', 'junk', 'food', 'ug', 'sugary', 'drinks', 'sa', 'mga', 'pampublikong', 'tunghaan', 'aron', 'tubag', 'sa', 'child', 'obesity', 'ug', 'malnutrition.', 'Tumong', 'sa', 'Senate', 'Bill', 'No.', '1231', 'nga', 'magtukod', 'og', 'mahimsog', 'nga', 'pagkaon', 'ug', 'ilimnon', 'nga', 'programa', 'alang', 'sa', 'tanang', 'pampublikong', 'elementary', 'ug', 'sekondarya', 'nga', 'mga', 'tunghaan', 'ug', 'learnining', 'institutions.', 'Gisubli', 'ni', 'Senador', 'Lapid', 'nga', 'aduna'y', 'importante', 'nga', 'papel', 'ang', 'usa', 'ka', 'healthful', 'diet', 'alang', 'sa', 'learning', 'ug', 'cognitive', 'development.', 'Gipakita', 'sa', 'mga', 'pagtuon', 'ni', 'Lapid', 'nga', 'ang', 'mga', 'bata', 'kinsa', 'dili', 'makakuha', 'og', 'igo', 'nga', 'sustansiya', 'mao'y', 'maglisod', 'sa', 'pagkat-on', ',', 'hinungdan', 'nga', 'makakuha', 'sila', 'og', 'maba', 'nga', 'academic', 'scores.', 'Dugang', 'pa', 'niya', 'nga', 'kung', 'masiguro', 'nga', 'makakaon', 'ang', 'mga', 'estudyante', 'og', 'pagkaon', 'nga', 'aduna'y', 'taas', 'nga', 'nutritional', 'value', ',', 'masiguro', 'sab', 'nga', 'molambo', 'ang', 'kahimsog', 'sa', 'ilang', 'panglawas', 'ingon', 'man', 'ang', 'ilang', 'performance', 'sa', 'eskwelahan', '.'] 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, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 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, 1, 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] | cebuaner |
4,622 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Special', 'guests', 'of', 'honor', 'sa', 'Maskara', 'Festival', '2022', 'sa', 'Bacolod', 'sila', 'si', 'President', 'Ferdinand', '"', 'Bongbong', '"', 'Marcos', 'Jr.', ',', 'iyang', 'asawa', 'nga', 'si', 'First', 'Lady', 'Liza', 'Araneta-Marcos', ',', 'ug', 'iyang', 'anak', 'nga', 'si', 'Vinny', ',', 'sumala', 'pa', 'sa', 'organizer', 'niini.', 'Pangulohan', 'ang', 'maong', 'kapistahan', 'karong', 'Oktubre', 'nila', 'ni', 'Mayor', 'Alfredo', 'Abelardo', '"', 'Albee', '"', 'Benitez', ',', 'Masskara', 'Honorary', 'Chairman', 'Jojie', 'Dingcong', ',', 'Festival', 'Director', 'lawyer', 'Pinky', 'Mirano-Ocampo', ',', 'ug', 'Victorias', 'City', 'Mayor', 'Javier', 'Miguel', '"', 'Javi', '"', 'Benitez', 'kinsa', 'mao'y', 'kasamtangang', 'presidente', 'sa', 'Negros', 'Association', 'of', 'Chief', 'Executives.', 'Sumala', 'pa', 'organizer', ',', 'i-feature', 'sa', 'three-week', 'festivities', 'ang', 'mosunod', ':', 'Masskaralympics', 'sa', 'Oktubre', '1-16', ';', 'Masskara', 'Trade', 'Show', 'sa', 'Oktubre', '10', '-16', ';', 'ug', 'Masskara', 'Cultural', 'and', 'Culminating', 'Activities', 'sa', 'Oktubre', '19-23.', 'Ang', 'tema', 'karong', 'tuiga', 'mao', 'ang', '"', 'Balik', 'Yuhom', '.', '"'] 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, 5, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 7, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 5, 6, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,623 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', ',', 'GIDUNGGAB', 'TUNGOD', 'SA', 'AWAY', 'SA', 'MANSANITAS', 'Gidunggab', 'ang', 'usa', 'ka', 'lalaki', 'sa', 'Sibulan', 'niadtong', 'Sabado', 'tungod', 'lamang', 'sa', 'giingong', 'panaglalis', 'bahin', 'sa', 'punoan', 'sa', 'mansanitas.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', 'sa', 'Sibulan', ',', 'gidunggab', 'sa', 'makadaghang', 'higayon', 'sa', 'suspek', 'ang', 'biktima', 'nga', 'si', 'Romeo', 'Lapinig', 'nga', 'lumolupyo', 'sa', 'Purok', 'Narra', ',', 'Barangay', 'Tubtubon.', 'Giingong', 'ganahan', 'nga', 'putlon', 'sa', 'biktima', 'ang', 'punoan', 'sa', 'mansanitas', 'nga', 'gipanag-iya', 'sa', 'suspek.', 'Gibulag', 'sila', 'sa', 'live-in', 'partner', 'sa', 'biktima', 'ug', 'dali', 'siya', 'nga', 'gidala', 'sa', 'ospital', 'kung', 'diin', 'kasamtangan', 'kini', 'nga', 'gitambalan.', '"', 'Wala', 'na-control', 'sa', 'suspek', 'ang', 'iyang', 'kasuko', ',', '"', 'dungag', 'pa', 'sa', 'report', 'sa', 'police', 'station', 'sa', 'English', 'nga', 'pinulongan.', 'Anaa', 'na', 'sa', 'kustodiya', 'sa', 'kapulisan', 'ang', 'suspek', 'ug', 'pasakaan', 'siya', 'og', 'kaso', 'nga', 'frustrated', 'homicide', '.'] 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, 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, 1, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,624 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SC', ':', 'ILEGAL', ',', 'DISKRIMINASYON', 'ANG', 'PAGPAPAHAWA', 'SA', 'USA', 'KA', 'EMPLEYADO', 'TUNGOD', 'NAKIGMINYO', 'KINI', 'SA', 'IYANG', 'KATRABAHO', 'Gideklara', 'sa', 'Supreme', 'Court', 'niadtong', 'Agosto', '30', 'nga', 'discriminasyon', 'ang', '"', 'no-spouse', '"', 'rules', 'alang', 'sa', 'mga', 'empleyado', 'gawas', 'kon', 'aduna'y', 'makatarungangon', 'nga', 'rason', 'sa', 'negosyo.', 'Ang', 'maong', 'desisyon', 'nagtimaan', 'sa', 'katapusan', 'sa', 'halos', 'pulo', 'ka', 'tuig', 'nga', 'legal', 'battle', 'sa', 'petitioner', 'nga', 'si', 'Catherine', 'dela', 'Cruz-Cagampan', ',', 'kinsa', 'gipapahawa', 'sa', 'iyang', 'trabaho', 'sa', 'One', 'Network', 'Bank', ',', 'Incorporated', '(', 'ONBI', ')', 'human', 'nakigminyo', 'siya', 'sa', 'iyang', 'katrabaho', 'nga', 'si', 'Audie', 'Angelo.', 'Wala', 'hatagi', 'sa', 'korte', 'og', 'katakus', 'ang', 'argumento', 'sa', 'bangko', 'nga', 'ang', 'kaminyuon', 'ni', 'Cagampan', 'ngadto', 'ni', 'Angelo', ',', 'nagbutang', 'sa', 'pondo', 'sa', 'bangko', 'ngadto', 'sa', 'peligro', 'sa', '"', 'embezzlement', '"', 'o', 'pagpangawkaw.', 'Sumala', 'pa', 'sa', 'korte', ',', 'kinahanglan', 'nga', 'nakabase', 'sa', 'kamatuoran', 'ang', 'mga', 'pahayag', 'nga', 'sama', 'niini', 'ug', 'dili', 'sa', 'mga', 'espekulasyon', '.'] 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, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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] | cebuaner |
4,625 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DEPED', ':', 'GENDER-RESPONSIVE', 'EDUCATION', 'POLICY', ',', 'IPATUMAN', 'SA', 'TANANG', 'ELEMENTARY', ',', 'HIGH', 'SCHOOL', 'SA', 'TANANG', 'REHIYON', 'Strikto', 'nga', 'ipatuman', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'ang', 'Gender-Responsive', 'Education', 'Policy', 'sa', 'tanang', 'elementary', 'ug', 'high', 'schools', 'sa', 'tanang', 'rehiyon.', 'Subay', 'kini', 'sa', 'mandato', 'sa', 'DepEd', 'nga', 'pagsiguro', 'sa', 'paghatag', 'og', 'kalidad', 'nga', 'basic', 'education', 'alang', 'sa', 'tanan.', 'Ang', 'DepEd', 'Order', 'No.', '32', ',', 's.', '2017', ',', 'maghatag', 'og', '"', 'guidelines', 'for', 'Gender-Responsive', 'Basic', 'Education', 'that', 'shall', 'allow', 'the', 'Department', 'to', 'integrate', 'the', 'principles', 'of', 'gender', 'equality', ',', 'gender', 'equity', ',', 'gender', 'sensitivity', ',', 'non-discrimination', ',', 'and', 'human', 'rights', 'in', 'the', 'provision', 'and', 'governance', 'of', 'basic', 'education.', '"', 'Ang', 'maong', 'Order', ',', 'magtugot', 'sa', 'Departamento', 'sa', 'paghimo', 'og', 'gender-mainstreaming', 'aron', 'matubag', 'ang', 'mga', 'isyu', 'ug', 'kabalaka', 'sa', 'basic', 'education', 'nga', 'may', 'kalabot', 'sa', 'gender', 'ug', 'sexuality.', 'Isiguro', 'nga', 'ang', 'tanan', 'nga', 'mga', 'estudyante', 'mapanalipdan', 'sa', 'tanang', 'klase', 'sa', 'gender-related', 'violence', ',', 'abuse', ',', 'exploitation', ',', 'discrimination', 'ug', 'bullying.', 'Iduso', 'sab', 'niini', 'ang', 'gender', 'equality', 'ug', 'non-discrimination', 'sa', 'tanang', 'lebel', 'sa', 'pagdumala', '.'] 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 7, 8, 8, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,626 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'YELLOW', 'FINS', ',', 'NAKADAOG', 'SA', '4', 'KA', 'CATEGORY', 'SA', 'MANDANI', 'BAY', 'DRAGON', 'BOAT', 'REGATTA', '2022', 'Nakadaog', 'ang', 'Dumaguete', 'Yellow', 'Fins', 'sa', 'upat', 'ka', 'kategorya', 'atol', 'sa', 'Mandani', 'Bay', 'Dragon', 'Board', 'Regatta', '2022', 'sa', 'Mandaue', 'City', 'niadtong', 'Agosto', '20', ',', '2022.', 'Nibisita', 'ang', 'grupo', 'ngadto', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo', 'aron', 'mopadayag', 'sa', 'ilang', 'pagpasalamat', 'sa', 'suporta', 'nga', 'ilang', 'nakuha.', 'Gidayeg', 'sab', 'ni', 'Mayor', 'Remollo', 'ang', 'Dumaguete', 'Yellow', 'Fins', 'alang', 'sa', 'ilang', 'kadaogan', 'sa', 'mosunod', 'nga', 'mga', 'kategorya', ':', 'Minor', 'Plate', 'Champion', ':', 'Open', '200', 'meter', 'Small', 'Boat', 'Minor', 'Plate', 'Champion', ':', 'Mixed', 'Masters', '200', 'Small', 'Boat', '1st', 'Runner-up', ':', 'Open', '200', 'meter', 'Small', 'Boat', '2nd', 'Runner-up', ':', 'Open', 'Juniors', '200', 'meter', 'Small', 'Boat', 'Ang', 'gipahigayon', 'nga', '1st', 'Dragon', 'Boat', 'Race', 'sa', 'Mandaue', ',', 'giapilan', 'sa', 'nagkalain-laing', 'mga', 'grupo', 'sa', 'Mandani', 'Bay', ',', '"', 'a', 'world-class', '20-hectare', 'waterfront', 'development', 'with', 'a', 'stunning', 'view', 'of', 'the', 'coast', 'and', 'encompassing', 'cityscape', '.', '"'] 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, 4, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 5, 6, 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, 1, 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, 7, 8, 8, 0, 5, 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] | cebuaner |
4,627 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SUGBO', 'URBAN', 'TRANSIT', 'INC.', ',', 'NAGSUGOD', 'NA', 'SA', 'ILANG', 'CEBU', 'CITY-SIQUIJOR', 'ISLAND', 'TRIP', 'Niabot', 'na', 'ang', 'Sugbo', 'Urban', 'Transit', 'Inc.', '(', 'SUTI', ')', 'sa', 'Larena', ';', 'pagsugod', 'sa', 'una', 'niini', 'nga', 'Cebu', 'City-Siquijor', 'Island', 'pinaagi', 'sa', 'rota', 'sa', 'Liloan', 'Port', 'Santander', 'ug', 'Larena', 'Port', 'ug', 'vice', 'versa.', 'Nipadayag', 'sa', 'ilang', 'pagpasalamat', 'sa', 'SUTI', 'ang', 'munisipyo', 'sa', 'Larena', 'tungod', 'andam', 'na', 'nga', 'moalagad', 'sa', 'mga', 'Larenians', 'ug', 'tanang', 'biyahero', 'ang', 'isla', 'sa', 'Siquijor', 'kinsa', 'mo-avail', 'sa', 'serbisyo', 'sa', 'Maayo', 'Shipping', ',', 'Inc.', 'sugod', 'karong', 'adlawa', ',', 'Setyembre', '1', ',', '2022.', 'Kini', 'naamgohan', 'pagkahuman', 'sa', 'lima', 'ka', 'bulan', 'human', 'ni-request', 'ang', 'Bacolod-based', 'bus', 'company', 'sa', 'suporta', 'sa', 'Larena', 'alang', 'sa', 'paggamit', 'sa', 'loading', 'ug', 'unloading', 'sa', 'pantalan', 'sa', 'Larena', 'paingon', 'sa', 'terminal', 'sa', 'tanan', 'nga', 'SUTI', 'bus', 'units.', 'Niadtong', 'Mayo', '11', ',', '2022', ',', 'usa', 'ka', 'resolusyon', 'ang', 'gipasaka', 'sa', 'Office', 'of', 'the', 'Sangguniang', 'Bayan', 'nga', 'nagsuporta', 'sa', 'request', 'sa', 'Sugbo', 'Transit', ',', 'Inc.', 'nga', 'magpadagan', 'sa', 'mga', 'public', 'utility', 'buses', 'sa', 'Larena.', 'Nagpadayag', 'sab', 'sa', 'iyang', 'kalipay', 'si', 'Mayor', 'Cyrus', 'Vincent', 'Calibo', 'sa', 'dugang', 'kasayon', 'alang', 'sa', 'mga', 'biyahero', 'nga', 'moadto', 'sa', 'isla', 'sa', 'Siquijor', 'ug', 'kadtong', 'mga', 'moadto', 'sa', 'Cebu', 'City', 'pinaagi', 'sa', 'pantalan', 'sa', 'Larena', '.'] 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, 4, 4, 0, 0, 0, 0, 0, 5, 6, 5, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 5, 0, 0, 0, 0, 0, 0, 5, 6, 5, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 6, 6, 6, 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, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 5, 0] | cebuaner |
4,628 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BAG-ONG', 'PANGANAK', 'NGA', 'BATANG', 'BABAYE', ',', 'NAKIT-AN', 'SULOD', 'SA', 'SAKO', 'SA', 'LOOC', ',', 'DGTE', 'Usa', 'ka', 'bag-ong', 'panganak', 'nga', 'batang', 'babaye', 'ang', 'nakit-an', 'sulod', 'sa', 'sako', 'nga', 'gibilin', 'sa', 'eskina', 'sa', 'Barangay', 'Looc', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'sa', 'buntag', 'niadtong', 'Agosto', '29', ',', '2022.', 'Gidala', 'ni', 'Barangay', 'Captain', 'Angelita', 'Ragay', 'ang', 'masuso', 'ngadto', 'sa', 'Dumaguete', 'City', 'Health', 'Office.', 'Sumala', 'pa', 'ni', 'Flor', 'Angel', 'Cornelia', ',', 'officer-in-charge', 'sa', 'Dumaguete', 'Birthing', 'Center', ',', 'kusog', 'kaayo', 'ang', 'hilak', 'sa', 'batang', 'babaye', 'nga', 'nakakuha', 'sa', 'atensyon', 'sa', 'pipila', 'ka', 'mga', 'residente.', 'Gisusi', 'sa', 'mga', 'nabalaka', 'nga', 'residente', 'ang', 'lugar', 'ug', 'nakit-an', 'ang', 'masuso', 'sulod', 'sa', 'sako.', 'Nagtuo', 'si', 'Cornelia', 'nga', 'bag-ong', 'panganak', 'pa', 'ang', 'batang', 'babaye', 'tungod', 'aduna', 'pa'y', 'mga', 'lama', 'sa', 'dugo', 'sa', 'dalunggan', 'niini', 'ug', 'ubang', 'parte', 'sa', 'iyang', 'lawas.', 'Anaa', 'sa', '2,400', 'gramos', 'ang', 'gibug-aton', 'sa', 'bata', 'ug', 'gikonsiderar', 'nga', 'premature.', 'Magpabilin', 'sa', 'ilang', 'kustodiya', 'ang', 'masuso', 'samtang', 'nagpahigayon', 'pa', 'og', 'imbestigasyon', 'ang', 'mga', 'awtoridad', '.'] 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, 6, 6, 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, 1, 2, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 1, 2, 2, 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, 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] | cebuaner |
4,629 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['QUALIFIED', 'SENIOR', 'CITIZENS', 'SA', 'DGTE', ',', 'NAKADAWAT', 'NA', 'SA', 'ILANG', 'SOCIAL', 'PENSIONS', 'Nakadawat', 'na', 'sa', 'ilang', 'quarterly', 'pension', 'nga', 'P1,500', 'ang', 'mga', 'kwalipikadong', 'senior', 'citizens', 'sa', 'nagkalain-laing', 'barangay', 'gikan', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', 'Office', 'Region', '7.', 'Gi-organisar', 'kini', 'sa', 'mga', 'kawani', 'gikan', 'sa', 'opisina', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo', ',', 'Office', 'of', 'the', 'Senior', 'Citizens', 'Affairs', '(', 'OSCA', ')', 'ug', 'City', 'Social', 'Welfare', 'and', 'Development', 'Office', 'aron', 'ma-ila', 'ang', 'mga', 'eligible', 'seniors', 'ug', 'mahatod', 'gikan', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'barangay.', 'Gipatuman', 'sab', 'ang', 'health', 'protocols', 'atol', 'sa', 'maong', 'distribusyon', '.'] 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, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4, 4, 4, 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] | cebuaner |
4,630 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nisulod', 'na', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'karong', 'gabii', '(', 'Aug.', '31', ',', '2022', ')', 'ang', 'Super', 'Typhoon', '"', 'Hinnamnor.', '"', 'Tungod', 'niini', ',', 'gihatagan', 'na', 'kini', 'sa', 'PAGASA', 'og', 'local', 'name', 'nga', '#', 'HenryPH.', 'Mga', '5:30', 'sa', 'hapon', 'karong', 'Miyerkules', 'nisulod', 'sa', 'PAR', 'ang', 'naasoy', 'nga', 'kusog', 'nga', 'bagyo.', 'Magpaluwat', 'na', 'og', 'mga', 'abiso', 'ang', 'PAGASA', 'bahin', 'sa', 'maong', 'bagyo', 'sugod', 'karong', 'gabii', '.'] 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, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,631 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niulbo', 'ang', 'sunog', 'sulod', 'mismo', 'sa', 'West', 'City', 'Elementary', 'School', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'hapon', ',', 'Agosto', '31', ',', '2022.', 'Sa', 'usa', 'ka', 'classroom', 'sa', 'likod', 'nga', 'bahin', 'sa', 'maong', 'eskwelahan', 'gikatahong', 'niulbo', 'ang', 'maong', 'sunog.', 'Padayon', 'pa', 'kining', 'gipalong', 'sa', 'kabumberohan', '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, 5, 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, 0, 0, 0, 0, 0] | cebuaner |
4,632 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAGSUL-OB', 'OG', 'FACE', 'MASK', ',', 'DILI', 'NA', 'MANDATORY', 'SA', 'CEBU', 'CITY', 'Gipirmahan', 'ni', 'Cebu', 'City', 'Mayor', 'Michael', 'Rama', 'karong', 'adlawa', 'ang', 'executive', 'order', 'nga', 'dili', 'na', 'mandatory', 'ang', 'pagsul-ob', 'og', 'face', 'mask', 'sa', 'gawas', 'ug', 'open', 'spaces', 'taliwala', 'sa', 'pandemya', 'sa', 'Covid-19.', 'Base', 'sa', 'Executive', 'Order', 'No.', '5', ',', 'gideklara', 'nga', '"', 'non-obligatory', '"', 'ang', 'pagsul-ob', 'og', 'face', 'mask', 'sulod', 'sa', 'territorial', 'jurisdiction', 'sa', 'Cebu', 'City.', 'Hinuon', ',', 'mahimo', 'gihapon', 'nga', 'mogamit', 'niini', 'isip', '"', 'self', 'preservation', 'and', 'protection', 'under', 'the', 'principle', 'of', 'shared', 'responsibility', 'and', 'mutual', 'respect.”', 'Sumala', 'pa', 'sa', 'EO', '5', ',', 'oras', 'na', 'sab', 'nga', 'sayunon', 'ug', 'ideklara', 'ang', 'maong', 'palisiya', 'ug', 'gikutlo', 'nga', 'nagkahinay', 'na', 'og', 'kahupa', 'ang', '"', 'lethal', 'effect', '"', 'sa', 'pandemya', 'ug', 'napamatud-an', 'nga', 'epektibo', 'ang', 'pagpabakuna', 'aron', 'mapunggan', 'ang', 'pagkuyanap', '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, 0, 0, 0, 0, 5, 6, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 8, 8, 8, 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
4,633 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-share', 'sa', 'NASA', 'ang', 'mga', 'hulagway', 'sa', 'Phantom', 'galaxy', ',', 'usa', 'ka', 'grand-design', 'spiral', 'galaxy', 'mga', '32', 'million', 'ka', 'light-years', 'ang', 'gilay-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, 3, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,634 | 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', '5', 'KA', 'BAG-ONG', 'KASO', 'SA', 'COVID-19', ';', '2', 'PASYENTE', ',', 'NAAYO', 'NA', 'Lima', 'ka', 'bag-ong', 'kaso', 'sa', 'Covid-19', 'ang', 'natala', 'sa', 'dakbayan', 'sa', 'Dumaguete', ',', 'samtang', 'duha', 'sab', 'ka', 'pasyente', 'ang', 'naayo', 'ug', 'nakagawas', 'na', 'sa', 'isolation.', 'Mao', 'kini', 'ang', 'gikompirma', 'ni', 'City', 'Health', 'Officer', 'Dr.', 'Sarah', 'B.', 'Talla', 'kagahapong', 'adlawa', ',', 'Agosto', '30', ',', '2022.', 'Ang', 'lima', 'ka', 'bag-ong', 'kaso', ',', 'mga', 'residente', 'sa', 'Barangay', 'Bagacay', ',', 'Batinguel', ',', 'Daro', 'ug', 'Junob.', 'Ang', 'upat', 'sa', 'lima', 'ka', 'bag-ong', 'kaso', ',', 'anaa', 'sa', 'ospital', 'apil', 'na', 'ang', 'usa', 'ka', '7-anyos', 'nga', 'bata.', 'Samtang', 'ang', 'duha', 'ka', 'nangaayo', 'nga', 'pasyente', ',', 'mga', 'residente', 'sa', 'Barangay', 'Daro', 'ug', 'Poblacion', '8.', 'Sa', 'pagkakaron', ',', 'aduna'y', '14', 'ka', 'aktibong', 'kaso', 'sa', 'Covid-19', 'ang', 'Dumaguete.', 'Anaa', 'na', 'sab', 'sa', '5,906', 'nangaayo', 'ug', '168', 'ang', 'namatay', 'sukad', 'naigo', 'sa', 'pandemya', 'ang', 'dakbayan', 'gikan', 'Enero', '30', ',', '2020', 'hangtod', 'Agosto', '30', ',', '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. | [5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 6, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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] | cebuaner |
4,635 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DIREKTANG', 'BIYAHE', 'GIKAN', 'CEBU', 'CITY', 'NGADTO', 'SA', 'SIQUIJOR', ',', 'BUKSAN', 'NA', 'Gianunsyo', 'sa', 'SUGBO', 'URBAN', 'TRANSIT', 'INC.', 'nga', 'gidumala', 'sa', 'VTI', '-', 'Cebu', 'South', 'Branch', '(', 'CERES', ')', 'ang', 'bag-ong', 'rota', 'gikan', 'Cebu', 'City', 'paingon', 'sa', 'Siquijor', 'Island.', 'Magsugod', 'kini', 'sa', 'Miyerkules', ',', 'Agosto', '31', ',', '2022', 'mga', 'alas-8', 'sa', 'gabie', 'sa', 'Cebu', 'South', 'Bus', 'Terminal.', 'Tumong', 'niini', 'nga', 'mahatagan', 'ang', 'mga', 'Siquijodnon', 'og', 'mas', 'maayong', 'paggamit', 'sa', 'transportasyon', '.'] 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, 5, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 5, 6, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,636 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', ',', 'Agosto', '30', ',', '2022', ',', 'mao', 'ang', '#', 'NationalPressFreedomDay.', 'Gideklara', 'ang', 'Agosto', '30', 'matag', 'tuig', 'nga', 'National', 'Press', 'Freedom', 'Day', 'sa', 'Pilipinas', 'ilalom', 'sa', 'Republic', 'Act', '11699', ',', 'aron', 'pagpasidungog', 'ni', 'Marcelo', 'H.', 'del', 'Pilar', ',', 'kinsa', 'mao'y', 'amahan', 'sa', 'Philippine', 'Journalism', 'ug', 'gipanganak', 'niadtong', 'Agosto', '30', ',', '1850', '.'] 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, 7, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 5, 0, 0, 7, 8, 8, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,637 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CHED', ':', 'MGA', 'ESTUDYANTENG', 'DILI', 'BAKUNADO', ',', 'PUWEDE', 'NANG', 'MAKAAPIL', 'SA', 'F2F', 'CLASSES', 'Tugutan', 'na', 'ang', 'tertiary', 'students', ',', 'faculty', 'members', 'ug', 'uban', 'pang', 'staff', 'sa', 'higher', 'education', 'institutions', '(', 'HEIs', ')', 'nga', 'moapil', 'sa', 'face-to-face', 'classes', 'bisan', 'pa', 'sa', 'ilang', 'Covid-19', 'vaccination', 'status.', 'Mao', 'kini', 'ang', 'gi-anunsyo', 'sa', 'Commission', 'on', 'Higher', 'Education', '(', 'CHEd', ')', 'niadtong', 'Lunes', ',', 'Agosto', '29', ',', '2022.', 'Sumala', 'pa', 'ni', 'CHEd', 'Chairman', 'J.', 'Prospero', '"', 'Popoy', '"', 'de', 'Vera', ',', 'ang', 'pagtangtang', 'nila', 'sa', 'vaccination', 'requirement', 'nakabase', 'sa', 'mga', 'uso', 'nga', 'na-obserbahan', 'sa', 'ilang', 'komisyon', 'sugod', 'pa', 'niadtong', 'Nobyembre', '2021', 'ug', 'tambag', 'sa', 'mga', 'health', 'experts', 'ug', 'consultants.', 'Gitaho', 'sa', 'CHEd', 'nga', '3.1', 'milyon', 'sa', '4.09', 'milyon', 'ka', 'mga', 'estudyante', 'sa', 'HEIs', 'ang', 'partially', 'o', 'fully', 'vaccinated', 'batok', 'Covid-19', ',', 'samtang', '90', '%', 'o', '260,000', 'sa', '289,000', 'ka', 'mga', 'kawani', 'ang', 'nabakunahan.', 'Gisuportaan', 'sab', 'ni', 'University', 'of', 'the', 'Philippines-Philippine', 'General', 'Hospital', '(', 'UP-PGH', ')', 'Director', 'Dr.', 'Gerardo', 'Legaspi', 'ang', 'pagtangtang', 'sa', 'vaccination', 'isip', 'requirement', 'alang', 'sa', 'in-person', 'classes', ',', 'ug', 'gisubli', 'nga', 'ang', 'mga', 'palisiya', 'dili', 'dapat', 'discriminatory', 'o', 'persecutory.', 'Bisan', 'paman', ',', 'tumong', 'gihapon', 'sa', 'CHEd', 'nga', 'makahatag', 'og', 'vaccination', 'ug', 'booster', 'doses', 'alang', 'sa', 'mga', 'estudyante', 'ug', 'HEIs', 'personnel.', 'Gipanan-aw', 'sab', 'nila', 'nga', 'mopahigayon', 'pag-usab', 'og', 'campus-based', 'vaccination', 'sa', 'koordinasyon', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 4, 4, 4, 4, 4, 0] | cebuaner |
4,638 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sakit', 'ang', 'mawad-an', 'og', 'mahal', 'sa', 'kinabuhi.', 'Karong', 'Grief', 'Awareness', 'Day', ',', 'atong', 'tabangan', 'nga', 'mapagaan', 'ang', 'kasingkasing', 'sa', 'mga', 'tawong', 'nakasinati', 'og', 'kaguol', '.'] 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, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,639 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DSWD', 'GIPANAN-AW', 'ANG', 'PAG-EXTEND', 'SA', 'EDUCATION', 'AID', 'PAYOUT', ',', 'HOUSE-TO-HOUSE', 'DISTRIBUTION', 'Gipanan-aw', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', 'nga', 'palugwayan', 'ang', 'pag-apod-apod', 'sa', 'cash', 'assistance', 'ngadto', 'sa', 'mga', 'kwalipikado', 'nga', 'estudyante', 'ingon', 'man', 'ang', 'house-to-house', 'distribution.', 'Sumala', 'pa', 'sa', 'usa', 'ka', 'opisyal', 'karong', 'Lunes', ',', 'Agosto', '29', ',', '2022.', 'Matod', 'ni', 'DSWD', 'Assistant', 'Secretary', 'Romel', 'Lopez', ',', 'nigahin', 'ang', 'ahensya', 'og', 'P1.5', 'bilyon', 'alang', 'sa', 'maong', 'programa', 'ug', 'iapod-apod', 'ang', 'cash', 'aid', 'sa', 'mosunod', 'nga', 'upat', 'ka', 'Sabado', 'o', 'hangtod', 'sa', 'Setyembre', '24.', 'Sulayan', 'nila', 'ang', 'paghatod', 'sa', 'kwarta', 'karong', 'semanaha', 'apan', 'sa', 'pinili', 'lamang', 'nga', 'mga', 'rehiyon', 'tungod', 'responsibilidad', 'sa', 'matag', 'regional', 'office', 'ang', 'pagpalambo', 'sa', 'ilang', 'pamaagi', 'sa', 'pag-apod-apod', 'sa', 'ayuda.', 'Gisubli', 'ni', 'Lopez', 'nga', 'gitakda', 'nga', 'magkita', 'pag-usab', 'ang', 'DSWD', 'committee', 'aron', 'ma-', '"', 'fine', 'tune', '"', 'ang', 'distribution', 'guidelines', ',', 'apil', 'na', 'ang', 'pagpalapad', 'ug', 'pagpalugway', 'sa', 'distribusyon.', 'Gisubli', 'sab', 'niya', 'nga', 'gipanan-aw', 'sa', 'DSWD', 'ang', 'house-to-house', 'aid', 'distribution', 'ilabi', 'na', 'sa', 'mga', 'dili', 'makagamit', 'og', 'internet', 'ug', 'dili', 'maka-register', 'online.', 'Human', 'sa', 'kasikas', 'niadtong', 'Agosto', '20', 'kung', 'diin', 'gidumog', 'sa', 'katawhan', 'ang', 'mga', 'opisina', 'sa', 'DSWD', ',', 'gi-awhag', 'nila', 'ang', 'publiko', 'nga', 'dili', 'mo', 'walk-ins', 'ug', 'tagdon', 'lamang', 'kadtong', 'mga', 'naka-register', 'online', 'ug', 'aduna'y', 'text', 'confirmation', '.'] 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, 7, 8, 8, 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, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,640 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuhanan', 'og', 'video', 'ni', 'Dywin', 'Tac-an', 'Lou', 'ang', 'bayanihan', 'sa', 'pag-aswat', 'og', 'usa', 'ka', 'bahay-kubo', 'sa', 'Barangay', 'Bito-on', ',', 'Clarin', ',', 'Misamis', 'Occidental.', 'Sumala', 'pa', 'ni', 'Lou', ',', 'igo', 'lamang', 'siya', 'nga', 'nibisita', 'sa', 'maong', 'lugar', 'sa', 'dihang', 'nakasaksi', 'siya', 'sa', 'bayanihan', 'sa', 'mga', 'residente.', 'Dugang', 'pa', 'niya', ',', 'gibalhin', 'ang', 'maong', 'bahay-kubo', 'aron', 'himuon', 'nga', 'bilyaran', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 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] | cebuaner |
4,641 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', ',', 'Agosto', '29', ',', '2022', ',', 'atong', 'gisaulog', 'ang', 'National', 'Heroes', 'Day', 'kon', 'Araw', 'ng', 'mga', 'Bayani', 'aron', 'hatagan', 'og', 'pagdayig', 'ug', 'pasidungog', 'ang', 'kaisog', 'sa', 'mga', 'nakigbisog', 'alang', 'sa', 'kagawsan', 'sa', 'nasud.', 'Usa', 'ka', 'dakong', 'pagsaludo', 'alang', 'sa', 'tanan', 'nga', 'mga', 'Bayaning', 'Pilipino', '!'] 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, 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, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
4,642 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAMILYA', 'NI', 'DAUIN', 'MAYOR', 'GALIC', 'TRUITA', ',', 'GI-HOSTAGE', 'SULOD', 'MISMO', 'SA', 'ILANG', 'BALAY', 'APAN', 'LUWAS', 'NA', 'Gi-hostage', 'ang', 'asawa', 'ug', 'mga', 'anak', 'ni', 'Dauin', 'Mayor', 'Galicano', 'Truita', 'sulod', 'mismo', 'sa', 'ilang', 'balay', 'sa', 'Barangay', 'Masaplod', 'sa', 'lungsod', 'Dauin', 'mga', 'alas-9', 'sa', 'buntag', 'sa', 'Agosto', '27', ',', '2022.', 'Ang', 'suspek', ':', 'kaugalingon', 'nilang', 'driver.', 'Mao', 'kini', 'ang', 'gikompirma', 'sa', 'Negros', 'Oriental', 'Provincial', 'Police', 'Office.', 'Giila', 'ni', 'Provincial', 'Director', 'P', '/', 'Col.', 'Jonathan', 'Pineda', 'ang', 'hostage', 'taker', 'nga', 'si', 'Venirando', 'Dalope', 'kinsa', 'driver', 'sa', 'mga', 'biktima.', 'Nakadawat', 'og', 'tawag', 'ang', 'kapulisan', 'gikan', 'sa', 'mayor', 'bahin', 'sa', 'maong', 'insidente', 'ug', 'diha-diha', 'dayon', 'sila', 'nga', 'nagpadala', 'og', 'negotiation', 'team', 'aron', 'mahisgutan', 'ang', 'mga', 'butang', 'sa', 'suspek.', 'Kasamtangan', 'anaa', 'sa', 'Manila', 'si', 'Mayor', 'Truita', 'alang', 'sa', 'usa', 'ka', 'opisyal', 'nga', 'byahe.', 'Nilungtad', 'og', 'kapin', 'walo', 'ka', 'oras', 'ang', 'negotiation', 'sa', 'hostage', 'taker.', 'Sa', 'pagsulat', 'niining', 'balita', ',', 'wala', 'sab', 'gibutyag', 'sa', 'kapulisan', 'kung', 'unsa', 'ang', 'motibo', 'sa', 'suspek', 'bahin', 'sa', 'maong', 'insidente', 'sa', 'hostage', 'taking.', 'Ipailalom', 'og', 'psychological', 'debriefing', 'ang', 'mga', 'biktima', 'samtang', 'anaa', 'na', 'sa', 'kustodiya', 'sa', 'kapulisan', '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, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 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, 0, 0, 0, 0, 0, 0, 1, 2, 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, 5, 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] | cebuaner |
4,643 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Taas-taas', 'ang', 'atong', 'umalabot', 'nga', 'weekend', 'tungod', 'kay', 'usa', 'ka', 'regular', 'holiday', 'karong', 'Lunes', ',', 'Agosto', '29', ',', '2022', ',', 'isip', 'pagsaulog', 'sa', 'National', 'Heroes', ''', 'Day.', 'Ang', 'National', 'Heroes', 'Day', 'usa', 'ka', '"', 'movable', 'holiday', ';', '"', 'buot', 'ipasabot', 'niini', ',', 'mairog', 'kini', 'sa', 'laing', 'petsa', 'kada', 'tuig.', 'Kini', 'gisaulog', 'sa', 'katapusang', 'Lunes', 'sa', 'bulan', 'sa', 'Agosto', '.'] 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, 7, 8, 8, 8, 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] | cebuaner |
4,644 | 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', 'mga', 'venue', 'diin', 'ipanghatag', 'ang', 'educational', 'cash', 'assistance', 'sa', 'DSWD', 'ugmang', 'adlawa', ',', 'Agosto', '27', ',', '2022.', 'Makadawat', 'og', '₱1,000', 'nga', 'ayuda', 'ang', 'nga', 'estudyante', 'sa', 'elementary', 'school', ',', '₱2,000', 'alang', 'sa', 'junior', 'high', 'school', ',', '₱3,000', 'alang', 'sa', 'senior', 'high', 'school', ',', 'ug', '₱4,000', 'alang', 'sa', 'mga', 'tinun-an', 'sa', 'college', 'ug', 'vocational', 'studies', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,645 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ESTUDYANTE', 'SA', 'BUNTAG', ',', 'GRAB', 'DRIVER', 'SA', 'GABIE.', 'Nakakuha', 'og', 'mga', 'pagdayig', 'ang', 'istorya', 'sa', 'usa', 'ka', '52-anyos', 'nga', 'lalake', 'human', 'sa', 'unang', 'adlaw', 'niya', 'isip', 'usa', 'ka', 'Grade', '12', 'nga', 'estudyante.', 'Usa', 'ka', 'estudyante', 'sa', 'buntag', 'si', 'Benjie', 'Estillore', 'ug', 'nagtrabaho', 'siya', 'isip', 'usa', 'ka', 'Grab', 'driver', 'sa', 'gabie.', 'Sumala', 'pa', 'ni', 'Benjie', ',', 'pangandoy', 'gyud', 'niya', 'nga', 'makahuman', 'og', 'skwela', 'tungod', 'makita', 'niya', 'ang', 'importansiya', 'sa', 'edukasyon', '.'] 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, 1, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,646 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAODNON', 'NGA', 'NAGTINGUHA', 'SA', 'PAGBALIK', 'PAG-USAB', 'SA', 'DEATH', 'PENALTY', ',', 'GIDUSO', 'Giduso', 'pag-usab', 'ni', 'Surigao', 'del', 'Norte', 'Rep.', 'Robert', 'Ace', 'Barbers', 'ang', 'pagpasig-uli', 'sa', 'death', 'penalty', 'isip', 'pagpugong', 'batok', 'sa', 'mga', 'makalilisang', 'nga', 'krimen.', 'Gusto', 'sab', 'ni', 'Barbers', ',', 'kinsa', 'nangulo', 'sa', 'House', 'committee', 'sa', 'dangerous', 'drugs', ',', 'nga', 'maapil', 'ang', 'pluder', 'sa', 'listahan', 'sa', 'heinous', 'crimes', 'nga', 'mahimong', 'silotan', 'sa', 'kamatayon.', 'Niadtong', 'Hulyo', '7', ',', 'gipasaka', 'ni', 'Barbers', 'ang', 'House', 'Bill', '1543', 'nga', 'nagtinguha', 'nga', 'ibalik', 'ang', 'capital', 'punishment', 'ug', 'gikutlo', 'ang', 'pagtaas', 'sa', 'mga', 'maka-alarma', 'nga', 'krimen', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,647 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagsugod', 'na', 'ang', 'mga', 'kawani', 'ug', 'estudyante', 'sa', 'Silliman', 'University', 'sa', 'pagtukod', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'booth', 'alang', 'sa', 'Hibalag', 'Booth', 'Festival.', 'Mao', 'kini', 'ang', 'kinaunahang', 'higayon', 'nga', 'ipahigayon', 'pag-usab', 'ang', 'in-person', 'nga', 'tradisyon', 'sa', 'Hibalag', 'human', 'ang', 'duha', 'ka', 'tuig', 'nga', 'gipahigayon', 'kini', 'online', 'tungod', 'sa', 'pandemya', 'sa', '#', 'COVID19', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 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, 7, 0] | cebuaner |
4,648 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['5-ANYOS', 'NGA', 'ESTUDYANTENG', 'NAWALA', ',', 'NAA', 'RA', 'DIAY', 'SA', 'LAING', 'CLASSROOM', 'Nalisang', 'ang', 'mga', 'residente', 'sa', 'Talisay', ',', 'Batangas', 'human', 'ma-report', 'nga', 'aduna'y', 'usa', 'ka', '5-anyos', 'nga', 'batang', 'lalaki', 'ang', 'nawala', 'human', 'kini', 'ihatod', 'sa', 'iyang', 'amahan', 'sa', 'Tranca', 'Elementary', 'School.', 'Sumala', 'pa', 'ni', 'Jessie', 'Aala', ',', 'alas-6', 'sa', 'buntag', 'niya', 'gihatod', 'ang', 'iyang', 'anak', 'nga', 'kinder', 'sa', 'gate', 'sa', 'giskwelahan', 'niini.', 'Apan', 'niadtong', 'iya', 'na', 'kining', 'kuhaon', 'human', 'sa', 'klase', 'mga', 'alas-10', 'sa', 'buntag', ',', 'wala', 'na', 'niya', 'nakit-an', 'ang', 'iyang', 'anak.', 'Nag-panic', 'sab', 'si', 'Aala', 'sa', 'dihang', 'niingon', 'ang', 'maestra', 'niini', 'nga', 'wala', 'ang', 'iyang', 'anak', 'sa', 'tibuok', 'oras', 'sa', 'klase.', 'Diha-diha', 'dayon', 'nga', 'nag-post', 'sa', 'Facebook', 'si', 'Aala', 'sa', 'hulagway', 'sa', 'iyang', 'anak', 'ug', 'nagtinabangay', 'na', 'sab', 'sa', 'pagbulong', 'ang', 'mga', 'pulis', ',', 'mga', 'opisyal', 'sa', 'barangay', 'ug', 'mga', 'residente.', 'Gibulong', 'nila', 'kini', 'sa', 'tibuok', 'eskwelahan', 'ug', 'gisubay', 'ang', 'dalan', 'papauli', 'sa', 'ilang', 'balay', 'apan', 'napakyas', 'gihapon', 'sila', 'nga', 'makita', 'ang', 'maong', 'bata.', 'Apan', 'paglabay', 'sa', 'unom', 'ka', 'oras', ',', 'usa', 'ka', 'bata', 'ang', 'namatikdan', 'sa', 'usa', 'ka', 'magtutudlo', 'nga', 'nabilin', 'sa', 'kwarto', 'sa', 'Grade', '2', 'nga', 'anaa', 'sa', 'second', 'floor', 'ug', 'nahibal-an', 'nga', 'mao', 'kini', 'ang', 'batang', 'nawala.', 'Sumala', 'pa', 'sa', 'bata', ',', 'anaa', 'lamang', 'siya', 'sulod', 'sa', 'maong', 'classroom', 'sulod', 'sa', 'tibuok', 'adlaw.', 'Natingala', 'sab', 'ang', 'amahan', 'niini', 'nganong', 'napunta', 'ang', 'bata', 'sa', 'second', 'floor', 'nga', 'anaa', 'lamang', 'unta', 'ang', 'classroom', 'sa', 'kinder', 'sa', 'first', 'floor.', 'Gatuo', 'sila', 'nga', 'na-engkanto', 'ang', 'iyang', 'anak.', 'Bisan', 'paman', ',', 'dako', 'ang', 'pasalamat', 'sa', 'amahan', 'nga', 'nakit-an', 'ang', 'iyang', 'anak', 'ug', 'wala'y', 'daotan', 'nga', 'nahitabo', 'niini.', '#', 'NewsBite', '|', 'with', 'reports', 'from', 'ABS-CBN', 'News'] 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, 6, 6, 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, 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, 0, 0, 0, 0, 0, 7, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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] | cebuaner |
4,649 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Inay', 'manakop', ',', 'nanghatag', 'ang', 'kapulisan', 'og', 'helmet', 'ngadto', 'sa', 'mga', 'motorista', 'sa', 'National', 'Highway', ',', 'South', 'Road', ',', 'Barangay', 'Masaplod', ',', 'Dauin', 'niadtong', 'buntag', 'sa', 'Martes', ',', 'Agosto', '23', ',', '2022.', 'Nagtinabangay', 'sa', 'paghatag', 'ang', 'Regional', 'Highway', 'Patrol', 'Unit', '7', ',', 'Provincial', 'Highway', 'Patrol', 'Team', 'Negros', 'Oriental', ',', 'uban', 'sa', 'augmented', 'personnel', 'gikan', 'sa', '1st', 'ug', '2nd', 'Provincial', 'Mobile', 'Force', 'Company.', 'Nangulo', 'sab', 'sa', 'maong', 'kalihukan', 'si', 'PLTCOL', 'Manuel', 'Ana', ',', 'Regional', 'Chief', 'sa', 'RHPU7', 'ug', 'PCMS', 'Aurelio', 'Bodo', ',', 'OIC', 'PNCO', 'sa', 'PHPT', 'Negros', 'Oriental.', 'Tumong', 'sa', 'maong', 'kalihukan', 'nga', 'mapalambo', 'ang', 'road', 'safety', 'awareness', 'sa', 'tanang', 'mga', 'motorista', 'ug', 'mapahibalo', 'ang', 'importansiya', 'sa', 'RA', '10054', 'o', 'Motorcycle', 'Helmet', 'Act', 'of', '2009', '.'] 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, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 3, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 8, 8, 8, 0] | cebuaner |
4,650 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'mga', 'estudyante', 'ang', 'nagsuot', 'og', 'shoe', 'coats', 'o', 'footwear', 'aron', 'magpabiling', 'luwas', 'ug', 'uga', 'ang', 'mga', 'classrooms', 'sa', 'Concepcion', 'Elementary', 'School', 'sa', 'Marikina', 'City', 'atol', 'sa', 'unang', 'adlaw', 'sa', 'face-to-face', 'classes', 'niadtong', 'Lunes', ',', 'August', '22', ',', '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, 5, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,651 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'kakulang', 'sa', 'mga', 'learning', 'facilities', ',', 'usa', 'ka', 'public', 'school', 'teacher', 'sa', 'Tanjay', 'City', ',', 'Negros', 'Oriental', 'ang', 'gihimo', 'ang', 'usa', 'ka', 'stockroom', 'ngadto', 'sa', 'usa', 'ka', 'classroom', 'gamit', 'ang', 'iyang', 'kaugalingong', 'kwarta.', 'Sumala', 'pa', 'ni', 'Thessalyn', 'Samson', ',', 'usa', 'ka', 'grade', 'school', 'teacher', ',', 'usa', 'kini', 'ka', 'mahagiton', 'nga', 'trabaho', 'sa', 'sugod', 'apan', 'malipayon', 'siya', 'nga', 'kini', 'natuman', 'ug', 'nahuman.', 'Gawas', 'sa', 'pagkamaestra', ',', 'usa', 'sab', 'ka', 'home', 'baker', 'si', 'Samson.', 'Gi-welcome', 'niya', 'ang', 'iyang', 'mga', 'estudyante', 'pinaagi', 'sa', 'paghatag', 'og', 'sweet', 'cake', 'treats', 'sa', 'unang', 'adlaw', 'sa', 'ilang', 'face-to-face', 'classes', '.'] 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, 6, 6, 6, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,652 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'PNP', ',', 'GIIMBESTIGARAN', 'NA', 'ANG', 'MGA', 'TAHO', 'SA', 'GIINGONG', 'KIDNAPPING', 'NGA', 'NAG-VIRAL', 'ONLINE', 'Gitan-aw', 'ug', 'gitun-an', 'na', 'karon', 'sa', 'kapulisan', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'ang', 'mga', 'taho', 'sa', 'giingong', 'kidnapping', 'nga', 'unang', 'nag-viral', 'sa', 'social', 'media.', 'Sa', 'usa', 'ka', 'press', 'conference', 'karong', 'Martes', ',', 'Agosto', '23', ',', '2022', ',', 'gibutyag', 'ni', 'Dumaguete', 'City', 'Police', 'Chief', 'PLt.', 'Col.', 'Joeson', 'Parallag', 'nga', 'duna', 'na'y', 'special', 'unit', 'sa', 'kapulisan', 'nga', 'gitahasang', 'mag-imbestigar', 'pag-ayo', 'sa', 'maong', 'mga', 'taho.', 'Sa', 'niaging', 'mga', 'adlaw', ',', 'dunay', 'nitumaw', 'nga', 'posts', 'online', 'nunot', 'sa', 'mga', 'giingong', 'pagdagit', 'sa', 'pipila', 'ka', 'mga', 'lugar', 'sa', 'Dumaguete.', 'Giawhag', 'ni', 'Parallag', 'ang', 'publiko', 'nga', 'magmatngon', 'ug', 'mag-amping', 'sa', 'palibot', ',', 'ug', 'dili', 'magduhaduha', 'nga', 'i-report', 'ngadto', 'sa', 'kapulisan', 'kung', 'duna', 'man', 'gani', 'masaksihan', 'nga', 'insidente', 'sa', 'kidnapping.', '"', 'Andam', 'ang', 'kapulisan', 'modawat', 'sa', 'inyong', 'complaint', ',', 'ari', 'lang', 'diri', ',', 'pa-blotter', 'ta', ',', 'most', 'importantly', ',', 'i-blotter', 'gyud', 'nato', 'na', 'siya', ',', '"', 'ingon', 'ni', 'Parallag', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 1, 0] | cebuaner |
4,653 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HAPSAY', ',', 'MALINAWON', ''', 'ANG', 'UNANG', 'ADLAW', 'SA', 'KLASE', 'BISAN', 'PA', 'SA', 'TAHO', 'SA', 'PAGHUOT', ',', 'KAKULANG', 'SA', 'CLASSROOMS', 'Gihulagway', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'ang', 'unang', 'adlaw', 'sa', 'klase', 'nga', 'hapsay', 'ug', 'malinawon', ',', 'bisan', 'paman', 'sa', 'pipila', 'ka', 'mga', 'isyu.', 'Sumala', 'pa', 'sa', 'inisyal', 'nga', 'assessment', 'sa', 'DepEd', 'base', 'sa', 'mga', 'reports', 'nga', 'gisumite', 'sa', 'mga', 'regional', 'directors', ',', 'hapsay', 'ang', 'pag-abri', 'sa', 'school', 'year', '2022-2023', 'bisan', 'paman', 'sa', 'pipila', 'ka', 'mga', 'kabalaka', 'nga', 'natubag', 'na', 'sa', 'tagsa-tagsa', 'ka', 'mga', 'tunghaan', ',', 'sama', 'sa', 'paghuot', 'o', 'kakulang', 'sa', 'classrooms', 'sa', 'mga', 'lugar', 'nga', 'nagpahigayon', 'og', 'face-to-face', 'classes.', 'Matod', 'pa', 'ni', 'DepEd', 'spokesperson', 'Michael', 'Poa', ',', 'natubag', 'kini', 'pinaagi', 'sa', 'mga', 'estratehiya', 'nga', 'gigamit', 'sa', 'nagkalain-laing', 'mga', 'tunghaan', 'pinaagi', 'sa', 'paggamit', 'og', 'shifting', 'schedules', 'ug', 'blended', 'learning', 'mode.', '#', 'NewsBite', '|', 'woth', 'reports', 'from', 'Inquirer.Net'] 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, 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, 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, 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, 0, 0, 0, 0] | cebuaner |
4,654 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PH', 'POVERTY', ':', 'DILI', 'KA', '"', 'FOOD', 'POOR', '"', 'KUNG', 'MOGASTO', 'KA', 'OG', 'LABAW', 'SA', 'P18.62', 'MATAG', 'KAON', 'Gipahayag', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'niadtong', 'Agosto', '15', 'nga', 'P8,379', 'lamang', 'niadtong', '2021', 'ang', 'food', 'threshold', 'o', 'sukaranan', 'sa', 'pagkaon', 'sa', 'usa', 'ka', 'pamilya', 'nga', 'aduna'y', 'lima', 'ka', 'kabanay.', 'Nagpasabot', 'kini', 'nga', 'dili', 'iklasipikar', 'sa', 'gobyerno', 'ang', 'usa', 'ka', 'indibidwal', 'nga', '"', 'food', 'poor', '"', 'kung', 'mogasto', 'siya', 'og', 'labaw', 'sa', 'P18.62', 'matag', 'kaon.', 'Sumala', 'pa', 'sa', 'PSA', ',', 'nisaka', 'sa', '18.1', '%', 'ang', 'poverty', 'incidence', 'niadtong', '2021', 'nga', 'katumbas', 'sa', '19.99', 'milyon', 'ka', 'mga', 'Pilipino', '(', '3.50', 'milyon', 'ka', 'mga', 'pamilya', ')', 'kinsa', 'gikonsiderar', 'nga', 'kabus.', 'Mas', 'taas', 'kini', 'kung', 'ikompara', 'sa', '16.7', '%', 'niadtong', '2018', 'nga', 'katumbas', 'sa', '17.67', 'milyon', 'ka', 'mga', 'Pilipino', '(', '3', 'milyon', 'ka', 'mga', 'pamilya', ')', '.', 'P12,030', 'ang', 'minimum', 'income', 'nga', 'kinahanglan', 'sa', 'usa', 'ka', 'pamilya', 'nga', 'aduna'y', 'lima', 'ka', 'kabanay', 'aron', 'mapalit', 'ang', 'ilang', 'basic', 'food', 'ug', 'non-food', 'needs.', 'Mas', 'taas', 'kini', 'kung', 'ikompara', 'sa', 'P10,756', 'niadtong', '2018', '.'] 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 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] | cebuaner |
4,655 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikompirmar', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'nga', 'natala', 'niini', 'ang', 'ikaupat', 'nga', 'kaso', 'sa', 'sakit', 'nga', 'monkeypox', 'sa', 'Pilipinas.', 'Sumala', 'pa', 'sa', 'DOH', ',', 'ang', 'maong', 'bag-ong', 'kaso', 'usa', 'ka', '25-anyos', 'nga', 'Pilipino', 'kinsa', 'walay', 'travel', 'history', 'gikan', 'o', 'paingon', 'sa', 'nasud', 'nga', 'dunay', 'mga', 'kompirmadong', 'kaso', 'sa', 'monkeypox.', 'Nagpositibo', 'sa', 'monkeypox', 'ang', 'naasoy', 'nga', 'pasyente', 'niadtong', 'Biyernes', ',', 'Agosto', '19', ',', 'ug', 'anaa', 'na', 'karon', 'gitambalan', 'sa', 'usa', 'ka', 'isolation', 'facility', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 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, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,656 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'adlaw', 'sa', 'face-to-face', 'classes', 'sa', 'Sibulan', 'National', 'High', 'School', 'karong', 'adlawa', ',', 'Agosto', '22', ',', '2022.', 'Mao', 'kini', 'ang', 'unang', 'higayon', 'nga', 'dunay', 'in-person', 'flag', 'ceremony', 'sa', 'maong', 'eskuwelahan', 'human', 'ang', 'duha', 'ka', 'tuig', 'nga', 'distance', 'learning', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 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 |
4,657 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'na', 'sa', 'P1,239,000', 'ang', 'nahatag', 'nga', 'educational', 'assistance', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', 'sa', 'Negros', 'Oriental', 'sa', 'unang', 'adlaw', 'sa', 'pag-apod-apod', 'sa', 'maong', 'ayuda', 'kagahapong', 'adlawa', ',', 'Aug.', '20', ',', '2022.', 'Sumala', 'pa', 'sa', 'DSWD', 'Region', 'VII', ',', '594', 'ka', 'estudyante', 'sa', 'probinsya', 'ang', 'nakadawat', 'sa', 'maong', 'ayuda.', 'Unanf', 'gianunsyo', 'nga', 'makadawat', 'og', 'P1,000', 'ang', 'mga', 'estudyante', 'sa', 'elementary', ',', 'P2,000', 'sa', 'junior', 'high', 'school', ',', 'P3,000', 'sa', 'senior', 'high', 'school', ',', 'ug', 'P4,000', 'alang', 'sa', 'mga', 'tinun-an', 'sa', 'kolehiyo', 'ug', 'vocational', 'school', '.'] 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, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 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] | cebuaner |
4,658 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AMPING', 'SA', 'PAG-ULAN', 'BESHIE', '!', 'Padayong', 'makasinati', 'og', 'pag-ulan', 'ang', 'Negros', 'Oriental', 'ug', 'tibuok', 'Kabisay-an', 'karong', 'adlawa', ',', 'Agosto', '22', ',', '2022', ',', 'tungod', 'sa', 'epekto', 'sa', 'ikog', '(', 'trough', ')', 'sa', 'Bagyong', '#', 'FloritaPH', 'ug', 'sa', 'Hanging', 'Habagat', ',', 'sumala', 'pa', 'sa', 'PAGASA.', 'Usa', 'na', 'ka', 'tropical', 'storm', 'ang', 'Bagyong', 'Florita', ',', 'sumala', 'pa', 'sa', 'latest', 'update', 'sa', 'ahensya.', 'Pipila', 'ka', 'lugar', 'sa', 'Luzon', 'ang', 'gipaubos', 'sa', 'Signal', 'No.', '1', 'ug', '2', 'tungod', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 7, 8, 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 |
4,659 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'ESTUDYANTENG', 'NITRABAHO', 'SA', 'CITY', 'HALL', ',', 'NAKADAWAT', 'NA', 'SA', 'SWELDO', 'GIKAN', 'SA', 'KAGAMHANANG', 'SIYUDAD', 'Nadawat', 'na', 'sa', 'mga', 'estudyanteng', 'nitrabaho', 'sa', 'City', 'Hall', 'ang', '60', '%', 'sa', 'ilang', 'sweldo', 'gikan', 'sa', 'kagamhanang', 'siyudad', 'sa', 'Dumaguete', 'niadtong', 'usang', 'semana', ',', 'samtang', 'andam', 'na', 'sab', 'nga', 'ihatag', 'ang', '40', '%', 'nga', 'bahin', 'sa', 'Department', 'of', 'Labor', 'and', 'Employment', '(', 'DOLE', ')', 'human', 'kini', 'maproseso.', 'Nakatrabaho', 'ang', 'maong', 'mga', 'estudyante', 'ubos', 'sa', 'Special', 'Program', 'for', 'the', 'Employment', 'of', 'Students', '(', 'SPES', ')', ',', 'usa', 'ka', 'programa', 'nga', 'gipatuman', 'sa', 'buhatan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'pinaagi', 'sa', 'Public', 'Employment', 'Service', 'Office', '(', 'PESO', ')', 'ug', 'DOLE.', 'Tumong', 'niini', 'nga', 'mahatagan', 'og', 'kahigayonan', 'ang', 'mga', 'estudyante', 'nga', 'makatigom', 'aron', 'isuporta', 'sa', 'ilang', 'pagskwela', 'ug', 'makat-on', 'sa', 'mga', 'buluhaton', 'sa', 'opisina', 'sa', 'City', 'Hall.', 'Aduna', 'sila'y', 'kinatibuk-ang', 'sweldo', 'nga', 'P9,846.40', 'matag', 'usa', 'nga', 'gitrabahuan', 'nila', 'sulod', 'sa', '20', 'ka', 'adlaw.', 'Gi-awhag', 'sila', 'ni', 'Mayor', 'Remollo', 'nga', 'ihatag', 'ang', 'ilang', 'sweldo', 'aron', 'ibayad', 'sa', 'ilang', 'tuition', 'fee', 'ug', 'uban', 'pang', 'galastuhan', 'para', 'maka-eskwela', 'ug', 'makatabang', 'sa', 'ilang', 'mga', 'ginikanan.', 'Gibutyag', 'sab', 'ni', 'Mayor', 'Remollo', 'mga', 'aduna', 'pa'y', 'laing', 'batch', 'sa', 'mga', 'estudyanteng', 'makatrabaho', 'sa', 'City', 'Hall', 'sunod', 'tuig', 'tungod', 'gahinan', 'kini', 'og', 'pondo', 'alang', 'sa', 'ilang', 'sweldo', 'ubos', 'sa', 'maong', 'programa', '.'] 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, 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, 5, 0, 0, 0, 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, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 4, 4, 4, 4, 4, 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, 3, 4, 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, 1, 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] | cebuaner |
4,660 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'na', 'sa', 'P756,000', 'ang', 'nahatag', 'nga', 'educational', 'assistance', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', 'sa', 'Negros', 'Oriental', 'sa', 'unang', 'adlaw', 'sa', 'pag-apod-apod', 'sa', 'maong', 'ayuda', 'kagahapong', 'adlawa', ',', 'Aug.', '20', ',', '2022.', 'Sumala', 'pa', 'sa', 'DSWD', 'Region', 'VII', ',', '369', 'ka', 'estudyante', 'sa', 'probinsya', 'ang', 'nakadawat', 'sa', 'maong', 'ayuda.', 'Unang', 'gianunsyo', 'nga', 'makadawat', 'og', 'P1,000', 'ang', 'mga', 'estudyante', 'sa', 'elementary', ',', 'P2,000', 'sa', 'junior', 'high', 'school', ',', 'P3,000', 'sa', 'senior', 'high', 'school', ',', 'ug', 'P4,000', 'alang', 'sa', 'mga', 'tinun-an', 'sa', 'kolehiyo', 'ug', 'vocational', 'school', '.'] 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, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 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] | cebuaner |
4,661 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Oras', 'na', 'aron', 'magsabwag', 'og', 'papremyo', 'alang', 'sa', 'Week', '6', 'sa', 'Champion', 'Na', ',', 'Milyonaryo', '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, 7, 8, 8, 8, 8] | cebuaner |
4,662 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Natala', 'sa', 'Pilipinas', 'ang', 'ikaduha', 'ug', 'ikatulong', 'kaso', 'niini', 'sa', 'sakit', 'nga', 'monkeypox.', 'Sumala', 'pa', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', ',', 'nag-edad', 'og', '34', 'ug', '29', 'anyos', 'ang', 'mga', 'naasoy', 'nga', 'kaso.', 'Sila', 'pulos', 'gikan', 'sa', 'mga', 'nasud', 'diin', 'dunay', 'mga', 'natalang', 'kaso', 'sa', 'monkeypox', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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 |
4,663 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-share', 'ni', 'Carlo', 'Toleza', 'ngadto', 'sa', 'social', 'media', 'ang', 'usa', 'ka', 'video', 'sa', 'naglutaw', 'nga', 'sari-sari', 'store', 'sa', 'kadagatan', 'sa', 'Bais', 'City', 'sa', 'probinsya', 'sa', 'Negros', 'Oriental', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 0] | cebuaner |
4,664 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'KOMPANYA', 'SA', 'SOFTDRINKS', ',', 'NIANGKON', 'NGA', 'NAMROBLEMA', 'SA', 'KAKULANGON', 'SA', 'KALAMAY', 'SA', 'NASUD', 'Giangkon', 'sa', 'mga', 'nag-unang', 'kompanyang', 'naghimo', 'og', 'softdrinks', 'sa', 'Pilipinas', 'nga', 'namroblema', 'sila', 'tungod', 'sa', 'giatubang', 'nilang', 'sugar', 'shortage', 'kon', 'kakulangon', 'sa', 'kalamay', 'sa', 'nasud.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Coca-Cola', 'Beverages', 'Philippines', ',', 'Pepsi-Cola', 'Products', 'Philippines', ',', 'ug', 'ARC', 'Refreshments', 'Corporation', 'pinaagi', 'sa', 'usa', 'ka', 'talagsaon', 'nga', 'hiniusang', 'pamahayag', 'niadtong', 'Martes', ',', 'Agosto', '16', ',', '2022.', 'Giluwatan', 'sa', 'maong', 'mga', 'kompanya', 'ang', 'ilang', 'pamahayag', 'taliwala', 'sa', 'padayong', 'pagsaka', 'sa', 'presyo', 'sa', 'mga', 'palaliton', 'ug', 'kontrobersiya', 'sa', 'gobyerno', 'nunot', 'sa', 'pag-angkat', 'og', 'kalamay.', 'Gisubli', 'nila', 'nga', 'nag-atubang', 'karon', 'ang', 'industriya', 'og', 'kakulangon', 'sa', 'premium', 'refined', 'sugar', 'o', 'bottlers', ''', 'grade', 'sugar', 'nga', 'mao'y', '"', 'key', 'ingredient', '"', 'sa', 'ilang', 'mga', 'produkto.', 'Dugang', 'pa', 'nila', ',', 'nakigtimbayayong', 'na', 'sab', 'sila', 'sa', 'ubang', 'stakeholders', 'sa', 'industriya', 'ug', 'sa', 'gobyerno', 'aron', 'mahatagan', 'og', 'pagtagad', 'ang', 'maong', 'sitwasyon', '.'] 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 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] | cebuaner |
4,665 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PASAHERO', 'NGA', 'WALA'Y', 'HELMET', ',', 'MANNEQUIN', 'MAN', 'DIAY', 'Viral', 'karon', 'ang', 'mga', 'hulagway', 'sa', 'usa', 'ka', 'delivery', 'rider', 'kinsa', 'gipahunong', 'sa', 'mga', 'traffic', 'enforcer', 'sa', 'Rizal', 'Avenue', 'sa', 'Puerto', 'Princesa', 'City', ',', 'Palawan', 'tungod', 'aduna', 'kini', 'pasahero', 'nga', 'wala'y', 'helmet', '--', 'apan', 'usa', 'diay', 'kini', 'ka', 'mannequin', 'nga', 'iyang', 'i-deliver', 'sa', 'usa', 'niya', 'ka', 'customer.', 'Gi-post', 'ang', 'mga', 'hulagway', 'sa', 'FB', 'page', 'nga', 'Just', 'Ride', 'Palawan', 'niadtong', 'Lunes', ',', 'Agosto', '15', ',', '2022.', 'Sumala', 'pa', 'sa', 'admin', 'sa', 'maong', 'FB', 'page', ',', 'nahitabo', 'ang', 'makalingaw', 'nga', 'insidente', 'mga', 'duha', 'na', 'ka', 'bulan', 'ang', 'milabay.', 'Makita', 'sab', 'sa', 'usa', 'sa', 'mga', 'hulagway', 'nga', 'gihatagan', 'sa', 'traffic', 'enforcer', 'og', 'thumbs', 'up', 'ang', 'rider', 'human', 'sa', 'paglabay', 'niya', 'sa', 'inspection', '.'] 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, 5, 6, 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, 7, 0, 0, 7, 8, 8, 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] | cebuaner |
4,666 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGTIAYON', ',', 'GIPUSIL', 'PATAY', 'SA', 'SAN', 'JOSE', 'Patay', 'ang', 'usa', 'ka', 'magtiayon', 'human', 'gipusil', 'sa', 'Purok', '3', 'sa', 'Barangay', 'Janay-Janay', 'sa', 'lungsod', 'sa', 'San', 'Jose', 'mga', '9:15', 'sa', 'gabie', 'niadtong', 'August', '14', ',', '2022.', 'Giila', 'ang', 'mga', 'biktima', 'nga', 'sila', 'si', 'Margarito', 'Rosano', 'ug', 'Antonieta', 'Rosana', ',', 'puros', '47-anyos', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'kapulisan', ',', 'nakita', 'sa', 'ilang', 'menor', 'de', 'edad', 'nga', 'anak', 'nga', 'gipusil', 'sa', 'makadaghang', 'higayon', 'ang', 'magtiayon', 'samtang', 'naghigda', 'sa', 'ilang', 'balay.', 'Human', 'niini', ',', 'dali', 'nga', 'niikyas', 'ang', 'mga', 'suspek.', 'Giila', 'sab', 'ang', 'mga', 'suspek', 'nga', 'sila', 'si', 'Jayson', 'Rosano', 'ug', 'Jennifer', 'Rosano', ',', 'puros', 'hingkod', 'ang', 'pangidaron', 'ug', 'residente', 'sa', 'naasoy', 'nga', 'lugar.', 'Manag-igsuon', 'ang', 'duha', 'ka', 'suspek', 'kinsa', 'pag-umangkon', 'sab', 'sa', 'usang', 'biktima', 'nga', 'si', 'Margarito.', 'Human', 'sa', 'maong', 'insidente', ',', 'gidala', 'ang', 'mga', 'biktima', 'sa', 'Negros', 'Oriental', 'Provincial', 'Hospital', 'aron', 'mahatag', 'og', 'atensyong', 'medical', 'apan', 'gideklarar', 'nga', 'dead', 'on', 'arrival', 'si', 'Antonieta', ',', 'samtang', 'gideklarar', 'nga', 'patay', 'na', 'si', 'Margarito', 'mga', 'alas', '11:15', 'sa', 'gabie', 'sa', 'maong', 'adlaw.', 'Nakuha', 'sa', 'crime', 'scene', 'ang', 'unom', 'ka', 'fired', 'cartridges', 'sa', 'Caliber', '.45', 'ug', 'upat', 'ka', 'Caliber', '9MM.', 'Ipailalom', 'kini', 'sa', 'Ballistic', 'Examination', 'sa', 'Provincial', 'Crime', 'Laboratory.', 'Nagpahigayon', 'na', 'sab', 'og', 'hot', 'pursuit', 'operations', 'ang', 'kapulisan', 'aron', 'masikop', 'ang', 'mga', 'suspek', 'ug', 'nagpadayon', 'ang', 'imbestigasyon', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 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, 1, 2, 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, 1, 2, 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, 1, 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, 1, 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, 7, 8, 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] | cebuaner |
4,667 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PANEL', 'SA', 'KAMARA', ',', 'GIDUSO', 'ANG', 'PAG-POSTPONE', 'NA', 'PUD', 'SA', 'BRGY', ',', 'SK', 'ELECTIONS', 'Giaprobahan', 'sa', 'suffrage', 'panel', 'ang', 'usa', 'ka', 'mosyon', 'nga', 'nagtinguha', 'nga', 'i-postpone', 'ang', 'eleksyon', 'sa', 'barangay', 'ug', 'Sangguniang', 'Kabataan', '(', 'SK', ')', 'gikan', 'sa', 'Disyembre', '2022', 'ngadto', 'sa', 'Disyembre', '2023', ',', 'bisan', 'pa', 'sa', 'pahimangno', 'sa', 'Commission', 'on', 'Elections', '(', 'COMELEC', ')', 'nga', 'ang', 'pagbuhat', 'niini', 'magdala', 'lamang', 'og', 'dugang', 'nga', 'mga', 'gasto.', 'Gisubli', 'ni', 'COMELEC', 'chairman', 'George', 'Garcia', 'nga', 'kung', 'dili', 'madayon', 'ang', 'eleksyon', 'sa', 'katapusan', 'ning', 'tuiga', ',', 'ilang', 'ipadayon', 'ang', 'voter', 'registration', 'karong', 'Oktubre', 'nga', 'moresulta', 'sa', 'pagpanginahanglan', 'og', 'mas', 'dako', 'nga', 'budget', 'ug', 'dugang', 'nga', 'mga', 'voting', 'materials.', 'Dugang', 'pa', 'niya', ',', 'kung', 'dili', 'madayon', 'ang', 'barangay', 'ug', 'SK', 'elections', ',', 'magkinahanglan', 'og', 'dugang', 'nga', 'P5', 'bilyones', 'alang', 'sa', 'dugang', 'nga', 'gasto', 'gikan', 'sa', 'P8.4', 'bilyones', 'nga', 'budget.', '38', 'ka', 'mga', 'balaodnon', 'ang', 'giduso', 'sa', 'House', 'of', 'Representatives', 'nga', 'nagtinguha', 'sa', 'pagsuspenso', 'sa', 'eleksyon', 'sa', 'Disyembre', 'tungod', 'sa', 'nagkalain-laing', 'rason', ',', 'usa', 'niini', 'ang', 'paggahin', 'sa', 'P8.4', 'billion', 'nga', 'budget', 'ngadto', 'sa', 'COVID-19', 'response', 'ug', 'tugutan', 'ang', 'gobyerno', 'nga', 'mopahuway', 'human', 'sa', 'piniliay', 'niadtong', 'Mayo', 'ning', 'tuiga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,668 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Governor', 'Henry', 'Teves', ',', 'gipirmahan', 'ang', 'Medical', 'Scholar', 'sa', 'lalawigan', 'sa', 'Negros', 'Oriental', 'kagahapong', 'adlawa', ',', 'August', '15', ',', '2022.', 'Ang', 'mga', 'scholar', ',', 'mga', 'tinun-an', 'sa', 'Silliman', 'University', 'Medical', 'School', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 7, 8, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0] | cebuaner |
4,669 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PULIS', ',', 'GIPUSIL', 'PATAY', 'SA', 'BAYAWAN', 'CITY', 'Patay', 'ang', 'usa', 'ka', 'pulis', 'human', 'gipusil', 'sa', 'wala', 'pa', 'mailhing', 'mga', 'suspek', 'sa', 'Barangay', 'San', 'Roque', 'sa', 'dakbayan', 'sa', 'Bayawan', 'niadtong', 'August', '15', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Police', 'Corporal', 'Rey', 'Agustillo', 'Ambo', ',', '37-anyos', ',', 'ug', 'sakop', 'sa', '705th', 'Maneuver', 'Company', ',', 'Regional', 'Mobile', 'Force', 'Battalion', '7', 'nga', 'nakabase', 'sa', 'Sitio', 'Nagbagang', ',', 'Barangay', 'Poblacion', ',', 'Sta.', 'Catalina.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'nag-patrolya', 'si', 'Police', 'Corporal', 'Ambo', 'uban', 'si', 'Pat.', 'Rhullin', 'Mar', 'Abrasaldo', 'aron', 'makakuha', 'og', 'impormasyon', 'bahin', 'sa', 'presensya', 'sa', 'Communist', 'Terrorist', 'Group', 'sa', 'naasoy', 'nga', 'lugar', 'sa', 'dihang', 'kalit', 'lang', 'nga', 'aduna'y', 'wala', 'pa', 'mailhing', 'mga', 'suspek', 'ang', 'nagpabuto', 'nila', 'gamit', 'ang', 'KG-9', '9mm', 'caliber', 'firearm.', 'Nakaangkon', 'og', 'grabe', 'nga', 'samad', 'pinusilan', 'si', 'Police', 'Corporal', 'Ambo', 'nga', 'mao'y', 'hinungdan', 'sa', 'iyang', 'hinanaling', 'kamatayon', ',', 'samtang', 'luwas', 'ra', 'si', 'Pat.', 'Abrasaldo', 'kinsa', 'nakahimo', 'sab', 'sa', 'pagtawag', 'alang', 'sa', 'tabang.', 'Usa', 'sab', 'ka', 'menor', 'de', 'edad', 'ang', 'nakaangkon', 'og', 'samad', 'pinusilan', 'kinsa', 'atol', 'nga', 'anaa', 'sa', 'lugar', 'sa', 'insidente.', 'Dali', 'sab', 'siya', 'nga', 'gidala', 'sa', 'Bayawan', 'District', 'Hospital', 'aron', 'mahatagan', 'og', 'atensyong', 'medikal.', 'Nihatag', 'na', 'sab', 'og', 'mando', 'si', 'PRO7', 'Director', 'Police', 'Brigadier', 'General', 'Roque', 'Eduardo', 'DP', 'Vega', 'ngadto', 'sa', 'Bayawan', 'CPS', ',', 'NOPPO', 'nga', 'mopahigayon', 'og', 'hot', 'pursuit', 'operation', 'aron', 'sa', 'posibleng', 'pag-ila', 'ug', 'pag-aresto', 'sa', 'mga', '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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 2, 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, 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, 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, 1, 2, 2, 2, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,670 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gideklarar', 'sa', 'usa', 'ka', 'food', 'website', 'ang', 'Filipino', 'fast-food', 'restaurant', 'nga', 'Jollibee', 'nga', 'mao', 'ang', 'aduna'y', ''best', ''', 'fried', 'chicken', 'sa', 'Estados', 'Unidos.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Eater.com', 'sa', 'usa', 'ka', 'article', 'niini', 'bahin', 'sa', 'kung', 'kinsa', 'ang', 'naghimo', 'og', 'best', 'fried', 'chicken', 'sa', 'maong', 'nasud.', 'Kakompetensya', 'sa', 'Jollibee', 'ang', 'McDonalds', ',', 'Burger', 'King', ',', 'Wendy’s', ',', 'KFC', ',', 'ug', 'Bonchon', ',', 'ingon', 'man', 'mga', 'restaurant', 'sa', 'Estados', 'Unidos', 'sama', 'sa', 'Chick-Fil-A', ',', 'Church’s', 'Chicken', ',', 'White', 'Castle', 'ug', 'Bojangles.', 'Aduna', 'sila'y', 'gipahigayon', 'nga', 'upat', 'ka', 'rounds', 'sa', 'nagkalain-laing', 'kategorya', 'sama', 'sa', '“bones”', ',', '“no', 'bones”', ',', '“sandwiched”', 'ug', '“sauced”', ',', 'diin', 'tilawan', 'sa', 'mga', 'editors', 'ang', 'mga', 'manok', 'sa', 'matag', 'katergorya.', 'Human', 'niini', ',', 'mohatag', 'sila', 'og', 'review', 'kung', 'kinsa', 'ang', 'aduna'y', 'pinakalami', 'nga', 'manok', 'ug', 'kung', 'kinsa', 'ang', 'nadaog', '.'] 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, 5, 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, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 0, 5, 6, 0, 5, 6, 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] | cebuaner |
4,671 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'hulagway', 'sa', 'unang', 'adlaw', 'sa', 'face-to-face', 'classes', 'sa', 'St.', 'Paul', 'University', 'Dumaguete', 'kagahapong', 'adlawa', ',', 'August', '15', ',', '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, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,672 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'duha', 'ka', 'tuig', 'nga', 'online', 'classes', ',', 'gi-welcome', 'sa', 'Silliman', 'University', 'pinaagi', 'sa', 'pagpabanda', 'ang', 'mga', 'estudyante', 'sa', 'unang', 'adlaw', 'sa', 'face-to-face', 'classes', '.'] 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,673 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PSA', ':', 'MGA', 'POBRE', 'SA', 'NASUD', ',', 'MOABOT', 'NA', 'SA', '19', 'MILYON', 'Niigo', 'sa', '18.1', '%', 'ang', 'poverty', 'incidence', 'rate', 'o', 'kapobrehon', 'sa', 'Pilipinas', 'niadtong', '2021', ',', 'katumbas', 'sa', '19.99', 'milyon', 'ka', 'mga', 'kabus', 'nga', 'Pilipino.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'Lunes', ',', 'August', '15', ',', '2022.', 'Base', 'sa', 'datos', 'sa', 'PSA', ',', 'ang', 'National', 'Capital', 'Region', '(', 'NCR', ')', 'mao', 'ang', 'aduna'y', 'pinakaubos', 'nga', 'rate', 'sa', 'kapobrehon', 'nga', 'anaa', 'sa', '3.1', '%', 'niadtong', '2021', ',', 'samtang', 'ang', 'Bangsamoro', 'Autonomous', 'Region', 'mao', 'ang', 'aduna'y', 'pinakataas', 'nga', 'rate', 'sa', 'kapobrehon', 'nga', 'anaa', 'sa', '37.2', '%', 'sa', 'maong', 'tuig.', 'Bisan', 'paman', ',', 'niubos', 'sa', '24.6', '%', 'ang', 'poverty', 'incidence', 'rate', 'sa', 'BARMM', 'niadtong', '2021', 'ug', '2018', 'nga', 'aduna'y', '61.8', '%', '.'] 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, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,674 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAODNON', 'NGA', 'TUMONG', 'NGA', 'KUHAAN', 'ANG', 'HOLIDAY', 'SA', 'PILIPINAS', ',', 'GIDUSO', 'Giduso', 'sa', 'House', 'of', 'Representatives', 'ang', 'usa', 'ka', 'balaodnon', 'nga', 'tumong', 'nga', 'kuhaan', 'ang', 'gidaghanon', 'sa', 'holiday', 'sa', 'Pilipinas.', 'Gipasaka', 'ni', 'Albay', 'Representative', 'Joey', 'Salceda', 'ang', 'House', 'Bill', 'No.', '672', 'o', '"', 'Holiday', 'Rationalization', 'Act', 'of', '2022.', '"', 'Kung', 'maaprobahan', ',', 'aduna', 'sab', 'pipila', 'ka', 'mga', 'pulong', 'ang', 'tangtangon.', 'Mahimong', '"', 'Holidays', '"', 'ug', '"', 'Special', 'Days', '"', 'nalang', 'ang', 'kasamtangang', '"', 'Regular', 'Holidays', '"', 'ug', '"', 'Nationwide', 'Special', 'Days', '"', 'Aduna', 'sab', 'pipila', 'ka', 'mga', 'holidays', 'ang', 'kuhaon', 'sa', 'kalendaryo', 'sama', 'sa', 'Maundy', 'Thursday', ',', 'Eid’l', 'Adha', ',', 'National', 'Heroes', 'Day', ',', 'ug', 'uban', 'pa.', 'Sa', 'laing', 'bahin', ',', 'aduna', 'sab', 'usa', 'ka', 'lokal', 'nga', 'holiday', 'ang', 'matag', 'Local', 'Government', 'Unit', '(', 'LGU', ')', 'pinaagi', 'sa', 'balaod', 'o', 'ordinansa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 1, 2, 0, 7, 8, 8, 8, 0, 0, 7, 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, 7, 8, 0, 7, 8, 8, 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] | cebuaner |
4,675 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAODNON', 'NGA', 'MAGBAWAL', 'SA', 'PAGGAMIT', 'OG', 'CELLPHONE', 'ATOL', 'SA', 'KLASE', ',', 'GIDUSO', 'Giduso', 'sa', 'House', 'of', 'Representatives', 'ang', 'usa', 'ka', 'balaodnon', 'nga', 'magbawal', 'sa', 'paggamit', 'og', 'cellphone', 'ug', 'uban', 'pang', 'digital', 'device', 'atol', 'sa', 'klase.', 'Gipasaka', 'ni', 'Albay', 'Representative', 'Joey', 'Salceda', 'ang', 'House', 'Bill', 'No.', '662', 'o', '"', 'No', 'Cellphone', 'during', 'Classes', 'Act.', '"', 'Kung', 'maaprobahan', ',', 'bawal', 'na', 'nga', 'mogamit', 'og', 'cellular', 'phones', ',', 'smartphones', 'o', 'bisan', 'unsang', 'susama', 'nga', 'digital', 'devices', 'atol', 'sa', 'klase', 'gawas', 'kon', 'aduna'y', 'emergency', 'o', 'kinahanglan', 'kini', 'sa', 'pagtudlo.', 'Imandato', 'sab', 'sa', 'mga', 'eskwelahan', 'ang', 'pagpatukod', 'og', '"', 'device', 'depository', 'office', '"', 'kung', 'diin', 'pwede', 'nga', 'mabilin', 'sa', 'mga', 'estudyante', 'ang', 'ilang', 'gadgets', 'sa', 'pagsulod', 'sa', 'tunghaan.', 'Sumala', 'pa', 'sa', 'maong', 'balaodnon', ',', 'ipatuman', 'kini', 'sa', 'tanang', 'pribado', 'ug', 'pampublikong', 'tunghaan', 'gikan', 'sa', 'kindergarten', 'hangtod', 'sa', 'kolehiyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 1, 2, 0, 7, 8, 8, 8, 0, 0, 7, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,676 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigon', 'sa', 'Chinese', 'Lunar', 'Calendar', ',', 'gisaulog', 'ang', 'Ghost', 'Month', 'gikan', 'sa', 'Hulyo', '29', 'hangtod', 'Agosto', '26.', 'Matud', 'pa', 'sa', 'Chinese', 'culture', ',', 'mao', 'kini', 'ang', 'panahon', 'nga', 'monaog', 'nganhi', 'sa', 'atong', 'kalibutan', 'ang', 'mga', 'nitaliwan', 'natong', 'minahal', 'sa', 'kinabuhi', 'ug', 'kaliwatan', 'gikan', 'sa', 'laing', 'kalibutan.', 'Puwede', 'sab', 'nimo', 'karong', 'handumon', 'ang', 'inyong', 'namatay', 'nga', 'gugma', 'human', 'siya', 'nitaliwan', '...', 'sa', 'lain', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 8, 8, 0, 0, 0, 7, 8, 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] | cebuaner |
4,677 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SENSITIBONG', 'STORYA', 'UG', 'HULAGWAY', 'GIINGONG', 'BUKOG', 'SA', 'BIKTIMA', 'SA', 'BAGYONG', 'ODETTE', ',', 'NAPALGAN', 'SA', 'USA', 'KA', 'LUNA', 'SA', 'BAIS', 'CITY', 'Napalgan', 'sa', 'usa', 'ka', 'luna', 'sa', 'Sitio', 'Tambakan', ',', 'Barangay', '1', 'sa', 'Bais', 'City', 'ang', 'bukog', 'sa', 'usa', 'ka', 'biktima', 'nga', 'giingong', 'nawala', 'pa', 'niadtong', 'Bagyong', '#', 'OdettePH.', 'Wala', 'pa', 'hingpit', 'nga', 'mailhi', 'sa', 'pagkakaron', 'kun', 'kinsa', 'ang', 'nasangpit', 'nga', 'biktima.', 'Napalgan', 'karong', 'Huwebes', ',', 'Agosto', '11', ',', '2022', 'ang', 'maong', 'bukog', 'samtang', 'nag-ayo', 'og', 'alad', 'ang', 'usa', 'ka', 'lalaki', 'sa', 'naasoy', 'nga', 'luna.', 'Giingong', 'naghayang', 'ug', 'daw', 'naghalog', 'sa', 'punuan', 'sa', 'saging', 'ang', 'kalabira', 'sa', 'maong', 'biktima.', 'Gikatahong', 'dunay', 'nawala', 'sa', 'maong', 'dapit', 'sa', 'pag-igo', 'sa', 'Bagyong', 'Odette', 'sa', 'Bais', 'City', 'niadtong', 'Disyembre', '2021', 'human', 'sila', 'naanod', 'sa', 'kusog', 'nga', 'baha.', 'Gihipos', 'na', 'sa', 'mga', 'sakop', 'sa', 'City', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Office', '(', 'CDRRMO', ')', 'sa', 'Bais', 'ang', 'maong', 'bukog', 'apan', 'padayon', 'pang', 'gikompirmar', 'ang', 'kailhanan', 'sa', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 5, 6, 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, 0, 0, 0, 0, 0, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,678 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DEPED', 'SA', 'NEGROS', 'ORIENTAL', 'ANDAM', 'NA', 'SA', 'PAG-ABRI', 'SA', 'KLASE', 'KARONG', 'AGOSTO', ',', '100', '%', 'F2F', 'CLASSES', 'SA', 'NOBYEMBRE', 'Andam', 'na', 'ang', 'probinsya', 'sa', 'Negros', 'Oriental', 'alang', 'sa', 'pagsugod', 'sa', 'limitado', 'nga', 'face-to-face', 'classes', 'karong', 'Agosto', 'ug', 'full', 'in-person', 'classes', 'karong', 'Nobyembre.', 'Mao', 'kini', 'ang', 'gipahayag', 'sa', 'mga', 'opisyales', 'sa', 'DepEd', 'NegOr', 'atol', 'sa', '"', 'Kapihan', 'sa', 'PIA', '"', 'sa', 'Bethel', 'Guest', 'House', 'karong', 'adlawa', ',', 'Agosto', '11', ',', '2022.', 'Nitambong', 'sa', 'maong', 'presscon', 'ang', 'School’s', 'Division', 'Superintendent', ',', 'DepEd', 'Division', 'of', 'Dumaguete', 'Dr.', 'Cyrus', 'Gregorio', 'Elejorde', ',', 'Information', 'Officer', 'of', 'DepEd', 'Neg', 'Or.', 'Division', 'Karla', 'P.', 'Antonio', ',', 'apil', 'na', 'si', 'PLTCOL.', 'Joeson', 'Parallag.', 'Sumala', 'pa', 'ni', 'Dr.', 'Elejorde', ',', 'ilang', 'ipahiangay', 'ang', 'hybrid', 'modality', 'kung', 'diin', 'aduna', 'kini', 'schedule', 'kung', 'kanus-a', 'mopahigayon', 'og', 'online', 'classes', 'ug', 'f2f', 'classes.', 'Apil', 'na', 'ang', 'pagpatuman', 'og', 'duha', 'ka', 'subject', 'kada', 'adlaw', 'aron', 'dili', 'bug-at', 'ang', 'mga', 'buluhaton', 'sa', 'mga', 'estudyante.', 'Matod', 'pa', 'ni', 'Information', 'Officer', 'Antonio', ',', 'naka-focus', 'ang', 'DepEd', 'sa', '16', 'ka', 'lungsod', 'sa', 'Negros', 'Oriental.', 'Gipatukuran', 'nila', 'ang', 'mga', 'tunghaan', 'sa', 'maong', 'dapit', 'og', 'isolation', 'areas', 'ug', 'uban', 'pang', 'pasilidad', 'aron', 'masunod', 'ang', 'health', 'protocols', 'alang', 'sa', 'kaluwasan', 'sa', 'mga', 'estudyante.', 'Gisubli', 'ni', 'PLTCOL.', 'Parallag', ',', 'aduna', 'na'y', 'panagsabot', 'ang', 'PNP', 'ug', 'DepEd', 'bahin', 'sa', 'ilang', 'pagtabang', 'sa', 'Brigada', 'Eskwela.', 'Aduna', 'sab', 'sila'y', 'memorandum', 'of', 'agreement', 'nga', 'magtudlo', 'ang', 'kapulisan', 'sa', 'mga', 'eskwelahan', 'bahin', 'sa', 'VAWC', ',', 'bullying', 'ug', 'uban', 'pa.', 'Mag-focus', 'sab', 'ang', 'kapulisan', 'sa', 'peace', 'and', 'order', 'sa', 'umalabot', 'nga', 'pag-abri', 'sa', 'klase.', 'Gi-awhag', 'sab', 'sa', 'DepEd', 'ang', 'mga', 'ginikanan', 'sa', 'mga', 'estudyante', 'nga', 'protektahan', 'ang', 'ilang', 'mga', 'anak', 'pinaagi', 'sa', 'pagpabalon', 'nila', 'og', 'safety', 'kits', 'sama', 'sa', 'extra', 'face', 'mask', ',', 'alcohol', ',', 'ug', 'pagpahinumdom', 'nga', 'mopatuman', 'og', 'social', 'distancing', 'sa', 'eskwelahan', '.'] 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, 5, 6, 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, 3, 4, 0, 0, 0, 7, 8, 8, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 1, 2, 2, 0, 0, 0, 0, 3, 4, 4, 4, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 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, 1, 0, 0, 0, 3, 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, 1, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 7, 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, 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] | cebuaner |
4,679 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Agosto', 'pa', 'lang', 'apan', 'makita', 'na', 'sa', 'usa', 'ka', 'mall', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'kining', 'mga', 'gibaligya', 'nga', 'Christmas', 'decor', 'ug', 'mga', 'estatwa', 'ni', 'Santa', 'Claus', ',', 'lima', 'ka', 'bulan', 'sa', 'dili', 'pa', 'ang', 'adlaw', 'sa', 'Pasko.', 'Magsugod', 'ang', 'Christmas', 'season', 'sa', 'Pilipinas', 'karong', 'Setyembre', ',', 'ang', 'unang', 'bulan', 'sa', 'gitawag', 'nga', '"', 'Ber', 'months', '.', '"'] 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, 5, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0] | cebuaner |
4,680 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpahigayon', 'og', 'road', 'clearing', 'operations', 'ang', 'mga', 'kawani', 'gikan', 'sa', 'nagkalain-laing', 'hingtungdan', 'nga', 'mga', 'ahensya', 'sa', 'kadalanan', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'aron', 'kuhaon', 'ang', 'tanang', 'nakababag', 'sa', 'dalan', 'ug', 'mapalambo', 'ang', 'dagan', 'sa', 'trapiko', 'sa', 'national', 'highway.', 'Gipangulohan', 'kini', 'sa', 'Department', 'of', 'Public', 'Works', 'and', 'Highways', '(', 'DPWH', ')', 'uban', 'sa', 'tabang', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'pinaagi', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo.', 'Nitabang', 'sab', 'sa', 'maong', 'operasyon', 'ang', 'Provincial', 'Highway', 'Patrol', 'Group', ',', 'Philippine', 'National', 'Police', ',', 'Traffic', 'Management', 'Office', 'ug', 'Department', 'of', 'Interior', 'and', 'Local', 'Government', '(', 'DILG', ')', '.', 'Gisubli', 'sa', 'DPWH', 'nga', 'mandato', 'sa', 'gobyerno', 'ang', 'pagsiguro', 'nga', 'wala'y', 'bisan', 'unsang', 'babag', 'ang', 'anaa', 'sa', 'kadalanan', 'ilabi', 'na', 'sa', 'national', 'highways', 'aron', 'mahimong', 'hapsay', 'ang', 'dagan', 'sa', 'trapiko', 'ug', 'masiguro', 'ang', 'kaluwasan', 'sa', 'mga', 'motorista', 'ug', 'kadtong', 'maglakaw.', 'Subay', 'kini', 'sa', 'Presidential', 'Decree', 'No.', '17', 'o', 'mas', 'giila', 'nga', 'Philippine', 'Highway', 'Act', 'nga', 'gisuportaan', 'sa', 'DPWH', 'Department', 'Order', 'No.', '73', ';', 'series', 'of', '2014', 'ug', 'ang', 'Local', 'Government', 'Code', 'of', '1991', '.'] 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 3, 4, 4, 0, 3, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 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, 7, 8, 8, 8, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 7, 8, 8, 8, 8, 0] | cebuaner |
4,681 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAUDNON', 'PAGHATAG', 'OG', 'P2,000', 'AYUDA', 'SA', 'TANANG', 'PILIPINO', ',', 'GIDUSO', 'Giduso', 'karon', 'sa', 'House', 'of', 'Representatives', 'ang', 'usa', 'ka', 'balaodnon', 'nga', 'tumong', 'nga', 'maghatag', 'og', 'one-time', 'cash', 'aid', 'nga', 'P2,000', 'alang', 'sa', 'tanang', 'Pilipino.', 'Gipasaka', 'ang', 'House', 'Bill', 'No.', '2022', 'o', 'Universal', 'Ayuda', 'Act', 'sa', 'representante', 'sa', 'Kalinga', 'party-list', 'nga', 'si', 'Irene', 'Gay', 'F.', 'Saulog', 'niadtong', 'July', '18', ',', '2022.', 'Sumala', 'pa', 'sa', 'usa', 'ka', 'bahin', 'sa', 'maong', 'balaodnon', ',', 'usa', 'kini', 'ka', 'tabang', 'gikan', 'sa', 'kalisod', 'nga', 'hatod', 'sa', 'pandemya', 'tungod', 'sa', 'Covid-19', 'ug', 'sa', 'padayong', 'pagsaka', 'sa', 'presyo', 'sa', 'mga', 'palaliton.', 'Kung', 'maaprobahan', ',', 'ihatag', 'ang', 'maong', 'ayuda', 'sa', 'tanan', 'nga', 'mga', 'Pilipino', 'nga', 'nagpuyo', 'sa', 'nasud', 'atol', 'sa', 'pagpatuman', 'sa', 'maong', 'programa.', 'Dugang', 'pa', ',', 'usa', 'sab', 'kini', 'ka', 'patas', 'nga', 'pamaagi', 'aron', 'mahatagan', 'og', 'ayuda', 'ang', 'tanan', 'nga', 'mga', 'Pilipino', 'ilabi', 'na', 'kadtong', 'mga', 'napasagdaan', 'sa', 'mga', 'niaging', 'programa', '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. | [0, 0, 0, 0, 0, 0, 0, 7, 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, 7, 0, 0, 7, 8, 8, 8, 0, 7, 8, 8, 0, 0, 0, 3, 4, 0, 0, 1, 2, 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, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,682 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'MARITES', ',', 'GIDUSO', 'NGA', 'MAPRISO', 'SA', 'USA', 'KA', 'BALAUDNON', 'BATOK', 'FAKE', 'NEWS', 'Gipasaka', 'ngadto', 'sa', 'House', 'of', 'Representatives', 'ang', 'usa', 'ka', 'balaodnon', 'nga', 'magduso', 'nga', 'mahimong', 'krimen', 'ang', 'paggama', 'ug', 'pagpakuyanap', 'og', 'fake', 'news', '--', 'komon', 'nga', 'termino', 'sa', 'mini', 'nga', 'impormasyon.', 'Ang', 'mga', 'representante', 'nga', 'sila', 'si', 'Josephine', 'Lacson-Noel', '(', 'Malabon', ')', 'ug', 'Florencio', 'Gabriel', 'Noel', '(', 'Aw', 'Waray', 'party-list', ')', 'ang', 'nipasang-at', 'sa', 'House', 'Bill', 'No.', '2971.', 'Sa', 'pagpasaka', 'sa', 'maong', 'balaodnon', ',', 'nangayo', 'sila', 'og', 'kausbanan', 'sa', 'Cybercrime', 'Prevention', 'Act', 'of', '2012', 'nga', 'ilakip', 'ang', 'depinisyon', 'sa', '“fake', 'news', '"', '.', 'Aduna', 'sab', 'mga', 'petisyon', 'sa', 'Cybercrime', 'Prevention', 'Act', 'ngadto', 'sa', 'Supreme', 'Court', 'tungod', 'aduna'y', 'mga', 'parte', 'niini', 'nga', 'dili', 'uyon', 'sa', 'konstitusyon.', 'Nagdala', 'og', 'mas', 'bug-at', 'nga', 'silot', 'ug', 'taas', 'nga', 'panahon', 'sa', 'pagkapriso', 'ang', 'probisyon', 'sa', 'cyber', 'libel', 'kung', 'itandi', 'sa', 'regular', 'nga', 'liber.', 'Ang', 'bisan', 'kinsa', 'nga', 'mapamatud-an', 'nga', 'sad-an', ',', 'mahimong', 'silotan', 'og', 'pagkapriso', 'nga', 'unom', 'ka', 'tuig', 'ug', 'usa', 'ka', 'adlaw', 'ngadto', 'sa', '12', 'ka', 'tuig', 'o', 'multa', 'nga', 'labing', 'menos', 'sa', 'P200,000', '.'] 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, 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, 1, 2, 0, 3, 0, 0, 1, 2, 2, 0, 3, 4, 4, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,683 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['EKONOMIYA', 'SA', 'PILIPINAS', ',', 'NITUBO', 'OG', '7.4', '%', 'SA', '2ND', 'QUARTER', 'SA', '2022', 'Nitubo', 'og', '7.4', 'porsiyento', 'ang', 'ekonomiya', 'sa', 'Pilipinas', 'sa', 'ikaduhang', 'quarter', 'karong', 'tuiga', ',', 'sumala', 'pa', 'sa', 'datos', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', '.', 'Ang', 'gross', 'domestic', 'product', '(', 'GDP', ')', 'sa', 'nasud', ',', 'nimaba', 'gamay', 'gikan', 'sa', '8.2', 'porsiyento', 'nga', 'natala', 'sa', 'niaging', 'unang', 'quarter', 'sa', '2022.', 'Dugang', 'pa', 'sa', 'PSA', ',', 'mao', 'na', 'kini', 'ang', 'ikalimang', 'higayon', 'nga', 'nagpadayon', 'ang', 'pagtubo', 'sa', 'ekonomiya', 'sa', 'nasud', 'human', 'kini', 'nihagsa', 'niadtong', 'panahon', 'sa', 'pandemya', 'sa', 'COVID-19.', 'Gibanabana', 'sa', 'mga', 'economic', 'manager', 'nga', 'posibleng', 'magtubo', 'ang', 'GDP', 'sa', 'nasud', 'gikan', 'sa', '6.5', 'ngadto', 'sa', '7.5', 'porsiyento', 'kada', 'tuig.', 'Kini', 'taliwala', 'sa', 'inflation', 'nga', 'padayong', 'nasinati', 'sa', 'Pilipinas', '.'] 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, 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, 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, 3, 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, 5, 0] | cebuaner |
4,684 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PILIPINONG', 'SI', 'CARDINAL', 'TAGLE', ',', 'USA', 'SA', 'GIPANAN-AW', 'NGA', 'MAHIMONG', 'SUNOD', 'NGA', 'SANTO', 'PAPA', 'Usa', 'sa', 'duha', 'nga', 'nangunang', 'pilian', 'aron', 'mahimong', 'sunod', 'nga', 'Santo', 'Papa', 'si', 'Cardinal', 'Luis', 'Antonio', 'Tagle', 'kinsa', 'kanhing', 'archbishop', 'sa', 'Manila.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'Catholic', 'Herald', 'nga', 'na-post', 'sa', 'website', 'niini', 'niadtong', 'Agosto', '5', ',', '2022.', 'Ang', 'usa', 'sa', 'gipilian', 'mao', 'si', 'Hungarian', 'Cardinal', 'Peter', 'Erdo', 'kinsa', 'kanhing', 'archbishop', 'sa', 'Esztergom-Budapest.', 'Nigawas', 'ang', 'maong', 'balita', 'taliwala', 'sa', 'mga', 'espekulasyon', 'nga', 'hapit', 'na', 'nga', 'moretiro', 'si', 'Pope', 'Francis', 'kinsa', 'bag-ohay', 'lamang', 'naglisod', 'sa', 'paglakaw', 'tungod', 'sa', 'pagkapiang', 'sa', 'tuhod.', 'Sumala', 'pa', 'sab', 'sa', 'maong', 'report', ',', 'gipaboran', 'ni', 'Pope', 'Francis', 'si', 'Italian', 'Cardinal', 'Pietro', 'Parolin', 'kinsa', 'mao'y', 'kasamtangang', 'kalihim', 'sa', 'estado', 'sa', 'Vatican.', 'Bag-ohay', 'lamang', ',', 'gibutang', 'ni', 'Pope', 'Francis', 'si', 'Cardinal', 'Tagle', 'isip', 'usa', 'sa', '22', 'ka', 'myembro', 'sa', 'Congregation', 'for', 'Divine', 'Worship', 'and', 'the', 'Discipline', 'of', 'Sacraments', 'sa', 'Vatican', 'City', 'nga', 'gipanguluhan', 'ni', 'Cardinal', 'Arthur', 'Roche.', 'Niadtong', '2019', ',', 'gitudlo', 'sab', 'si', 'Cardinal', 'Tagle', 'isip', 'tinugyanan', 'sa', 'Congregation', 'for', 'the', 'Evangelization', 'of', 'Peoples', 'sa', 'Vatican.', 'Mao', 'kini', 'ang', 'nahimong', 'hinungdan', 'nga', 'nasuod', 'si', 'Cardinal', 'Tagle', 'ni', 'Pope', 'Francis', ',', 'sumala', 'pa', 'sa', 'Henrietta', 'de', 'Villa', 'kinsa', 'kanhing', 'ambasador', 'sa', 'Pilipinas', 'ngadto', 'sa', 'Vatican.', 'Dugang', 'pa', 'sa', 'Catholic', 'Herald', ',', 'aduna'y', 'dako', 'nga', 'bahin', 'ang', 'mosunod', 'nga', 'pope', 'alang', 'sa', 'maayong', 'kaugmaon', 'sa', 'Vatican', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 1, 2, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 1, 2, 0, 0, 0, 0, 5, 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, 1, 0, 7, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6] | cebuaner |
4,685 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['WALA', 'NA'Y', 'ATRASAY', ':', 'DEPED', ',', 'NIBAROG', 'NGA', 'MAGSUGOD', 'ANG', 'KLASE', 'KARONG', 'AGOSTO', '22', 'Nagpabiling', 'lig-on', 'ang', 'desisyon', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'nga', 'sugdan', 'ang', 'umalabot', 'nga', 'school', 'year', 'karong', 'August', '22', ',', '2022', 'bisan', 'paman', 'sa', 'mga', 'panawagan', 'nga', 'iuswag', 'ang', 'pag-abri', 'sa', 'klase.', 'Mao', 'kini', 'ang', 'gibutyag', 'ni', 'DepEd', 'spokesperson', 'Michael', 'Poa', 'niadtong', 'Lunes', ',', 'August', '8', ',', '2022.', 'Bag-ohay', 'lamang', ',', 'giawhag', 'sa', 'Teacher’s', 'Dignity', 'Coalition', 'ang', 'DepEd', 'nga', 'balhinon', 'ang', 'pag-abri', 'sa', 'klase', 'ngadto', 'sa', 'tunga-tunga', 'sa', 'September.', 'Gisubli', 'nila', 'nga', 'kulang', 'pa', 'ang', 'school', 'break', 'aron', 'tugutan', 'ang', 'mga', 'magtutudlo', 'nga', 'makarekober', 'sa', 'niaging', 'academic', 'year', 'ug', 'makapangandam', 'sa', 'mosunod', 'nga', 'klase.', 'Apan', 'sumala', 'pa', 'ni', 'Poa', ',', 'daghan', 'na', 'ang', 'ni-apil', 'sa', 'programang', 'Brigada', 'Eskwela', 'kung', 'diin', 'aduna'y', 'mga', 'stakeholder', 'ug', 'volunteers', 'nga', 'nitabang', 'aron', 'maandam', 'ang', 'mga', 'tunghaan', 'sa', 'mosunod', 'nga', 'school', 'year.', 'Dugang', 'pa', 'ni', 'POA', ',', 'nakigtinabangay', 'na', 'sab', 'ang', 'DepEd', 'sa', 'ubang', 'ahensiya', 'sa', 'gobyerno', ',', 'sama', 'sa', 'trade', 'department', 'aron', 'mapa-ubsan', 'ang', 'presyo', 'sa', 'mga', 'gamit', 'sa', 'eskwelahan', ',', 'ug', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'aron', 'mabakunahan', 'ang', 'mga', 'estudyante', 'ug', 'magtutudlo', 'batok', '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, 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, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 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, 1, 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, 7, 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
4,686 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPIN', 'P1.5', 'MILYON', 'SA', 'GITUOHANG', 'SHABU', ',', 'NASAKMIT', 'SA', 'KAPULISAN', 'SA', 'SIBULAN', 'Nasikop', 'sa', 'kapulisan', 'ang', 'usa', 'ka', 'lalake', 'kinsa', 'nakuhaan', 'sa', 'gituohang', 'ilegal', 'nga', 'drugas', 'nga', 'nagkantidad', 'og', 'kapin', 'P1.5', 'milyon', 'sa', 'Purok', '6', 'sa', 'Barangay', 'Maslog', 'sa', 'lungsod', 'sa', 'Sibulan', 'mga', 'alas', '10', 'sa', 'gabie', 'niadtong', 'Agosto', '8', ',', '2022.', 'Giila', 'ang', 'nadakpan', 'nga', 'si', 'Jemar', 'Casalta', ',', '36-anyos', ',', 'lumad', 'nga', 'taga', 'Dipolog', 'City', 'apan', 'kasamtangang', 'nagpuyo', 'sa', 'Villarosa', 'Subdivision', 'sa', 'naasoy', 'nga', 'barangay.', 'Nagpahigayon', 'og', 'buy-bust', 'operation', 'ang', 'kapulisan', 'sa', 'maong', 'adlaw', 'ug', 'lugar', 'ug', 'nasakmit', 'gikan', 'sa', 'suspek', 'ang', 'usa', 'ka', 'gamay', 'ug', 'lima', 'ka', 'dagko', 'nga', 'size', 'sa', 'heat', 'sealed', 'transparent', 'sachet', 'nga', 'aduna'y', 'sulod', 'sa', 'gituohang', 'shabu.', 'Aduna', 'kini', 'gibug-aton', 'nga', '233', 'gramos', 'nga', 'nagkantidad', 'og', 'P1,584,400.00.', 'Nakuha', 'sab', 'ang', 'usa', 'ka', 'tinuod', 'nga', 'P500', 'uban', 'sa', 'mga', 'bogus', 'o', 'peke', 'nga', 'kwarta', 'ug', 'gigamit', 'isip', 'buy-bust', 'money', ',', 'ug', 'duha', 'ka', 'P1,000', 'nga', 'gituohang', 'halin', 'sa', 'suspek', 'sa', 'pagpamaligya.', 'Gidala', 'na', 'ang', 'suspek', 'sa', 'Sibulan', 'MPS', 'alang', 'sa', 'tukmang', 'disposisyon', ',', 'samtang', 'gihatod', 'na', 'sa', 'NOPFU', 'ang', 'drug', 'evidence', 'alang', 'sa', 'forensic', 'examination.', 'Giandam', 'na', 'sab', 'ang', 'mga', 'documentary', 'requirements', 'alang', 'sa', 'inquest', 'proceeding', '.'] 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, 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, 1, 2, 0, 0, 0, 0, 0, 0, 5, 6, 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, 5, 6, 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 |
4,687 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daw', 'nahimong', 'suba', 'kini', 'nga', 'dalan', 'sa', 'Colo', ',', 'Guihulngan', 'City', 'kagabii', ',', 'Agosto', '8', ',', '2022', 'human', 'nga', 'kini', 'gibaha', 'tungod', 'sa', 'walay', 'puas', 'nga', 'ulan', 'nga', 'dala', 'sa', 'Hanging', 'Habagat', '.'] 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, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,688 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'klase', 'sa', 'tanang', 'mga', 'pampubliko', 'ug', 'pribadong', 'eskuwelahan', 'sa', 'tibuok', 'Guihulngan', 'City', 'karong', 'adlawa', ',', 'Agosto', '9', ',', '2022.', 'Kini', 'subay', 'sa', 'kamanduan', 'kon', 'executive', 'order', 'nga', 'giluwatan', 'ni', 'Acting', 'Mayor', 'Ana', 'Eunica', 'Beatriz', 'Reyes.', 'Gisuspenso', 'ang', 'klase', 'sa', 'Guihulngan', 'City', 'tungod', 'sa', 'mga', 'pagbaha', 'didto', 'gumikan', 'sa', 'kusog', 'nga', 'ulan', 'nga', 'dala', 'sa', 'Hanging', 'Habagat', '.'] 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,689 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P8', 'NGA', 'PLETE', 'SA', 'FLIGHTS', 'SA', 'ILANG', '8.8', 'SALE', 'Gianunsyo', 'sa', 'Cebu', 'Pacific', 'ang', 'ilang', 'dugang', 'nga', 'mga', 'seat', 'sale', 'alang', 'sa', 'domestic', 'ug', 'international', 'nga', 'mga', 'destinasyon.', 'Gitanyag', 'nila', 'ang', '"', 'GR8', '8.8', '"', 'seat', 'sale', 'nga', 'magsugod', 'karong', 'August', '8', 'hangtod', 'August', '10', ',', '2022', 'nga', 'aduna'y', 'byahe', 'nga', 'ingon', 'kaubos', 'sa', 'P8', 'nga', 'one-way', 'base', 'fare.', 'Ang', 'travel', 'period', 'sa', 'maong', 'seat', 'sale', 'gikan', 'September', '1', ',', '2022', 'hangtod', 'February', '28', ',', '2023.', 'Apil', 'sa', 'domestic', 'destinations', 'ang', 'Boracay', ',', 'Bohol', ',', 'Siargao', ',', 'Palawan', ',', 'Cebu', ',', 'Negros', ',', 'Pampanga', 'ug', 'daghan', 'pa.', 'Samtang', 'apil', 'sa', 'international', 'destinations', 'ang', 'Indonesia', ',', 'Malaysia', ',', 'Vietnam', ',', 'Bangkok', ',', 'South', 'Korean', 'ug', 'Singapore.', 'Mahimong', 'mo-booked', 'sa', 'seat', 'sale', 'flights', 'ug', 'flight', 'pass', 'pinaagi', 'sa', 'Cebu', 'Pacific', 'website', '.'] 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0] | cebuaner |
4,690 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUL-AN', '3', 'MILYON', 'KA', 'PILIPINO', ',', 'WALAY', 'TRABAHO', 'Dul-an', 'sa', '2.99', 'milyon', 'ka', 'mga', 'Pilipino', 'ang', 'wala'y', 'trabaho', 'sa', 'bulan', 'sa', 'Hunyo', ',', 'samtang', 'nag-steady', 'ngadto', 'sa', '6', '%', 'ang', 'unemployment', 'rate', 'sa', 'Pilipinas', 'sugod', 'niadtong', 'Mayo', 'ning', 'tuiga.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'Lunes', ',', 'Agosto', '8', ',', '2022.', 'Sumala', 'pa', 'sa', 'PSA', ',', 'anaa', 'sab', 'sa', '6', '%', 'ang', 'unemployment', 'rate', 'niadtong', 'Mayo', 'apan', '2.93', 'milyon', 'lamang', 'ka', 'mga', 'trabahante', 'ang', 'giisip', 'nga', 'wala'y', 'trabaho.', 'Bisan', 'paman', ',', 'nitaas', 'ngadto', 'sa', '508,000', 'ang', 'mga', 'indibidwal', 'nga', 'nakatrabaho', 'niadtong', 'Hunyo', 'kung', 'ikompara', 'niadtong', 'Mayo.', 'Matod', 'pa', 'ni', 'National', 'Statistician', 'Dennis', 'Mapa', ',', 'anaa', 'sa', '94', '%', 'ang', 'employment', 'rate', 'niadtong', 'Hunyo', '2022', 'nga', 'katumbas', 'sa', '46.59', 'milyon', 'ka', 'mga', 'Pilipino', '.'] 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, 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, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
4,691 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MANGINGISDA', 'SA', 'MANJUYOD', ',', 'NALUMOS', 'PATAY', 'Patay', 'ang', 'usa', 'ka', 'lalake', 'human', 'malumos', 'sa', 'Sitio', 'Limayag', 'sa', 'Barangay', 'Bolisong', 'sa', 'lungsod', 'sa', 'Manjuyod', 'mga', '7:40', 'sa', 'gabie', 'niadtong', 'Agosto', '6', ',', '2022.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Nelger', 'Aboy', ',', '31-anyos', ',', 'usa', 'ka', 'mangingisda', ',', 'minyo', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'kapulisan', ',', 'aduna'y', 'usa', 'ka', 'mangingisda', 'ang', 'nakakita', 'pa', 'sa', 'biktima', 'kinsa', 'paingon', 'sa', 'naasoy', 'nga', 'lugar', 'aron', 'mamana.', 'Nakit-an', 'pa', 'sab', 'siya', 'sa', 'gabie', 'sa', 'maong', 'adlaw', 'apan', 'pagkataod-taod', ',', 'wala', 'na', 'namatikdan', 'ang', 'biktima.', 'Nagpahigayon', 'og', 'rescue', 'operation', 'ang', 'LGU', 'rescue', 'team', 'ug', 'narekober', 'ang', 'patay'ng', 'lawas', 'sa', 'biktima', 'nga', 'anaa', 'sa', 'ilalom', 'sa', 'tubig', 'mga', '8:30', 'na', 'sa', 'buntag', 'niadtong', 'Agosto', '7', ',', '2022.', 'Gidala', 'ang', 'patay'ng', 'lawas', 'sa', 'biktima', 'ngadto', 'sa', 'Manjuyod', 'Municipal', 'Morgue', 'alang', 'sa', 'post-mortem', 'examination', '.'] 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
4,692 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPIN', '3,000', 'KA', 'AFTERSHOCKS', ',', 'NATALA', 'SA', 'PHIVOLCS', 'HUMAN', 'ANG', 'KUSOG', 'NGA', 'LUZON', 'EARTHQUAKE', 'Nakatala', 'ang', 'Philippine', 'Institute', 'of', 'Volcanology', 'and', 'Seismology', '(', 'PHIVOLCS', ')', 'og', '3,235', 'ka', 'mga', 'linog', 'nga', 'gikonsiderar', 'nga', 'aftershocks', 'human', 'sa', 'magnitude', '7.0', 'nga', 'linog', 'nga', 'niigo', 'sa', 'Luzon', 'niadtong', 'July', '27', ',', '2022.', 'Na-record', 'ang', '3,235', 'ka', 'mga', 'aftershocks', 'sugod', 'July', '27', ',', '2022', 'hangtod', 'August', '8', ',', '2022', 'kung', 'diin', '63', 'niini', 'ang', 'nabati', 'nga', 'aduna'y', 'magnitude', 'range', 'anaa', 'sa', '1.4', 'hangtod', '5.1.', 'Kadaghanan', 'sa', 'mga', 'aftershocks', ',', 'natala', 'sa', 'mga', 'probinsya', 'sa', 'Abra', 'ug', 'Ilocos', 'Sur', 'nga', 'parehong', 'nakaangkon', 'og', 'grabeng', 'kadaot', 'tungod', 'sa', 'linog', '.'] 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, 3, 0, 0, 0, 0, 7, 8, 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, 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, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,693 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'kompanya', 'sa', 'petrolyo', 'nagpahibalo', 'nga', 'aduna'y', 'rollback', 'sa', 'presyo', 'sa', 'gas', ',', 'epektibo', 'karong', 'Martes', ',', 'August', '9', ',', '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] | cebuaner |
4,694 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nangita', 'karon', 'ang', 'usa', 'ka', 'online', 'candy', 'shop', 'og', 'usa', 'ka', 'professional', 'taster', 'aron', 'motilaw', 'sa', 'ilang', 'mga', 'produkto.', 'Sumala', 'pa', 'sa', 'tindahang', 'Candy', 'Funhouse', ',', 'nagtanyag', 'kini', 'og', 'suweldo', 'nga', '100,000', 'Canadian', 'dollars', ',', 'kon', 'P4', 'million', ',', 'sa', 'masuwerte', 'nga', 'makuha', 'sa', 'maong', 'trabaho.', 'Matud', 'pa', 'sa', 'tigpama-ba', 'sa', 'Candy', 'Funhouse', 'nga', 'si', 'Vanessa', 'Janakijevski-Rebelo', ',', '3', 'ang', 'ilang', 'original', 'taste', 'testers', 'nga', 'ilang', 'gitawag', 'nga', '"', 'Candyologists.', '"', 'Sa', 'pagkakaron', ',', 'nangita', 'sila', 'og', 'Chief', 'Candy', 'Office', 'kinsa', 'modumala', 'sa', 'maong', 'mga', 'Candyologists', 'aron', 'masiguro', 'nga', 'lami', 'ug', 'tam-is', 'ang', 'mga', 'produkto', 'sa', 'Candy', 'Funhouse.', 'Lakip', 'sab', 'sa', 'mga', 'responsibilidad', 'sa', 'Chief', 'Candy', 'Officer', 'mao', 'ang', 'pag-aprubar', 'sa', 'mga', 'bag-ong', 'produkto', 'ingon', 'man', 'ang', 'mahimong', 'usa', 'ka', '"', 'chief', 'taster.', '"', 'Gipangita', 'nila', 'karon', 'ang', 'usa', 'ka', 'aplikante', 'nga', 'dunay', '"', 'sweet', 'tooth', '"', 'kun', 'hilig', 'gyud', 'mokaon', 'og', 'candy.', 'Ang', 'mapili', 'nga', 'aplikante', ',', 'moagi', 'pa', 'og', 'pagbansay-bansay', 'sa', 'pagtilaw', 'sa', 'candy.', 'Makadawat', 'sab', 'sila', 'og', 'halapad', 'nga', 'dental', 'health', 'benefits', '.'] 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, 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, 5, 6, 0, 0, 1, 2, 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, 7, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,695 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitanyag', 'karon', 'ang', 'bugas', 'sa', 'presyong', 'P20', 'kada', 'kilo', 'sa', 'lungsod', 'sa', 'Botolan', ',', 'Zambales.', 'Kini', 'pinaagi', 'sa', 'usa', 'ka', 'programa', 'nga', 'gilusad', 'sa', 'kagamhanang', 'lokal', 'sa', 'naasoy', 'nga', 'lungsod.', 'Ubos', 'sa', 'rice', 'subsidy', 'program', 'ni', 'Botolan', 'Mayor', 'Omar', 'Jun', 'Ebdane', ',', 'duna', 'pay', 'pakapin', 'nga', 'libreng', 'usa', 'ka', 'kilo', 'kung', 'lima', 'ka', 'kilo', 'nga', 'bugas', 'ang', 'paliton.', 'Nagsugod', 'ang', 'naasoy', 'nga', 'programa', 'niadtong', 'Hulyo', '12', 'og', 'mahuman', 'kini', 'karong', 'Setyembre', '29', ',', '2022.', 'Gimanduan', 'na', 'sa', 'kagamhanang', 'lungsod', 'sa', 'Botolan', 'ang', 'mga', 'kabarangayan', 'didto', 'pagbaligya', 'sa', 'barato', 'nga', 'bugas', 'aron', 'mas', 'daghan', 'kunong', 'pamilya', 'ang', 'makapahimulos', 'niini.', 'Mahinumduman', 'nga', 'niadtong', 'panahon', 'sa', 'kampanya', 'karong', 'tuiga', ',', 'nisaad', 'si', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'paubsan', 'sa', 'iyang', 'administrasyon', 'ang', 'presyo', 'sa', 'bugas', 'ngadto', 'sa', 'P20', 'kada', 'kilo.', 'Apan', 'gisupak', 'kini', 'sa', 'grupong', 'Bantay', 'Bigas', 'niadtong', 'Hunyo', 'ug', 'niingong', 'imposible', 'kuno', 'ang', 'maong', 'saad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 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, 1, 2, 2, 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] | cebuaner |
4,696 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaubos', 'na', 'sa', 'nagkalain-laing', 'heavy', 'rainfall', 'warning', 'ang', 'Negros', 'Oriental', 'ug', 'mga', 'kasikbit', 'nga', 'probinsya', 'niini', 'tungod', 'sa', 'walay', 'puas', 'nga', 'ulan', 'nga', 'dala', 'gihapon', 'sa', 'Hanging', 'Habagat', '(', 'southwest', 'monsoon', ')', '.', 'Anaa', 'na', 'karon', 'sa', 'ORANGE', 'WARNING', 'ang', 'tungang', 'bahin', 'sa', 'Negros', 'Oriental', ',', 'ingon', 'man', 'ang', 'mga', 'lalawigan', 'sa', 'Siquijor', 'ug', 'habagatang', 'Sugbo.', 'Gipaubos', 'sab', 'sa', 'YELLOW', 'WARNING', 'ang', 'habagatang', 'bahin', 'sa', 'Negros', 'Oriental.', 'Gipahimangno', 'ang', 'mga', 'lumolupyo', 'sa', 'mga', 'lugar', 'ubos', 'niini', 'nga', 'mga', 'warning', 'nga', 'magmatngon', 'sa', 'posibleng', 'kusog', 'nga', 'pagbaha', 'ug', 'pagdahili', 'sa', 'yuta', 'kon', 'landslides', ',', 'ilabi', 'na', 'sa', 'mga', 'bukirang', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 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] | cebuaner |
4,697 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Baha', 'sa', 'Dumaguete', 'City', 'tungod', 'sa', 'mga', 'pag-ulan', 'nga', 'dala', 'sa', 'LPA', 'ug', 'Habagat.', 'Live', 'karon', 'ang', 'atong', 'Silliman', 'University', 'intern', 'nga', 'si', 'Jan', 'Aarron', 'Dela', 'Torre', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2, 2, 2, 0] | cebuaner |
4,698 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sitwasyon', 'sa', 'pipila', 'ka', 'dalan', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'adlawa', 'Agosto', '7', ',', '2022', ',', 'human', 'nga', 'kini', 'gibaha', 'tungod', 'sa', 'walay', 'puas', 'nga', 'ulan', 'nga', 'dala', 'sa', 'LPA', 'ug', 'Hanging', 'Habagat'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,699 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'CITY', ',', 'GIBAHA', 'Napuno', 'sa', 'baha', 'karon', 'ang', 'national', 'highway', 'duol', 'sa', 'Freedom', 'Park', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'tungod', 'sa', 'kusog', 'nga', 'ulan', 'nga', 'nibundak', 'karong', 'adlawa', ',', 'Agosto', '7', ',', '2022.', 'Ang', 'kusog', 'nga', 'ulan', ',', 'epekto', 'sa', 'Low', 'Pressure', 'Area', '(', 'LPA', ')', 'ug', 'Hanging', 'Habagat', '(', 'southwest', 'monsoon', ')', '.'] 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, 6, 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] | cebuaner |
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