Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
|---|---|---|---|
4,500 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'GIHATAGAN', 'OG', 'AWARD', 'ISIP', ''MOST', 'IMPROVED', ''', 'COMPONENT', 'CITY', 'SA', 'PILIPINAS', 'Giila', 'ang', 'Dumaguete', 'City', 'isip', ''Most', 'Improved', ''', '(', 'Ranked', '1', ')', 'sa', 'mga', 'component', 'city', 'sa', 'Pilipinas', 'atol', 'sa', '10th', 'Cities', 'and', 'Municipalities', 'Competitiveness', 'Summit', 'sa', 'Philippine', 'International', 'Convention', 'Center.', 'Gidawat', 'nila', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo', ',', 'Acting', 'Vice-Mayor', 'Karissa', 'Tolentino-Maxino', ',', 'Councilor', 'Dionie', 'Amores', 'ug', 'DTI-Negros', 'Oriental', 'Provincial', 'Director', 'Nimfa', 'Virtucio', 'ang', 'award', 'sa', 'ngalan', 'sa', 'City', 'Government', 'sa', 'Dumaguete.', 'Usa', 'ang', 'Cities', 'and', 'Municipalities', 'Competitiveness', 'Summit', 'sa', 'DTI', 'Competitiveness', 'Bureau’s', 'Programs', 'nga', 'nag-awhag', 'sa', 'mga', 'LGU', 'sa', 'pagtigom', 'ug', 'pagsumite', 'sa', 'mga', 'datos', 'nga', 'magsilbing', 'basehan', 'sa', 'ilang', 'mga', 'marka', 'ug', 'ranggo.', 'Karong', 'tuiga', ',', 'aduna'y', 'tema', 'ang', 'summit', 'nga', '"', 'A', 'Decade', 'of', 'Excellence', ':', 'Championing', 'Innovation', 'to', 'Sustain', 'Competitiveness', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 7, 8, 8, 8, 8, 0, 5, 6, 6, 6, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 3, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 7, 8, 8, 8, 8, 0, 7, 8, 8, 8, 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 |
4,501 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BRGY.', 'KAGAWAD', 'GIPUSIL', 'PATAY', 'SA', 'BAYAWAN', 'CITY', 'Patay', 'ang', 'usa', 'ka', 'Barangay', 'Kagawad', 'human', 'siya', 'gipusil', 'sa', 'wala', 'pa', 'mailhi', 'nga', 'suspek', 'sa', 'Sitio', 'Cabcabon', ',', 'Barangay', 'Banga', ',', 'Bayawan', 'City', 'mga', '12:30', 'sa', 'udto', 'niadtong', 'Biyernes', ',', 'Oktubre', '21', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Diosdado', 'Gemina', ',', '52-anyos', ',', 'Barangay', 'Kagawad', 'sa', 'Barangay', 'San', 'Miguel', ',', 'Bayawan', 'City', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'report', ',', 'ang', 'suspek', 'nakasuot', 'og', 'jacket', 'ug', 'itom', 'nga', 'ball', 'cap.', 'Dali', 'sab', 'kini', 'nga', 'nisibat', 'sakay', 'sa', 'motorsiklo', 'human', 'sa', 'maong', 'insidente.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'aron', 'masikop', 'ang', 'suspek', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 5, 6, 6, 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] | cebuaner |
4,502 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'MAG-UUMA', 'SA', 'DUMAGUETE', 'NAKADAWAT', 'OG', 'BAG-ONG', 'KAGAMITAN', 'ARON', 'MAPAUSBAW', 'ANG', 'PRODUKSYON', 'SA', 'BUGAS', 'Nakadawat', 'ang', 'mga', 'mag-uuma', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'og', 'mga', 'makinarya', 'sa', 'pagpanguma', 'isip', 'tabang', 'sa', 'ilang', 'asosasyon', 'pinaagi', 'sa', 'usa', 'ka', 'sa', 'turnover', 'ceremeny', 'sa', 'Covered', 'Court', 'sa', 'Barangay', 'Banilad.', 'Gihatag', 'ang', 'assistance', 'package', 'nga', 'naglakip', 'sa', 'upat', 'ka', 'mga', 'unit', 'sa', 'rice', 'tillers', 'ug', 'upat', 'ka', 'mga', 'unit', 'sa', 'hand', 'tractors', 'ngadto', 'sa', 'mga', 'miyembro', 'sa', 'Dumaguete', 'Rice', 'Farmers.', 'Gitunol', 'kini', 'nila', 'ni', 'Assistant', 'City', 'Administrator', 'Dr.', 'Dinno', 'T.', 'Depositario', '(', 'representing', 'Mayor', 'Felipe', 'Antonio', 'Remollo', ')', 'ug', 'Councilor', 'Franklin', 'D.', 'Esmeña', 'Jr.', 'uban', 'sa', 'Department', 'of', 'Agriculture', 'pinaagi', 'sa', 'Bottom-up', 'Budgeting', 'BUB', 'Project.', 'Subay', 'kini', 'sa', 'tumong', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'nga', 'madungagan', 'ang', 'produksyon', 'sa', 'bugas', 'aron', 'mahimong', 'suporta', 'sa', 'ilang', 'panginabuhian', 'ug', 'pagpausbaw', 'sa', 'food', 'security', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 2, 2, 2, 0, 0, 3, 4, 4, 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] | cebuaner |
4,503 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['74.2', 'MILYON', 'KA', 'MGA', 'PINOY', 'ANG', 'NAKAREHISTRO', 'SA', 'NATIONAL', 'ID', ';', '22', 'MILYON', 'ANG', 'NAHIMO', ',', '17.6', 'MILYON', 'ANG', 'NAHATOD', 'Anaa', 'na', 'sa', 'kinatibuk-ang', '74.2', 'milyon', 'ka', 'mga', 'Pilipino', 'ang', 'nakarehistro', 'sa', 'National', 'ID', ',', 'diin', 'nakahimo', 'na', 'og', '22', 'milyon', 'ka', 'mga', 'card', 'ug', '17.6', 'milyon', 'na', 'sab', 'ang', 'nahatod', 'sa', 'Philpost.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Statistic', 'Authority', '(', 'PSA', ')', 'niadtong', 'Biyernes', ',', 'Oktubre', '21', ',', '2022.', 'Sumala', 'pa', 'ni', 'Fred', 'Sollesta', ',', 'director', 'sa', 'PSA', 'Civil', 'Registration', 'System', '-', 'Information', 'Technology', 'Project', ',', 'nakigtambayayong', 'na', 'sila', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', '(', 'BSP', ')', 'aron', 'makahimo', 'og', 'dugang', 'nga', 'electronic', 'ug', 'physical', 'copies', 'sa', 'Philippine', 'National', 'ID', 'sa', 'katapusan', 'ning', 'tuiga.', 'Gipasabot', 'sab', 'ni', 'Sollestra', 'nganong', 'aduna'y', 'pagkalangan', 'sa', 'pag-print', 'sa', 'National', 'ID.', 'Hinuon', ',', 'gisubli', 'niya', 'nga', 'aduna'y', 'pamaagi', 'aron', 'ma-track', 'sa', 'publiko', 'ang', 'status', 'sa', 'ilang', 'National', 'ID', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 0, 0, 0, 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, 3, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
4,504 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LA', 'UNION', 'GOV'T', 'TUGUTAN', 'ANG', 'MGA', 'BABAYING', 'EMPLEYADO', 'NGA', 'MO-WFH', 'ATOL', 'SA', 'ILANG', '"', 'PERIOD', 'DAYS', '"', 'Tugutan', 'ang', 'mga', 'babayi', 'nga', 'nagtrabaho', 'sa', 'La', 'Union', 'provincial', 'government', 'nga', 'mag-work', 'from', 'home', '(', 'WFH', ')', 'sa', 'ilang', 'mga', 'adlaw', 'sa', 'pagregla', ',', 'matod', 'pa', 'sa', 'usa', 'ka', 'executive', 'order', '(', 'EO', ')', 'nga', 'gipagawas', 'ni', 'Governor', 'Raphaelle', 'Veronica', 'Ortega-David.', 'Sa', 'usa', 'ka', 'pahayag', ',', 'gisubli', 'sa', 'gobernador', 'nga', 'tagaan', 'og', '"', 'menstrual', 'privileges', '"', 'nga', 'duha', 'ka', 'adlaw', 'matag', 'bulan', 'nga', 'WFH', 'arrangement', 'sa', 'ilang', '"', 'period', 'days', '"', 'kadtong', 'mga', 'babayi', 'nga', 'empleyado.', 'Sumala', 'pa', 'ni', 'Ortega-David', ',', 'tumong', 'sa', 'maong', 'lakang', 'ang', 'pag-', '"', 'spread', 'awareness', 'and', 'be', 'kinder', 'to', 'our', 'female', 'employees', 'during', 'their', 'period', 'days.', '"', '.', 'Ilalom', 'sa', 'maong', 'EO', ',', 'tugutan', 'sab', 'ang', 'probisyon', 'nga', '"', 'menstrual', 'kits', '"', 'sa', 'matag', 'opisina', 'sa', 'provincial', 'government.', 'Matod', 'pa', 'ni', 'Ortega-David', ',', 'nahimo', 'sa', 'iyang', 'administrasyon', 'ang', 'bag-ong', 'palisiya', 'human', 'niya', 'paminawa', 'ang', 'gikinahanglan', 'sa', 'mga', 'empleyado', 'sa', 'provincial', 'government', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 7, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 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] | cebuaner |
4,505 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidayeg', 'sa', 'mga', 'netizens', 'ang', 'artist', 'nga', 'si', 'Nestor', 'Abayon', 'Jr.', 'sa', 'Rizal', 'Occidental', 'Mindoro', 'tungod', 'sa', 'iyang', 'painting', 'nga', 'usa', 'hyper-realistic', 'portrait.', 'Ang', 'realistic', 'oil', 'painting', 'aduna'y', 'title', 'nga', '"', 'The', 'Last', 'Harvest.', '"', 'Naghulagway', 'kini', 'sa', 'usa', 'ka', 'mag-uuma', 'kinsa', 'nawad-an', 'og', 'trabaho', 'tungod', 'sa', 'pagsaka', 'sa', 'presyo', 'sa', 'mga', 'palaliton', 'ug', 'pag-us-os', 'sa', 'presyo', 'sa', 'bugas', 'nga', 'hinungdan', 'nga', 'wala', 'na'y', 'makaon', 'ang', 'iyang', 'mga', 'anak', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 5, 6, 6, 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, 0, 0, 0, 0, 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 |
4,506 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gideklarar', 'na', 'sa', 'PAGASA', 'ang', 'pagsugod', 'sa', 'northeast', 'monsoon', 'o', 'amihan', 'season', 'niadtong', 'Huwebes', ',', 'Oktubre', '20', ',', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,507 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DISNEY+', ',', 'AVAILABLE', 'NA', 'SA', 'PILIPINAS', 'SUGOD', 'KARONG', 'NOBYEMBRE', '17', ',', '2022', 'Ma-access', 'na', 'sa', 'Pilipinas', 'ang', 'streaming', 'service', 'nga', 'Disney+', 'gikan', 'Nobyembre', '17', ',', 'mao', 'kini', 'ang', 'gianunsyo', 'sa', 'Walt', 'Disney', 'Company', '(', 'Southeast', 'Asia', ')', 'Ltd.', 'karong', 'adlawa', ',', 'Oktubre', '20', ',', '2022.', 'Gihulagway', 'sa', 'Disney+', 'ang', 'kaugalingon', 'niini', 'isip', 'usa', 'ka', 'gipahinungod', 'nga', 'streaming', 'home', 'alang', 'sa', 'movies', 'ug', 'TV', 'shows', 'gikan', 'sa', 'iconic', 'brands', 'sa', 'Disney', 'lakip', 'na', 'ang', 'Disney', ',', 'Pixar', ',', 'Marvel', ',', 'Star', 'Wars', 'ug', 'National', 'Geographic', 'Sugod', 'sunod', 'bulan', ',', 'mahimong', 'moapil', 'ang', 'mga', 'konsumidor', 'sa', 'Pilipinas', 'sa', 'Disney+', 'streaming', 'service', 'uban', 'sa', 'pagpaila', 'sa', 'subsciption', 'plans', 'nga', 'maghatag', 'og', 'daghang', 'kapilian.', 'Magtanyag', 'sab', 'kini', 'og', 'flexibility', 'uban', 'sa', 'nagkalain-laing', 'subscription', 'options', 'nga', 'mohaum', 'sa', 'panginahanglan', 'sa', 'mga', 'konsumidor', ',', 'apil', 'na', 'ang', ':', 'Magamit', 'ang', 'Disney+', 'sa', 'lapad', 'nga', 'kapilian', 'sa', 'mobile', 'o', 'television', 'nga', 'sakop', 'sa', 'napili', 'nga', 'subscription', 'plans', 'sa', 'tiggamit', ',', 'lakip', 'na', 'ang', 'smartphones', 'ug', 'tablets', '(', 'Android', 'or', 'iOS', ')', ',', 'smart', 'TVs', 'sama', 'sa', 'Samsung', 'ug', 'LG', 'ug', 'connected', 'TV', 'devices', ',', 'apil', 'na', 'ang', 'Google', 'TV', 'ug', 'uban', 'pang', 'Android', 'TV', 'OS', ',', 'Apple', 'TV', '4K', 'ug', 'Apple', 'TV', 'HD', 'ug', 'Chromecast'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 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, 3, 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, 7, 0, 7, 0, 7, 0, 7, 8, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 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, 7, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 7, 8, 8, 0, 7, 8, 8, 0, 7, 8, 8, 0, 7] | cebuaner |
4,508 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipagawas', 'na', 'sa', 'lokal', 'nga', 'kagamhanan', 'sa', 'Negros', 'Oriental', 'ang', 'official', 'video', 'alang', 'sa', 'Buglasan', '2022', 'nga', 'gilaumang', 'magsugod', 'karong', 'Biyernes', ',', 'Oct.', '21.', 'Karong', 'tuiga', 'gibalik', 'na', 'gyud', 'ang', 'naandan', 'nga', 'Buglasan', 'Festival', ',', 'human', 'kini', 'gikanselar', 'sa', 'niaging', 'duha', 'ka', 'tuig', '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, 5, 6, 0, 0, 0, 0, 0, 7, 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, 7, 0] | cebuaner |
4,509 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mosunod', 'mao', 'ang', 'schedule', 'sa', 'mga', 'kalihokan', 'sa', 'Buglasan', 'Festival', 'karon', 'Oktuber', '17-30', ',', '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. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0] | cebuaner |
4,510 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPITAN', 'SA', 'PIAPI', ',', 'NIRESIGN', 'ARON', 'MAKAGAHIN', 'OG', 'PANAHON', 'ALANG', 'SA', 'PAMILYA', 'Ni-resign', 'si', 'Mr.', 'Clark', 'L.', 'Labi', 'isip', 'Punong', 'Barangay', 'sa', 'Piapi', ',', 'apan', 'nipasalig', 'nga', 'padayon', 'siya', 'nga', 'mosuporta', 'sa', 'iyang', 'pinalanggang', 'barangay.', 'Sumala', 'pa', 'ni', 'Labi', 'sa', 'usa', 'ka', 'Facebook', 'post', ',', 'gihimo', 'kini', 'niya', 'nga', 'desisyon', 'tungod', 'kinahanglan', 'sa', 'iyang', 'pamilya', 'ang', 'iyang', 'presensya', 'ngadto', 'sa', 'Canada.', 'Gisubli', 'sab', 'niya', 'nga', 'kinahanglan', 'sa', 'iyang', 'pamilya', 'ang', 'iyang', 'suporta', 'ug', 'giya.', 'Dugang', 'pa', 'niya', ',', 'mingawon', 'siya', 'sa', 'tanang', 'opisyal', 'ug', 'empleyado', 'sa', 'gobyerno', 'nga', 'iyang', 'nakatrabaho.', 'Nipadayag', 'sab', 'siya', 'sa', 'iyang', 'pagpasalamat', 'sa', 'pagsabot', 'bahin', 'sa', 'iyang', 'desisyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 2, 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, 7, 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] | cebuaner |
4,511 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['1,000', 'KA', 'RESIDENTE', 'SA', 'HIMAMAYLAN', ',', 'WALA', 'GIHAPON', 'KAULI', 'TUNGOD', 'SA', 'ENGKWENTRO', 'SA', 'MILITAR', 'UG', 'NPA', 'DIDTO', 'Ubos', 'nalang', 'sa', '1,000', 'ka', 'mga', 'nibakwit', 'nga', 'residente', 'ang', 'nagpabilin', 'sa', 'evacuation', 'centers', 'sa', 'Himamaylan', 'City', ',', 'Negros', 'Occidental.', 'Mao', 'kini', 'ang', 'gibutyag', 'ni', 'Mayor', 'Raymund', 'Tongson', 'niadtong', 'Lunes', ',', 'Oktubre', '17', ',', '2022.', 'Gitugutan', 'na', 'nga', 'makapauli', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'panimalay', 'ang', 'mga', 'nibakwit', 'nga', 'lumolupyo', 'sa', 'Barangay', 'Cabadiangan', 'ug', 'Carabalan', ',', 'gawas', 'sa', 'mga', 'residente', 'sa', 'Sitio', 'Campayas', ',', 'Medel', 'ug', 'Sig-an', 'sa', 'Carabalan.', 'Mao', 'kini', 'sila', 'ang', 'mga', 'nibakwit', 'sugod', 'niadtong', 'Oktubre', '6', 'tungod', 'sa', 'engkwentro', 'tali', 'sa', 'gobyerno', 'ug', 'pwersa', 'sa', 'mga', 'rebelde.', 'Gimandoan', 'na', 'sab', 'ni', 'Mayor', 'Tongson', 'ang', 'pagbalik', 'sa', 'klase', 'sa', 'Barangay', 'Cabadiangan', 'ug', 'Carabalan', 'karong', 'Miyerkules', ',', 'Oktubre', '19', ',', '2022.', 'Walo', 'ka', 'mga', 'engkwentro', 'ang', 'nahitabo', 'sa', 'Carabalan', 'sukad', 'niadtong', 'Oktubre', '6', 'hinungdan', 'sa', 'pagkapatay', 'sa', 'usa', 'ka', 'giingong', 'taas', 'nga', 'lider', 'sa', 'rebelde', 'ug', 'duha', 'ka', 'sundalo', '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, 0, 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, 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, 5, 6, 0, 5, 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, 1, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,512 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', 'EXCITED', 'NA', 'PUD', 'BA', 'KA', 'SA', 'BUGLASAN', '?', 'Nagsugod', 'na', 'sa', 'pagtukod', 'ang', 'mga', 'Local', 'Government', 'Units', '(', 'LGU', ')', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'booth', 'alang', 'sa', 'pagbukas', 'sa', 'Buglasan', 'Festival', 'karong', 'Biyernes', ',', 'Oktubre', '21', ',', '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, 7, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,513 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ni-pose', 'alang', 'sa', 'ilang', 'unang', 'litrato', 'ang', 'asawa', 'ni', 'Justin', 'Bieber', 'nga', 'si', 'Hailey', 'Bieber', 'ug', 'ex', 'nga', 'si', 'Selena', 'Gomez', 'atol', 'sa', '2022', 'Academy', 'Museum', 'Gala', 'sa', 'Los', 'Angeles', ',', 'nga', 'makapahunong', 'sa', 'mga', 'balita', 'nga', 'aduna'y', 'panagbingkil', 'sa', 'duha', 'ka', 'kampo'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,514 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Video', 'sa', 'pagluwas', 'sa', 'PNP', 'Claveria', 'sa', 'usa', 'ka', 'residente', 'nga', 'na-trap', 'sa', 'sulod', 'sa', 'iyang', 'panimalay', 'sa', 'Barangay', 'Dibalio', ',', 'Claveria', ',', 'Cagayan', 'atol', 'sa', 'taas', 'nga', 'pagbaha', 'tungod', 'sa', 'severe', 'tropical', 'storm', 'nga', '#', 'NenengPH', 'karong', 'Dominggo.', 'Nagpadayon', 'pa', 'sab', 'ang', 'search', 'and', 'rescue', 'operations', 'sa', 'uban', 'pang', 'stranded', 'nga', 'mga', 'residente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 6, 6, 6, 6, 6, 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] | cebuaner |
4,515 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['1,747', 'DENGUE', 'CASES', 'NATALA', 'SA', 'NEGOR', ';', '8', 'NGA', 'NAMATAY', 'Nakatala', 'og', 'kinatibuk-ang', '1,747', 'ka', 'mga', 'kaso', 'sa', 'dengue', 'ang', 'probinsya', 'sa', 'Negros', 'Oriental', 'gikan', 'sa', 'nagkalain-laing', 'disease', 'reporting', 'units', '(', 'DRUs', ')', 'alang', 'sa', 'panahon', 'sa', 'Enero', '1', 'hangtod', 'Oktubre', '1', ',', '2022.', 'Walo', 'sa', 'maong', 'mga', 'kaso', 'ang', 'natala', 'nga', 'namatay.', 'Mas', 'taas', 'kini', 'og', '203', '%', 'kung', 'itandi', 'sa', 'samang', 'panahon', 'sa', 'niaging', 'tuig', 'nga', 'aduna'y', '576', 'cases', 'ug', '0', 'deaths.', 'Anaa', 'sa', '0-81', 'anyos', 'ang', 'edad', 'sa', 'natala', 'nga', 'mga', 'kaso.', 'Kalagmitan', 'nga', 'maapektaran', 'mao', 'ang', 'nag-edad', 'og', '1-10', 'anyos', '(', '38', '%', ')', 'ug', 'kadaghanan', 'nila', 'mga', 'lalaki', '(', '53', '%', ')', '.', 'Ang', 'mosunod', 'mao', 'ang', 'Top', '10', 'Municipality', '/', 'City', 'sa', 'probinsya', 'nga', 'aduna'y', 'taas', 'nga', 'kaso', 'sa', 'dengue', ':', 'Canlaon', 'City', ',', 'Dumaguete', 'City', ',', 'Bayawan', 'City', ',', 'Guihulngan', ',', 'La', 'Libertad', ',', 'Sibulan', ',', 'Bais', 'City', ',', 'Sta.', 'Catalina', ',', 'Siaton', ',', 'ug', 'Valencia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 6, 0, 5, 0, 5, 6, 0, 5, 0, 5, 6, 0, 5, 6, 0, 5, 0, 0, 5, 0] | cebuaner |
4,516 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['COA', 'REPORT', ':', '90', '%', 'SA', 'MGA', 'PAMILYA', 'SA', '4Ps', ',', 'UBOS', 'GIHAPON', 'SA', 'POVERTY', 'THRESHOLD', 'Human', 'sa', '13', 'ka', 'tuig', 'nga', 'pagdawat', 'og', 'cash', 'grants', ',', '90', '%', 'sa', 'mga', 'aktibong', 'benepisyaryo', 'sa', 'programa', 'sa', 'gobyerno', 'nga', 'Pantawid', 'Pamilyang', 'Pilipino', 'Program', '(', '4Ps', ')', 'ang', 'ubos', 'gihapon', 'sa', 'poverty', 'threshold.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'Commission', 'on', 'Audit', '(', 'COA', ')', ',', 'kadaghanan', 'sab', 'nila', 'ang', 'mahimong', 'matangtang', 'sa', 'lista', 'sa', 'programa', 'kung', 'dili', 'dayon', 'mopahigayon', 'og', 'kinahanglanon', 'nga', 'mga', 'lakang', 'bahin', 'niini.', 'Nakadawat', 'ang', 'antipoverty', 'program', 'sa', 'gobyerno', 'og', 'kinatibuk-ang', 'pondo', 'nga', 'P780.71', 'bilyon', 'tali', 'sa', 'tuig', '2008', 'ug', '2021', ',', 'gikan', 'sa', '62-page', 'performance', 'audit', 'report', 'sa', '4Ps', 'nga', 'gi-post', 'sa', 'COA', 'sa', 'ilang', 'website', 'niadtong', 'Oktubre', '4.', 'Gisubli', 'sab', 'nga', 'aduna'y', 'kinatibuk-ang', '4.2', 'milyon', 'nga', 'aktibong', 'benepisyaryo', 'sa', '4Ps', 'nga', 'anaa', 'sa', 'maong', 'programa', 'sulod', 'sa', 'pito', 'hangtod', '13', 'ka', 'tuig.', 'Apan', '90', '%', 'nila', 'o', '3,820,012', 'ka', 'mga', 'pamilya', 'ang', 'ubos', 'gihapon', 'sa', 'poverty', 'threshold', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 8, 8, 8, 8, 8, 8, 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, 7, 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] | cebuaner |
4,517 | 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', 'hulagway', 'sa', 'duha', 'ka', ''interacting', 'galaxies', ''', 'nga', 'morag', 'nagtapad', 'nga', 'naglutaw', ',', 'kuha', 'sa', 'Hubble', ''s', 'Advanced', 'Camera', 'for', 'Surveys.', 'Ang', 'galactic', 'duo', ',', 'bahin', 'sa', 'pagpaningkamot', 'nga', 'makatukod', 'og', 'archive', 'sa', ''interesting', 'targets', ''', 'alang', 'mas', 'detalyado', 'nga', 'umalabot', 'nga', 'pagtuon', 'pinaagi', 'sa', 'Hubble', ',', 'ground-based', 'telescopes', ',', 'ug', 'NASA', 'Webb', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 7, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
4,518 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gilaoman', 'nga', 'aduna'y', 'laing', 'pagsaka', 'sa', 'presyo', 'sa', 'gasolina', 'sa', 'mosunod', 'nga', 'semana.', 'Sumala', 'pa', 'sa', 'usa', 'ka', 'oil', 'industry', 'source', ',', 'ang', 'mosunod', 'mao', 'ang', 'gibanabana', 'nga', 'kausbanan', 'sa', 'presyo', 'sa', 'mga', 'produkto', 'sa', 'petroleum', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
4,519 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'GIKONSIDERAR', 'ANG', 'PAGPATUMAN', 'SA', 'PALENG-QR', 'ALANG', 'SA', 'CASHLESS', 'PAYMENTS', 'SA', 'PUBLIC', 'MARKET', ',', 'TRANSPORT', 'HUBS', 'Nakadawat', 'si', 'Mayor', 'Felipe', 'Remollo', 'og', 'orientation', 'bahin', 'sa', 'pagpatuman', 'sa', 'Paleng-QR', 'Ph', 'Program', 'nga', 'magtugot', 'sa', 'mga', 'tigbaligya', 'ug', 'mga', 'drayber', 'sa', 'pedicab', 'nga', 'makadawat', 'og', 'digital', '/', 'online', 'payments', 'gamit', 'ang', 'QR', 'codes', 'gikan', 'sa', 'ilang', 'mga', 'kostumer', 'o', 'pasahero.', 'Kini', 'alang', 'sa', 'mas', 'paspas', ',', 'epektibo', 'ug', 'luwas', 'nga', 'mga', 'transaksyon', 'ilabi', 'na', 'sa', 'mga', 'restrictions', 'sa', 'paglihok', 'tungod', 'sa', 'pandemya', 'sa', 'Covid-19.', 'Gipresentar', 'ni', 'Luther', 'Palma', ',', 'representante', 'sa', 'local', 'branch', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', ',', 'ngadto', 'ni', 'Mayor', 'Remollo', 'ang', 'konsepto', ',', 'tumong', 'ug', 'mechanics', 'sa', 'Paleng-QR', ',', 'nga', 'gitambayayong', 'sa', 'BSP', 'ug', 'Department', 'of', 'Interior', 'and', 'Local', 'Government', '(', 'DILG', ')', 'aron', 'pagdasig', 'sa', 'dakbayan', 'sa', 'pag-apil.', 'Gilaoman', 'sab', 'nga', 'mobisita', 'si', 'BSP', 'Deputy', 'Governor', 'Bernadette', 'Puyat', 'ngadto', 'ni', 'Mayor', 'Remollo', 'alang', 'sa', 'usa', 'ka', 'conference', 'aron', 'hisgutan', 'kung', 'unsaon', 'pag-institutionalize', 'sa', 'pagpatuman', 'sa', 'Paleng-QR', 'Ph', 'Program', 'sa', 'mga', 'lokalidad', ',', 'nga', 'makatabang', 'sab', 'sa', 'pagpakgang', 'sa', 'pagkuyanap', 'sa', 'peke', 'nga', 'kwarta', 'ug', 'impeksyon', '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. | [5, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 7, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 3, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
4,520 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NGCP', 'NAKOMPLETO', 'NA', 'ANG', 'RESTORATION', 'SA', 'AMLAN-SAMBOAN', 'SUBMARINECABLE', 'Malampuson', 'nga', 'gipakusog', 'sa', 'NGCP', 'ang', 'Amlan-Samboan', '138kV', 'Transmission', 'Line', '1', 'niadtong', 'Oktubre', '2', ',', '2022', 'mga', 'alas-2', 'sa', 'hapon.', 'Mao', 'kini', 'ang', 'nadugang', 'og', 'kasaligan', 'nga', 'power', 'transmission', 'services', 'tali', 'sa', 'mga', 'isla', 'sa', 'Negros', 'ug', 'Cebu.', 'Nadaot', 'ang', 'transmission', 'line', 'atol', 'sa', 'dredging', 'ug', 're-channeling', 'nganmga', 'kalihukan', 'sa', 'DPWH', 'sa', 'Amlan', ',', 'Negros', 'Oriental', 'niadtong', 'Hunyo', '15', ',', '2021.', 'Gitunga', 'ang', 'kapasidad', 'sa', 'underwater', 'cable', 'sa', 'NGCP', 'sa', 'pagpasa', 'og', 'kuryente', 'tali', 'sa', 'Negros', 'ug', 'Cebu', ',', 'gikan', 'sa', '180', 'MW', 'anaa', 'nalang', 'kini', 'sa', '90', 'MW', ',', 'nga', 'nag-limit', 'sa', 'dagan', 'sa', 'kuryente', 'tali', 'sa', 'mga', 'isla', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 5, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,521 | 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', 'blessing', 'sa', 'nagkalain-laing', 'opisina', 'sa', 'Provincial', 'Capitol', 'karong', 'Huwebes', ',', 'Oktubre', '13', ',', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,522 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuhaan', 'og', 'video', 'sa', 'netizen', 'nga', 'si', 'Mark', 'Niño', 'Rosellosa', 'ang', 'usa', 'ka', 'King', 'Cobra', 'nga', 'iyang', 'nakit-an', 'sa', 'luyo', 'sa', 'ilang', 'panimalay', 'sa', 'Tabogon', ',', 'Cebu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0] | cebuaner |
4,523 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SEN.', 'TULFO', 'GISUGYOT', 'ANG', 'FULL', 'SCHOLARSHIP', 'ALANG', 'SA', 'NURSING', 'STUDENTS', 'Gisugyot', 'ni', 'Senador', 'Raffy', 'Tulfo', 'niadtong', 'Martes', 'ang', 'probisyon', 'sa', 'paghatag', 'og', 'full', 'scholarship', 'sa', 'nursing', 'students', ',', 'ubos', 'sa', 'usa', 'ka', 'kondisyon.', 'Makadawat', 'og', 'full', 'scholarship', 'ang', 'mga', 'estudyante', 'sa', 'nursing', ',', 'basta', 'moserbisyo', 'sila', 'sa', 'nasud', 'sulod', 'sa', 'upat', 'ngadto', 'sa', 'lima', 'ka', 'tuig', 'human', 'sa', 'ilang', 'graduation.', 'Sumala', 'pa', 'ni', 'Philippine', 'Heart', 'Center', 'Executive', 'Director', 'Dr.', 'Joel', 'Abanilla', ',', 'kinahanglan', 'og', 'balaod', 'alang', 'sa', 'gisugyot', 'sa', 'Senador.', 'Gawas', 'ni', 'Tulfo', ',', 'nipadayag', 'sab', 'og', 'suporta', 'sa', 'maong', 'balaodnon', 'si', 'Senador', 'Christopher', 'Go', ',', 'kinsa', 'chairperson', 'sa', 'panel.', 'Nasayran', 'sa', 'mga', 'senador', 'nga', 'anaa', 'lamang', 'sa', 'P34,000', 'ngadto', 'sa', 'P36,000', 'ang', 'sweldo', 'sa', 'mga', 'nurse', 'sa', 'government', 'hospitals', ',', 'mas', 'ubos', 'kini', 'sa', 'ikatanyag', 'sa', 'langyaw', 'nga', 'mga', 'nasud.', 'Gisubli', 'ni', 'Tulfo', 'nga', 'sobra', 'sa', 'trabaho', 'ang', 'mga', 'nurse', ',', 'ug', 'nagserbisyo', 'sa', 'daghang', 'mga', 'pasyente', 'kesa', 'sa', 'sulundon', 'nga', '4:1', 'ratio', 'o', 'upat', 'ka', 'pasyente', 'sa', 'usa', 'ka', 'nurse.', 'Bag-ohay', 'lamang', ',', 'gibutyag', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'nga', 'aduna'y', 'kakulang', 'nga', '106,000', 'ka', 'mga', 'nurse', 'ang', '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, 1, 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, 3, 4, 4, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,524 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPIN', '16,000', 'KA', 'MGA', 'RESIDENTE', ',', 'NIBAKWIT', 'TUNGOD', 'SA', 'PINAKABAG-O', 'NGA', 'ENGWENTRO', 'SA', 'HIMAMAYLAN', 'Nisaka', 'ang', 'numero', 'sa', 'mga', 'nibakwit', 'sa', 'Himamaylan', 'City', 'ngadto', 'sa', '16,976', 'human', 'sa', 'pinakabag-o', 'nga', 'engkwentro', 'tali', 'sa', 'gobyerno', 'ug', 'mga', 'pwersa', 'sa', 'rebelde', 'sa', 'Barangay', 'Carabalan.', 'Mao', 'kini', 'ang', 'gibutyag', 'ni', 'Provincial', 'Social', 'Welfare', 'Officer', 'Merle', 'Garcia', 'niadtong', 'Martes', ',', 'Oktubre', '11', ',', '2022.', '1,952', 'ka', 'mga', 'tawo', 'ang', 'nadungag', 'sa', 'mga', 'nibakwit', 'gikan', 'niadtong', 'Lunes', 'diin', 'aduna', 'kini', 'sa', '15,024.', 'Bag-ohay', 'lamang', ',', 'napatay', 'si', 'Romeo', 'Nanta', ',', 'alias', 'Ka', 'Juanito', 'Magbanua', ',', 'sa', 'engkwentro', 'sa', 'Sitio', 'Medel', 'sa', 'Barangay', 'Carabalan', 'mga', '5:25', 'sa', 'hapon', 'niadtong', 'Lunes.', 'Siya', 'ang', 'commanding', 'officer', 'sa', 'New', 'People', ''s', 'Army', 'Regional', 'Operational', 'Command', '--', 'Negros', 'ug', 'tigpamaba', 'sa', 'Apolinario', 'Gatmaitan', 'Command', ',', 'sumala', 'pa', 'sa', 'kasundaluhan.', 'Mao', 'na', 'kini', 'ang', 'ikalima', 'nga', 'engkwentro', 'sa', 'Carabalan', 'sukad', 'pa', 'niadtong', 'Huwebes', 'nga', 'hinungdan', 'sa', 'pagbakwit', 'sa', 'mga', 'lumolupyo', 'tungod', 'sa', 'kahadlok.', 'Sumala', 'pa', 'ni', 'Garcia', ',', 'gilaoman', 'nila', 'nga', 'mosaka', 'pa', 'ang', 'numero', 'sa', 'mga', 'mobakwit', 'human', 'sa', 'pinakabag-o', 'nga', 'engkwentro', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 5, 6, 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, 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, 1, 2, 0, 0, 1, 2, 2, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,525 | 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', 'tigbaligya', 'sa', 'Mandaue', 'City', 'Public', 'Market', 'ang', 'nagdawat', 'na', 'og', 'bayad', 'pinaagi', 'sa', 'GCash.', 'Sumala', 'pa', 'ni', 'Mandaue', 'City', 'Mayor', 'Jonas', 'Cortes', 'sa', 'usa', 'ka', 'Facebook', 'post', ',', 'mga', '60', 'ka', 'stalls', 'ang', 'nagdawat', 'na', 'og', 'digital', 'payment', ',', 'human', 'nagsugod', 'ang', 'maong', 'dakbayan', 'sa', 'pagduso', 'niini', 'niadtong', 'Agosto', '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, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 5, 6, 0, 1, 2, 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] | cebuaner |
4,526 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GIINGONG', 'LIDER', 'SA', 'NPA', 'SA', 'NEGROS', ',', 'NAPATAY', 'SA', 'ENGKWENTRO', 'Napatay', 'ang', 'usa', 'ka', 'giingong', 'taas', 'nga', 'lider', 'sa', 'NPA', 'sa', 'engkwentro', 'tali', 'sa', 'mga', 'tropa', 'sa', '94th', 'Infantry', 'Battalion', 'sa', 'Sitio', 'Medel', ',', 'Barangay', 'Carabalan', ',', 'Himamaylan', 'City', ',', 'Negros', 'Occidental', 'niadtong', 'Lunes', ',', 'Oktubre', '10', ',', '2022.', 'Giila', 'ang', 'napatay', 'nga', 'si', 'Romeo', 'V.', 'Nanta', ',', 'kinsa', 'mas', 'ilado', 'sa', 'pangalan', 'nga', 'Juanito', 'Magbanua.', 'Siya', 'ang', 'tigpamaba', 'sa', 'Komiteng', 'Rehiyon', '–', 'Negros', ',', 'Cebu', ',', 'Bohol', ',', 'and', 'Siquijor', '(', 'KR-NCBS', ')', 'sa', 'CPP-NPA', '–', 'NDF', 'ug', 'giingon', 'inila', 'nga', 'communist', 'terrorist', 'leader', 'sa', 'isla', 'sa', 'Negros.', 'Giingon', 'sab', 'nga', 'aduna', 'siya'y', 'daghan', 'nga', 'mga', 'warrant', 'of', 'arrest', 'ug', 'apil', 'sa', 'sunod-sunod', 'nga', 'mga', 'engkwentro', 'sa', 'kasundalohan', 'sa', 'Carabalan.', 'Sumala', 'pa', 'sa', 'report', ',', 'samtang', 'nagpahigayon', 'og', 'pursuit', 'operation', 'human', 'sa', 'sunod-sunod', 'nga', 'engkwentro', 'sa', '94IB', 'tali', 'sa', 'CTGs', ',', 'laing', 'engkwentro', 'ang', 'nahitabo', 'tali', 'sa', 'tropa', 'sa', '94IB', 'ug', 'kapin', 'o', 'kulang', 'pulo', 'ka', 'giingon', 'Communist', 'NPA', 'Terrorists', '(', 'CNT', ')', 'sa', 'naasoy', 'nga', 'lugar.', 'Niresulta', 'kini', 'sa', '10', 'minutos', 'nga', 'pagpinusilay', 'sa', 'wala', 'pa', 'nisibat', 'ang', 'mga', 'giingong', 'CNT', 'sa', 'managlahing', 'direksyon', 'ug', 'gibiyaan', 'ang', 'patay', 'nga', 'lawas', 'sa', 'ilang', 'kauban.', 'Nakuha', 'sa', 'iyang', 'posesyon', 'ang', 'cal.45', 'pistol', 'nga', 'gi-convert', 'sa', '.9mm', 'pistol', ',', 'unom', 'ka', 'buhi', 'nga', 'bala', ',', 'usa', 'ka', 'magazine', ',', 'usa', 'backpack', ',', 'cellphone', 'ug', 'personal', 'nga', 'mga', 'butang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,527 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PRES.', 'MARCOS', 'GIPIRMAHAN', 'ANG', 'SIM', 'REGISTRATION', 'LAW', 'TALIWALA', 'SA', 'PAGDAGHAN', 'SA', 'TEXT', 'SCAMS', 'Giaprobahan', 'na', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'SIM', 'Card', 'Registration', 'Law', 'karong', 'adlawa', ',', 'Oktubre', '10', ',', '2022.', 'Mao', 'kini', 'ang', 'gipanan-aw', 'nga', 'pamaagi', 'aron', 'mapunggan', 'ang', 'text', 'scams', 'o', 'text', 'spams', 'nga', 'gi-target', 'ang', 'daghan', 'nga', 'mga', 'Pilipino.', 'Gipirmahan', 'ni', 'Presidente', 'Marcos', 'ang', 'balaod', ',', 'nga', 'nag-require', 'sa', 'mga', 'tiggamit', 'og', 'SIM', 'card', 'o', 'mas', 'bag-ong', 'tipo', 'sa', 'SIM', '(', 'eSIM', ')', 'nga', 'mopresentar', 'ug', 'bisan', 'unsa', 'nga', 'opisyal', 'nga', 'identipikasyon', 'sa', 'dili', 'pa', 'kini', 'nila', 'magamit.', 'Dugang', 'pa', 'niya', 'nga', 'ilalom', 'sa', 'maong', 'balaod', ',', 'kinahanglan', 'sab', 'nga', 'morehistro', 'ang', 'mga', 'aduna', 'na'y', 'prepaid', 'SIM', 'card', 'sulod', 'sa', 'gitakda', 'nga', 'panahon.', 'Kung', 'mapakyas', 'sa', 'pagbuhat', 'niini', ',', 'moresulta', 'kini', 'sa', 'automatic', 'deactivation', 'sa', 'serbisyo', 'sa', 'SIM', 'ngadto', 'sa', 'mga', 'subscriber', 'niini.', 'Nipasalig', 'sab', 'ang', 'Presidente', 'sa', 'publiko', 'nga', 'ang', 'SIM', 'Card', 'Registration', 'Act', ',', 'ipaibabaw', 'ang', 'proteksyon', 'sa', 'confidentiality', 'ug', 'data', 'privacy', 'rights', 'sa', 'mga', 'subscriber', 'niini.', 'Ilalom', 'sa', 'maong', 'balaod', ',', 'gimandoan', 'ang', 'tanan', 'nga', 'mga', 'public', 'telecommunications', 'entities', '(', 'PTEs', ')', '--', 'gobyerno', 'o', 'pribado', '--', 'ug', 'mga', 'direct', 'sellers', 'nga', 'mo-require', 'sa', 'mga', 'tiggamit', 'nga', 'mopresentar', 'og', 'valid', 'ID', 'sa', 'dihang', 'mokuha', 'og', 'SIM', 'card.', 'Samtang', 'ang', 'mga', 'korporasyon', ',', 'kinahanglan', 'nga', 'mopresentar', 'og', 'certificate', 'of', 'registration', 'ingon', 'man', 'sa', 'duly-adopted', 'resolution', 'nga', 'motudlo', 'sa', 'ilang', 'duly-authorized', 'representative', ',', 'ug', 'special', 'power', 'of', 'attorney', 'alang', 'sa', 'pagrehistro', 'sa', 'SIM', 'sa', 'uban', 'pang', 'judicial', 'entities', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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] | cebuaner |
4,528 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', 'NAGTUKOD', 'OG', 'CENTER', 'ALANG', 'SA', 'MGA', 'MAG-UUMA', 'ARON', 'PAGKAT-ON', 'SA', 'BAG-ONG', 'MGA', 'TEKNOLOHIYA', 'UG', 'MAPALAMBO', 'ANG', 'PRODUKSYON', 'Ma-access', 'na', 'sa', 'mga', 'lokal', 'nga', 'mag-uuma', 'ug', 'mangingisda', 'ang', 'pinakabag-o', 'nga', 'mga', 'impormasyon', 'ug', 'teknik', 'sa', 'agrikultura', 'pinaagi', 'sa', 'gitukod', 'nga', 'Farmer’s', 'Information', 'and', 'Technology', 'Service', '(', 'FITS', ')', 'Center', 'sa', 'dakbayan', 'sa', 'Dumaguete.', 'Gidayeg', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo', 'ang', 'Agricultural', 'Training', 'Institute', 'sa', 'Department', 'of', 'Agriculture', 'tungod', 'sa', 'pag-turnover', 'sa', 'ubay-ubay', 'nga', 'mga', 'kagamitan', 'sa', 'ICT', 'ug', 'mga', 'materyales', 'sa', 'Information', 'Education', 'Campaign', 'ngadto', 'sa', 'dakbayan.', 'Gamiton', 'kini', 'sa', 'FITS', 'Center', 'aron', 'makatudlo', 'og', 'science', ',', 'technology-based', 'ug', 'indigenous', 'technologies', 'gikan', 'sa', 'mga', 'malampusong', 'farmer-scientists', 'aron', 'mapalambo', 'ang', 'produksyon.', 'Lakip', 'sa', 'mga', 'gi-turnover', 'nga', 'kagamitan', 'ang', 'laptop', ',', 'GPS', ',', 'Samsung', 'tablet', ',', 'pocket', 'wifi', ',', 'printer', 'ug', 'gatusan', 'ka', 'mga', 'materyales', 'sa', 'IEC.', 'Anaa', 'sab', 'atol', 'sa', 'pag-turnover', 'sila', 'si', 'Councilor', 'Karissa', 'Tolentino-Maxino', '(', 'Deputy', 'Mayor', 'for', 'Livelihood', ')', ',', 'City', 'Administrator', 'Lilani', 'Ramon', ',', 'mga', 'lider', 'sa', 'nagkalain-laing', 'farmers', 'associations', 'ug', 'kawani', 'sa', 'City', 'Agriculture', 'Office', 'nga', 'gipangulohan', 'ni', 'Maria', 'Victoria', 'B.', 'Umbac.', 'Gilaoman', 'nga', 'sa', 'pagtukod', 'sa', 'FITS', 'Center', ',', 'mahatagan', 'ang', 'mga', 'mag-uuma', 'og', 'science-based', 'information', 'ug', 'technology', 'services', 'nga', 'makatabang', 'nila', 'sa', 'pagsiguro', 'sa', 'seguridad', 'sa', 'pagkaon'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 5, 0, 0, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 7, 8, 8, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 1, 2, 2, 2, 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] | cebuaner |
4,529 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KALABAW', ',', 'NAANOD', 'SA', 'BAHA', 'SA', 'MINGLANILLA', 'Tungod', 'sa', 'kusog', 'nga', 'pag-ulan', ',', 'duha', 'ka', 'kalabaw', '(', 'water', 'buffaloes', ')', 'ang', 'nadala', 'sa', 'baha', 'sa', 'Barangay', 'Campo', '8', 'sa', 'lungsod', 'sa', 'Minglanilla', 'sa', 'Cebu', 'niadtong', 'Sabado', 'sa', 'hapon', ',', 'Oktubre', '8', ',', '2022.', 'Sa', 'video', ',', 'makita', 'ang', 'mga', 'lumolupyo', 'sa', 'maong', 'dapit', 'nga', 'gitabagang', 'ang', 'usa', 'sa', 'mga', 'kalabaw', 'nga', 'makuha', 'sa', 'baha', 'gamit', 'ang', 'nipis', 'nga', 'mga', 'pisi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,530 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'SUNDALO', 'PATAY', ',', '6', 'SAMARAN', 'SA', 'ENGKWENTRO', 'TALI', 'SA', 'MGA', 'GIINGONG', 'NPA', 'SA', 'NEGROS', 'OCCIDENTAL', 'Duha', 'ka', 'sundalo', 'ang', 'patay', 'samtang', 'unom', 'ang', 'samaran', 'sa', 'engkwentro', 'tali', 'sa', 'mga', 'giingong', 'miyembro', 'sa', 'New', 'People’s', 'Army', '(', 'NPA', ')', 'sa', 'Himamaylan', 'City', ',', 'Negros', 'Occidental', 'mga', 'alas-9', 'sa', 'buntag', 'niadtong', 'Sabado', ',', 'Oktubre', '8', ',', '2022.', 'Sumala', 'pa', 'ni', 'Brigadier', 'General', 'Inocencio', 'Pasaporte', ',', '303rd', 'Brigade', 'commander', ',', 'niadto', 'ang', 'mga', 'sundalo', 'sa', 'maong', 'dapit', 'human', 'makadawat', 'og', 'mga', 'taho', 'nga', 'nagapangikil', 'ang', 'mga', 'giingon', 'rebeldeng', 'NPA', 'sa', 'mga', 'residente', 'sa', 'Barangay', 'Carabalan.', 'Mao', 'na', 'kini', 'ang', 'ikaupat', 'nga', 'panagsangka', 'tali', 'sa', 'mga', 'tropa', 'sa', 'gobyerno', 'ug', 'mga', 'rebeldeng', 'kominista', 'sa', 'kabukiran', 'sa', 'Himamaylan', 'sulod', 'sa', 'tulo', 'ka', 'adlaw.', 'Tungod', 'sa', 'pagpinusilay', ',', 'niresulta', 'kini', 'sa', 'pagbakwit', 'sa', 'labing', 'menos', '1,767', 'ka', 'mga', 'lumolupyo', 'gikan', 'sa', 'Barangay', 'Carabala.', 'Gisakay', 'sa', 'helicopter', 'ang', 'mga', 'samaran', 'nga', 'sundalo', 'apan', 'duha', 'ang', 'namatay', 'nila', 'sa', 'ilang', 'pagpaingon', 'sa', 'ospital', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4, 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, 5, 6, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,531 | 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', 'P2,499', 'NGA', 'PLETE', 'SA', 'JAPAN', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'promotional', 'seats', 'nga', 'ingon', 'kaubos', 'sa', 'P2,499', 'nga', 'one-way', 'base', 'fare', 'sa', 'pipila', 'ka', 'mga', 'destinasyon', 'sa', 'Japan', ',', 'kini', 'human', 'gipagaan', 'sa', 'maong', 'nasud', 'ang', 'border', 'ug', 'visa', 'restrictions', 'niini', 'alang', 'sa', 'mga', 'turista.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'maong', 'airline', 'niadtong', 'Biyernes', ',', 'Oktubre', '7', ',', '2022.', 'Itanyag', 'kini', 'karong', 'Oktubre', '7-9', ',', '2022', 'ug', 'aduna'y', 'travel', 'period', 'gikan', 'Nobyembre', '1', ',', '2022', 'hangtod', 'Marso', '31', ',', '2023.', 'Sumala', 'pa', 'sa', 'Cebu', 'Pacific', ',', 'lakip', 'sa', 'promo', 'ang', 'biyahe', 'paingon', 'sa', 'Nagoya', ',', 'Narita', ',', 'Fukuoka', 'ug', 'Osaka.', 'Bag-ohay', 'lamang', ',', 'gianunsyo', 'sa', 'Japanese', 'Embassy', 'sa', 'Manila', 'nga', 'modawat', 'na', 'sila', 'og', 'aplikasyon', 'tanang', 'klase', 'sa', 'visa', ',', 'apil', 'na', 'ang', 'individual', 'travel.', 'Matod', 'pa', 'sa', 'notice', 'nga', 'gi-post', 'sa', 'Japanese', 'Embassy', 'niadtong', 'Martes', ',', 'magsugod', 'ang', 'pag-isyu', 'og', 'mga', 'visa', 'base', 'sa', 'gipagaan', 'nga', 'mga', 'pamaagi', 'sa', 'Oktubre', '11', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [3, 4, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 3, 4, 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,532 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['THAILAND', ',', 'NAGBANGOTAN', 'SA', 'MGA', 'BIKTIMA', 'SA', 'MASS', 'KILLING', 'LOOK', ':', '34', 'ka', 'mga', 'tawo', 'ang', 'namatay', 'sa', 'Thailand', 'niadtong', 'Huwebes', ',', 'Oktubre', '6', ',', '2022', ',', 'human', 'sa', 'pag-atake', 'sa', 'usa', 'ka', 'daycare', 'center', 'sa', 'usa', 'ka', 'kanhi', 'pulis', 'gamit', 'ang', 'kutsilyo', 'ug', 'pusil.', '22', 'ka', 'mga', 'bata', 'ang', 'nalakip', 'sa', 'mga', 'biktima', 'sa', 'suspek', ',', 'kinsa', 'gipapahawa', 'sa', 'serbisyo', 'sa', 'niaging', 'tuig', 'tungod', 'sa', 'drug-related', 'reasons', ',', 'sumala', 'pa', 'ni', 'district', 'police', 'official', 'Chakkraphat', 'Wichitvaidya.', 'Gisubli', 'niya', 'nga', 'nakit-an', 'sab', 'sa', 'mga', 'saksi', 'nga', 'nagkupot', 'og', 'kutsilyo', 'ang', 'suspek.', 'Mga', '30', 'ka', 'bata', 'ang', 'anaa', 'sa', 'center', 'sa', 'dihang', 'naabot', 'ang', 'suspek.', 'Mas', 'gamay', 'sa', 'naandan', 'tungod', 'sa', 'kusog', 'nga', 'ulan', ',', 'sumala', 'pa', 'ni', 'district', 'official', 'Jidapa', 'Boonsom', ',', 'kinsa', 'nagtrabaho', 'sa', 'usa', 'ka', 'duol', 'nga', 'opisina', 'niadtong', 'higayona.', '“The', 'shooter', 'came', 'in', 'around', 'lunch', 'time', 'and', 'shot', 'four', 'or', 'five', 'officials', 'at', 'the', 'childcare', 'center', 'first', ',', '”', 'sumala', 'pa', 'ni', 'Jidapa.', 'Apil', 'sa', 'mga', 'napatay', 'ang', 'usa', 'ka', 'magtutudlo', 'kinsa', 'walo', 'ka', 'bulan', 'nga', 'buntis.', 'Gisubli', 'niya', 'nga', 'niadtong', 'una', ',', 'nakaingon', 'sila', 'nga', 'fireworks', 'kini.', 'Dugang', 'pa', 'ni', 'Jidapa', ',', 'nipugos', 'pagsulod', 'ang', 'suspek', 'sa', 'usa', 'ka', 'sirado', 'nga', 'kwarto', 'diin', 'nangatulog', 'ang', 'mga', 'bata', 'aron', 'patyon', 'didto', 'gamit', 'ang', 'kutsilyo.', 'Human', 'sa', 'pag-atake', ',', 'niuli', 'ang', 'suspek', 'sa', 'ilang', 'panimalay.', 'Gipusil', 'niya', 'ang', 'iyang', 'asawa', 'ug', 'anak', ',', 'ug', 'giunay', 'sab', 'pagpatay', 'ang', 'iyang', 'kaugalingon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 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] | cebuaner |
4,533 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibalik', 'pag-usab', 'sa', 'Jollibee', 'ang', 'lamian', 'nga', 'Garlic', 'Pepper', 'Beef', 'sa', 'P95', '!', 'Anaa', 'lamang', 'kini', 'sa', 'Mega', 'Manila', 'Stores', 'pinaagi', 'sa', 'drive-thru', ',', 'take', 'out', ',', 'dine-in', 'ug', 'delivery', 'apps', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,534 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JAPANESE', 'CONSUL', 'GENERAL', ',', 'MAYOR', 'REMOLLO', 'NISAAD', 'SA', 'PAGPALAMBO', 'SA', 'PANAGHIGALAAY', 'Nibisita', 'si', 'Consul', 'General', 'sa', 'Japan', 'sa', 'Cebu', 'Hideki', 'Yamaji', 'ngadto', 'ni', 'Dumaguete', 'City', 'Mayor', 'Felipe', 'Remollo', 'kagahapon', 'sa', 'hapon', ',', 'Oktubre', '5', ',', '2022.', 'Naghiusa', 'silang', 'nisaad', 'nga', 'tutukan', 'ang', 'pagtrabaho', 'alang', 'sa', 'pagpalambo', 'sa', 'kaluwasan', 'ug', 'kaayohan', 'sa', 'tanang', 'Japanese', 'students', ',', 'citizens', 'ug', 'ilang', 'pamilya.', 'Gihisgutan', 'sa', 'duha', 'ka', 'mga', 'opisyal', 'kung', 'unsaon', 'pagpalambo', 'sa', 'cultural', 'exchanges', 'tali', 'sa', 'Pilipinas', 'ug', 'Japan', 'nga', 'makapalig-on', 'sa', 'long-standing', 'friendship', 'sa', 'duha', 'ka', 'nasud.', 'Pipila', 'ka', 'lokal', 'ng', 'mga', 'opisyal', 'ang', 'nitambong', 'sa', 'arrival', 'rites', 'sa', 'pagpasidungog', 'ni', 'Consul', 'General', 'Hideki', 'Yamaji', 'apil', 'ang', 'ubang', 'Japanese', 'students', 'kinsa', 'kasamtangang', 'nagskwela', 'sa', 'Silliman', 'University.', 'Nagbinayloay', 'og', 'token', 'sila', 'si', 'Mayor', 'Remollo', 'ug', 'Consul', 'General', 'Hideki', 'Yamaji', ',', 'nakigpulong', 'nila', 'ni', 'Councilors', 'Karissa', 'Tolentino-Maxino', ',', 'Liga', 'ng', 'mga', 'Barangay', 'President', 'Dionie', 'Amores', ',', 'Sangguniang', 'Kabataan', 'President', 'Renz', 'Macion', ',', 'City', 'Human', 'Resource', 'Management', 'Officer', 'Dr.', 'Dinno', 'T.', 'Depositario', 'JD', 'ug', 'uban', 'pang', 'department', 'heads', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 1, 2, 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, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,535 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'supporter', 'ni', 'Pryde', 'Henry', 'Teves', 'nagtigom', 'sa', 'gawas', 'sa', 'Kapitolyo', 'karong', 'hapon.', 'Gilaoman', 'ang', 'posibleng', 'pagtake-over', 'ni', 'Gov.', 'Roel', 'Degamo', 'human', 'siya', 'giproklamar', 'ug', 'nanumpa', 'isip', 'gobernador', '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, 0, 1, 2, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
4,536 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BUGNAW', 'NGA', 'PANAHON', ',', 'GILAOMAN', 'SA', 'MGA', 'MOSUNOD', 'NGA', 'ADLAW', 'Gilaoman', 'karon', 'sa', 'Pilipinas', 'ang', 'anam-anam', 'nga', 'pag-abot', 'sa', 'northeast', 'monsoon', 'o', 'amihan', 'season', 'sa', 'mga', 'umalabot', 'nga', 'adlaw.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'PAGASA', 'niadtong', 'Miyerkules', ',', 'Oktubre', '5', ',', '2022', ',', 'ingon', 'man', 'sa', 'pag-anunsyo', 'niini', 'sa', 'pagtapos', 'sa', 'southwest', 'monsoon', 'o', 'habagat', 'season.', 'Nipasidaan', 'sab', 'ang', 'state', 'weather', 'bureau', 'nga', 'tungod', 'sa', 'nagpadayong', 'La', 'Niña', 'phenomenon', ',', 'aduna'y', ''increased', 'likelihood', ''', 'nga', 'mahitabo', 'ang', 'above-normal', 'railfall', 'conditions', ',', 'nga', 'mahimong', 'moresulta', 'sa', 'kusog', 'nga', 'pag-ulan', ',', 'pagbaha', ',', 'ug', 'pagdahili', 'sa', 'yuta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 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 |
4,537 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAYOR', 'NA', ',', 'DRIVER', 'PA', 'Si', 'Bais', 'City', 'Mayor', 'Luigi', 'Marcel', 'Teves', 'Goñi', 'mao'y', 'nangunay', 'sa', 'pagmaneho', 'sa', 'chariot', 'nga', 'mao'y', 'gihimong', 'bridal', 'car', 'sa', 'bag-ong', 'kasal', 'nga', 'sila', 'si', 'Teacher', 'Jeddedeah', 'M.', 'Capila', 'ug', 'Jeramel', 'T.', 'Romano.', 'Nahitabo', 'kini', 'human', 'sa', 'seremonya', 'sa', 'kasal', 'diin', 'usa', 'sa', 'mga', 'principal', 'sponsors', 'si', 'Mayor', 'Luigi.', 'Si', 'Mayor', 'ang', 'nagmaneho', 'sa', 'bridal', 'chariot', 'paingon', 'sa', 'reception', 'venue', 'uban', 'ang', 'mga', 'potpot', 'nga', 'mao'y', 'gisakyan', 'sa', 'ubang', 'abay', 'sa', 'kasal.', 'Ang', 'bag-ong', 'kasal', ',', 'nagkanayon', 'nga', 'chariot', 'ug', 'mga', 'potpot', 'ang', 'gamiton', 'nga', 'sakyanan', 'sa', 'ilang', 'kasal.', 'Kini', 'aron', 'mapakita', 'ug', 'mapahibal', 'sa', 'tanan', 'nga', ',', '"', 'we', 'can', 'still', 'celebrate', 'love', 'and', 'happiness', 'though', 'we', 'are', 'poor.', '"', 'Mapasalamaton', 'sab', 'sila', 'nga', 'nisanong', 'sa', 'ilang', 'hangyo', 'si', 'Mayor', 'Luigi', 'nga', 'mao'y', 'momaneho', 'sa', 'bridal', 'chariot', ',', 'ug', 'napakita', 'ang', 'pagkamakamasa', 'ug', 'pagkamapalanggaon', 'ni', 'Mayor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 2, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,538 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mobalik', 'pag-usab', 'sa', 'Manila', 'si', 'American', 'R', '&', 'B', 'singer', 'ug', 'three-time', 'Grammy', 'winner', 'Ne-Yo', 'alang', 'sa', 'usa', 'ka', 'concert', 'karong', 'Enero', '23', ',', '2023', 'sa', 'Smart', 'Araneta', 'Coliseum', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 7, 8, 8, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0] | cebuaner |
4,539 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TEACHER', 'SA', 'CEBU', ',', 'NAMATAY', 'SA', 'AKSIDENTE', 'ATOL', 'SA', 'MISMONG', 'ADLAW', 'SA', 'MGA', 'MAGTUTUDLO', 'Patay', 'ang', 'usa', 'ka', 'magtutudlo', 'sa', 'Minglanilla', 'Central', 'School', 'human', 'maaksidente', 'sa', 'Barangay', 'Tunghaan', 'sa', 'lungsod', 'sa', 'Minglanilla', 'sa', 'Cebu', 'ganihang', 'buntag', ',', 'Oktubre', '5', ',', '2022.', 'Nahitabo', 'ang', 'maong', 'insidente', 'samtang', 'nagsaulog', 'ang', 'nasud', 'sa', 'National', 'Teachers', ''', 'Day.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Charisma', 'Servedo', ',', 'kinsa', 'gidala', 'pa', 'sa', 'ospital', 'apan', 'namatay', 'sa', 'ulahi', 'tungod', 'sa', 'iyang', 'mga', 'naangkon', 'nga', 'serious', 'injuries.', 'Sumala', 'pa', 'sa', 'report', ',', 'sakay', 'sa', 'motorsiklo', 'si', 'Charisma', 'nga', 'gimaneho', 'sa', 'iyang', 'bana', 'nga', 'si', 'Sherwin', 'sa', 'dihang', 'nibangga', 'nila', 'ang', 'usa', 'ka', 'lain', 'pa', 'nga', 'motorsiklo.', 'Matod', 'pa', 'ni', 'Police', 'Master', 'Sergeant', 'Joel', 'Lacson', 'sa', 'Minglanilla', 'Police', 'Station', ',', 'nakaangkon', 'sab', 'og', 'mga', 'samad', 'ang', 'bana', 'ni', 'Charisma.', 'Sa', 'pagkakaron', ',', 'padayon', 'pa', 'nga', 'gipangita', 'sa', 'kapulisan', 'ang', 'wala', 'pa', 'mailhing', 'driver', 'sa', 'motorsiklo', ',', 'kinsa', 'niikyas', 'human', 'sa', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 5, 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, 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, 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, 1, 2, 0, 3, 4, 4, 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] | cebuaner |
4,540 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INFLATION', 'RATE', 'SA', 'PILIPINAS', 'NISAKA', 'SA', '6.9', '%', 'SA', 'SETYEMBRE', 'GIKAN', '6.3', '%', 'SA', 'AGOSTO', 'Nisaka', 'ngadto', 'sa', '6.9', '%', 'ang', 'inflation', 'rate', 'sa', 'Pilipinas', 'niadtong', 'Setyembre', 'gikan', 'sa', '6.3', '%', 'niadtong', 'Agosto.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'karong', 'adlawa', ',', 'Oktubre', '5', ',', '2022.', 'Ang', 'inflation', 'mao', 'ang', 'rate', 'sa', 'pagsaka', 'sa', 'presyo', 'sa', 'consumer', 'goods', 'ug', 'services.', 'Mas', 'paspas', 'sab', 'ang', 'pagsaka', 'sa', 'inflation', 'rate', 'sa', 'Setyembre', 'karong', 'tuiga', 'kung', 'ikompara', 'sa', '4.2', '%', 'nga', 'inflation', 'rate', 'niadtong', 'Setyembre', '2021.', 'Ang', 'inflation', 'print', 'sa', 'niaging', 'bulan', ',', 'anaa', 'sab', 'sa', 'forecast', 'range', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', '(', 'BSP', ')', 'nga', '6.6', '%', 'hangtod', '7.4', '%', '.', 'Niabot', 'sa', '5.1', '%', 'ang', 'year-to-date', 'average', 'inflation', ',', 'sulod', 'sa', 'pangagpas', 'sa', 'administrasyong', 'Marcos', 'nga', '4.5', '%', 'ngadto', 'sa', '5.5', '%', 'alang', 'sa', 'tibuok', '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, 5, 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,541 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', ',', 'Oktubre', '5', ',', '2022', ',', 'atong', 'isaulog', 'ang', '#', 'WorldTeachersDay2022.', 'Usa', 'ka', 'dako', 'nga', 'pagsaludo', 'sa', 'tanan', 'nga', 'mga', 'magtutudlo', 'sa', 'tibuok', 'kalibutan', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,542 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['19', 'OUTSTANDING', 'JOB', 'ORDER', 'PERSONNEL', ',', 'GIHATAGAN', 'OG', 'PASIDUNGOG', 'SA', 'KAGAMHANAN', 'SA', 'DUMAGUETE', '19', 'ka', 'mga', 'job', 'order', 'personnel', 'gikan', 'sa', 'nagkalain-laing', 'opisina', 'ug', 'departamento', 'ang', 'gihatagan', 'og', 'pasidungog', 'sa', 'City', 'Government', 'sa', 'Dumaguete', 'pinaagi', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo.', 'Gihatag', 'sila', 'og', 'pag-ila', 'tungod', 'sa', 'ilang', 'outstanding', 'performance', 'base', 'sa', 'nahuman', 'nga', 'evaluation', 'sa', '4th', 'Quater', 'sa', 'Calendar', 'Year', '2021.', 'Nakadawat', 'sila', 'og', 'plaques', 'ug', 'tokens', 'gikan', 'ni', 'Civil', 'Service', 'Commission', 'Negros', 'Oriental', 'Provincial', 'Director', 'Merlinda', 'Flores-Quillano', 'ug', 'City', 'Human', 'Resource', 'Management', 'Officer', 'Dr.', 'Dinno', 'T.', 'Depositario', 'atol', 'sa', 'culmination', 'ceremony', 'sa', '122nd', 'Philippine', 'Civil', 'Service', 'Anniversary', 'niadtong', 'Sabado', 'sa', 'Pantawan', 'People’s', 'Park.', 'Ang', '19', 'ka', 'Outstanding', 'Job', 'Order', 'Personnel', ',', 'mao', 'sila', 'si', ':', 'Allen', 'S.', 'Abiera', ',', 'City', 'Veterinarian’s', 'Office', ';', 'Rowel', 'A.', 'Abueva', ',', 'City', 'Traffic', 'Management’s', 'Office', ';', 'Dennis', 'A.', 'Albesa', ',', 'City', 'Engineering’s', 'Office', ';', 'Jeannette', 'P.', 'Buling', ',', 'City', 'Local', 'Youth', 'Development', 'Office', ';', 'Gally', 'B.', 'Cadallo', ',', 'City', 'Sports', 'and', 'Youth', 'Development', 'Office', ';', 'Jamaima', 'B.', 'Casipong', ',', 'City', 'Discipline', 'Zone', ';', 'Dioge', 'B.', 'Ensertado', ',', 'City', 'Disaster', 'Risk', 'Reduction', 'Management', 'Office', ';', 'Mardelisa', 'M.', 'Estela', ',', 'City', 'Treasurer’s', 'Office', ';', 'Rochelle', 'T.', 'Evero', ',', 'City', 'Social', 'Welfare', 'and', 'Development', 'Office', ';', 'Generoso', 'M.', 'Flores', ',', 'City', 'Economic', 'Enterprises', 'Department', ';', 'Arlinda', 'V.', 'Francisco', ',', 'Dumaguete', 'PNP', 'Police', 'Station', ';', 'Niel', 'Mark', 'S.', 'Galera', ',', 'City', 'Environment', 'and', 'Natural', 'Resources', 'Office', ';', 'Rainer', 'C.', 'Famoso', ',', 'Special', 'Enforcement', 'Unit', ';', 'Erelyn', 'B.', 'Lanciso', ',', 'City', 'Agriculture’s', 'Office', ';', 'Dizanette', 'T.', 'Renquijo', ',', 'City', 'Assessor’s', 'Office', ';', 'Jason', 'G.', 'Talledo', ',', 'General', 'Services', 'Office', ';', 'Ana', 'Lourdes', 'M.', 'Yang', 'Yang', ',', 'Office', 'for', 'the', 'Senior', 'Citizens', 'Affairs', ';', 'Jedah', 'A.', 'Ybiosa', ',', 'City', 'Health', 'Office', 'and', 'Gina', 'G.', 'Zerna', ',', 'City', 'Nutrition', '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. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 4, 0, 1, 2, 2, 2, 0, 3, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 2, 2, 0, 3, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 4, 4, 0] | cebuaner |
4,543 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAGSEMENTO', 'SA', 'DALAN', 'NGA', 'APIL', 'SA', 'METRO', 'DUMAGUETE', 'DIVERSION', 'ROAD', ',', 'NAGPADAYON', 'Nagpadayon', 'ang', 'pagtrabaho', 'sa', 'dalan', 'nga', 'apil', 'sa', 'gihimo', 'nga', 'Metro', 'Dumaguete', 'Diversion', 'Road', 'human', 'nga', 'nisibog', 'na', 'ang', 'mga', 'tag-iya', 'sa', 'luna', 'nga', 'maagian', 'sa', 'maong', 'lapad', 'nga', 'dalan.', 'Padayon', 'ang', 'pagsemento', 'sa', 'dalan', 'paingon', 'sa', 'taytayan', 'nga', 'likod', 'sa', 'Vida', 'Royal', '(', 'Candau-ay', '-', 'Cadawinonan', 'Bridge', ')', 'nga', 'parte', 'ug', 'sumpay', 'sa', 'maong', 'diversion', 'road.', 'Gisuportaan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ang', 'gihimong', 'proyekto', 'sa', 'DPWH', 'tungod', 'makatabang', 'kini', 'sa', 'pagminus', 'sa', 'trapiko', 'sa', 'downtown', 'area.', 'Makahatag', 'sab', 'kini', 'og', 'kahapsay', 'ug', 'luwas', 'nga', 'pagbiyahe', 'alang', 'sa', 'mga', 'motorista', 'nga', 'molapos', 'ngadto', 'sa', 'lungsod', 'sa', 'Bacong', ',', 'Sibulan', 'ug', 'Valencia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 5, 0, 5, 0, 5, 0] | cebuaner |
4,544 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'MAGTUTUDLO', 'SA', 'BAIS', 'CITY', ',', 'NAKADAWAT', 'OG', 'REGALO', 'ALANG', 'SA', 'SELEBRASYON', 'SA', 'WORLD', 'TEACHERS', 'DAY', 'Nakadawat', 'ang', 'mga', 'magtutudlo', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'Bais', 'City', 'og', 'mga', 'regalo', 'alang', 'sa', 'selebrasyon', 'sa', 'World', 'Teachers', 'Day', 'niadtong', 'Sabado', ',', 'Oktubre', '1', ',', '2022.', 'Sa', 'usa', 'ka', 'video', 'nga', 'gi-upload', 'sa', 'social', 'media', ',', 'makita', 'ang', 'pipila', 'ka', 'mga', 'karton', 'nga', 'aduna'y', 'gamit', 'para', 'sa', 'eskwelahan.', 'Niabot', 'sa', '526', 'ka', 'Smart', 'TV', 'sets', 'ang', 'gipanghatag', 'aron', 'mapahimuslan', 'sa', 'mga', 'estudyante', 'ug', 'mga', 'magtutudlo.', 'Aduna', 'sab', 'mga', 'printers', 'nga', 'gihatag', 'sa', '226', 'ka', 'mga', 'magtutudlo', 'sa', 'K', 'to', '3.', 'Sa', 'pagsugod', 'sa', 'klase', 'kay', 'nakadawat', 'sab', 'sila', 'og', 'bondpapers', ',', 'alchohol', 'ug', 'facemasks.', 'Ang', 'Bais', 'City', 'mao', 'ang', 'pinakaunang', 'nagpahigayon', 'og', 'limited', 'face-to-face', 'classes', 'niadtong', 'Abril', ',', 'taliwala', 'sa', 'kalisod', 'nga', 'ilang', 'naagian', 'tungod', 'sa', 'Bagyong', 'Odette.', 'Sa', 'pagkakaron', ',', 'kada-adlawa', 'na', 'ang', 'klase', 'ug', 'gipatuman', 'na', 'ang', 'full', 'capacity', 'sa', 'kada', 'classroom', 'sa', 'tunghaan', 'sa', 'Bais', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 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, 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
4,545 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lima', 'ka', 'taga-Negros', 'Oriental', 'ang', 'suwerteng', 'nalakip', 'sa', '433', 'ka', 'mananaug', 'sa', 'sa', 'P236-million', 'jackpot', 'prize', '6', '/', '55', 'Grand', 'Lotto', 'niadtong', 'Sabado', ',', 'Oct.', '1', ',', '2022.', 'Kini', 'sumala', 'pa', 'sa', 'datos', 'nga', 'gipagawas', 'sa', 'Philippine', 'Charity', 'Sweepstakes', 'Office', '(', 'PCSO', ')', 'karong', 'gabii', ',', 'Oct.', '2.', 'Gilaomang', 'makadawat', 'ang', '5', 'ka', 'Negrense', 'og', 'kapin', 'P545,000', 'nga', 'premyo', 'human', 'nila', 'nakuha', 'ang', 'winning', 'number', 'combination', 'niadtong', 'Sabado', ':', '9-18-27-36-45-54', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,546 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'talagsaong', 'higayon', ',', '433', 'ka', 'tawo', 'ang', 'nidaug', 'sa', 'P236-million', 'nga', 'jackpot', 'sa', '6', '/', '55', 'Grand', 'Lotto', 'karong', 'gabii', ',', 'sumala', 'pa', 'sa', 'PCSO.', 'Tungod', 'niini', ',', 'bahin-bahinon', 'sa', 'mga', 'mananaug', 'ang', 'maong', 'premyo', 'og', 'makadawat', 'sila', 'og', 'kapin', '₱545,000', 'kada', 'usa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 8, 8, 8, 8, 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 |
4,547 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GINIKANAN', 'SA', 'CHILD', 'LABORERS', 'NAKABATON', 'OG', 'SKILLS', 'SA', 'PAGLUTO', 'ALANG', 'SA', 'ILANG', 'PANGINABUHI', 'Nagpahigayon', 'og', 'skills', 'training', 'ang', 'dakbayan', 'sa', 'Dumaguete', 'alang', 'sa', 'mga', 'ginikanan', 'sa', 'child', 'laborers', 'isip', 'parte', 'sa', 'intervention', 'program', 'aron', 'makakwarta', 'sila', 'alang', 'sa', 'ilang', 'mga', 'pamilya.', 'Giorganisar', 'kini', 'ni', 'City', 'Public', 'Employment', 'Service', 'Office', 'Manager', 'Maria', 'Socorro', 'P.', 'Mira.', 'Gidayeg', 'sab', 'ni', 'Councilor', 'Karissa', 'Tolentino-Maxino', ',', 'Deputy', 'Mayor', 'for', 'Livelihood', ',', 'Poverty', 'Alleviation', 'and', 'Women', ',', 'ang', 'mga', 'ginikanan', 'nga', 'nakahuman', 'sa', 'maong', 'kurso.', 'Parte', 'kini', 'sa', 'programa', 'sa', 'City', 'Government', 'alang', 'sa', 'mga', 'ginikanan', 'ilabi', 'sa', 'mga', 'babayi', 'aron', 'makabaton', 'sila', 'og', 'kahanas', 'nga', 'magamit', 'nila', 'sa', 'paghimo', 'og', 'dugang', 'nga', 'mga', 'panginabuhian.', 'Ang', 'maong', 'training', ',', 'gipahigayon', 'sa', 'TeamSkills', 'School', 'for', 'Culinary', 'Arts', 'and', 'Hospitality', 'Management', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 4, 4, 4, 0, 1, 2, 2, 2, 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, 3, 4, 4, 4, 4, 4, 4, 4, 0] | cebuaner |
4,548 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MOTOR', 'NIDAM-AG', 'SA', 'NAKA-STANDBY', 'NGA', 'TRUCK', 'SA', 'ZAMBOANGUITA', ';', 'DRIVER', 'SA', 'MOTOR', 'PATAY', 'Usa', 'ang', 'patay', 'human', 'maaksidente', 'ang', 'iyang', 'gimaneho', 'nga', 'motorsikto', 'sa', 'Sitio', 'Guisoan', 'sa', 'Barangay', 'Poblacion', 'sa', 'lungsod', 'sa', 'Zamboanguita', 'mga', 'alas-12', 'sa', 'kadlawon', 'niadtong', 'Biyernes', ',', 'Setyembre', '30', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Leomar', 'Caingcoy', 'Verances', ',', '23-anyos', ',', 'ulitawo', ',', 'ug', 'lumolupyo', 'sa', 'Barangay', 'Nabago', 'sa', 'naasoy', 'nga', 'lungsod.', 'Sumala', 'pa', 'sa', 'kapulisan', 'sa', 'Zamboanguita', ',', 'samtang', 'nagpahigayon', 'sila', 'og', 'mobile', 'patrolling', '/', 'roving', ',', 'nakadawat', 'sila', 'og', 'tawag', 'nga', 'aduna'y', 'usa', 'ka', 'aksidente', 'nga', 'nahitabo', 'sa', 'maong', 'lugar.', 'Dali', 'nga', 'niresponde', 'ang', 'kapulisan', 'ug', 'nasuta', 'nga', 'usa', 'ka', 'self', 'accident', 'ang', 'nahitabo.', 'Gikataho', 'sa', 'kapulisan', 'nga', 'sakay', 'sa', 'iyang', 'motorsiklo', 'si', 'Verances', 'sa', 'dihang', 'nidam-ag', 'kini', 'sa', 'truck', 'nga', 'naka-standby', 'sa', 'kilid', 'sa', 'dalan.', 'Dali', 'nga', 'gidala', 'si', 'Verances', 'sa', 'Negros', 'Oriental', 'Provincial', 'Hospital', '(', 'NOPH', ')', 'apan', 'gideklara', 'kini', 'nga', 'dead', 'on', 'arrival', 'sa', 'attending', 'physician', 'nga', 'si', 'Dr.', 'Erad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,549 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'magpapildi', 'ang', 'mga', 'estudyante', 'sa', 'Grade', '10', '-', 'Saturn', ',', 'Cluster', 'A', 'sa', 'Piapi', 'High', 'School', 'sa', ''No', 'Bag', 'Challenge.', ''', 'Ang', 'nakadaog—usa', 'ka', 'estudyante', 'nga', 'gigamit', 'ang', 'daang', 'electric', 'fan', 'isip', 'sudlanan', 'sa', 'iyang', 'mga', 'gamit', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 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 |
4,550 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipagawas', 'sa', 'James', 'Webb', 'ug', 'Hubble', 'telescopes', 'ang', 'mga', 'unang', 'hulagway', 'sa', 'pagbangga', 'sa', 'DART', 'spaceship', 'sa', 'asteroid', 'nga', 'Dimorphos', 'niadtong', 'Lunes.', 'Sumala', 'pa', 'sa', 'NASA', ',', 'gituyo', 'sa', 'naasoy', 'nga', 'spaceship', 'ang', 'pagbangga', 'sa', 'maong', 'asteroid', 'aron', 'sulayan', 'ang', 'planetary', 'defense', 'sa', 'Earth', 'batok', 'sa', 'mga', 'delikado', 'nga', 'asteroid', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 7, 8, 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, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,551 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['COMELEC', ',', 'GIMANDO', 'NA', 'ANG', 'PAG-ANNUL', 'SA', 'KADAUGAN', 'NI', 'GOV.', 'TEVES', 'NIADTONG', 'MAYO', 'Gimando', 'na', 'sa', 'Commission', 'on', 'Elections', '(', 'Comelec', ')', 'en', 'banc', 'ang', 'pag-annul', 'kon', 'pagbakwi', 'sa', 'kadaugan', 'ni', 'Governor', 'Pryde', 'Henry', 'Teves', 'niadtong', 'Mayo.', 'Kini', 'subay', 'sa', 'usa', 'ka', 'writ', 'of', 'execution', 'nga', 'giluwatan', 'sa', 'Comelec', 'niadtong', 'Martes', ',', 'Sept.', '27', ',', '2022.', 'Gipagawas', 'ngadto', 'sa', 'publiko', 'ug', 'gikompirmar', 'sa', 'Comelec', 'ang', 'maong', 'dokumento', 'karong', 'adlawa', ',', 'Sept.', '29.', 'Nakakuha', 'sab', 'ang', 'Yes', 'The', 'Best', 'Dumaguete', 'og', 'lehitimong', 'kopya', 'sa', 'maong', 'desisyon.', 'Sa', 'boto', 'nga', '3-2', ',', 'gidapigan', 'sa', 'Comelec', 'en', 'banc', 'ang', 'desisyon', 'sa', 'Second', 'Division', 'niini', 'niadtong', 'Disyembre', 'pagdeklarar', 'kang', 'Grego', 'Degamo', 'isip', 'nuisance', 'candidate.', 'Mahinumduman', 'nga', '"', 'Ruel', 'Degamo', '"', 'ang', 'gigamit', 'nga', 'ngalan', 'ni', 'Grego', 'sa', 'iyang', 'kandidatura', 'pagkagobernador.', 'Tungod', 'kay', 'wala', 'may', 'temporary', 'restraining', 'order', 'gikan', 'sa', 'Korte', 'Suprema', 'sa', 'pagkakaron', ',', 'gimanduan', 'sab', 'sa', 'Comelec', 'ang', 'Special', 'Provincial', 'Board', 'of', 'Canvassers', '(', 'SPBOC', ')', 'sa', 'Negros', 'Oriental', 'nga', 'bakwion', 'ang', 'kadaugan', 'ni', 'Teves.', 'Gimanduan', 'pud', 'ang', 'SPBOC', 'nga', 'ibalhin', 'ang', 'kapin', '49,000', 'ka', 'boto', 'ni', 'Ruel', 'Degamo', 'ngadto', 'kang', 'kanhing', 'Gov.', 'Roel', 'Degamo.', 'Sumala', 'pa', 'sa', 'Comelec', ',', 'pakalit', 'kuno', 'kaayo', 'nga', 'gigamit', 'ni', 'Grego', 'ang', 'ngalan', 'nga', '"', 'Ruel', '"', 'isip', 'angga', 'niadtong', 'piniliay.', 'Ang', 'maong', 'lakang', ',', 'makahatag', 'kuno', 'og', 'kalibog', 'tungod', 'kay', 'duna', 'sab', 'laing', 'kandidato', 'nga', 'nagngalan', 'og', 'Roel', 'Ragay', 'Degamo', ',', 'kinsa', 'mao', 'ang', 'kanhing', 'gobernador.', 'Giklaro', 'sab', 'sa', 'Comelec', 'nga', '"', 'Gaudia', '"', 'ang', 'apilyedo', 'ni', 'Grego', 'apan', 'gituyo', 'niya', 'og', 'gamit', 'ang', '"', 'Degamo', '"', 'niadtong', 'piniliay.', '“All', 'of', 'these', 'evinces', 'bad', 'faith', 'on', 'the', 'part', 'of', 'the', 'Respondent', ',', 'clearly', ',', 'he', 'has', 'no', 'bona', 'fide', 'intention', 'to', 'run', 'for', 'the', 'gubernatorial', 'seat', 'in', 'Negros', 'Oriental', ',', '”', 'ingon', 'sa', 'desisyon', 'sa', 'Comelec.', 'Subay', 'sa', 'mando', 'sa', 'Comelec', ',', 'gitakdang', 'magpulong', 'ang', 'SPBOC', 'didto', 'sa', 'Manila', 'karong', 'Lunes', ',', 'Oktubre', '3', ',', 'aron', 'bag-uhon', 'ang', 'mga', 'certificate', 'of', 'canvass', 'sa', 'mga', 'boto', ',', 'ingon', 'man', 'pagproklamar', 'sa', 'kandidatong', 'nidaog', 'sa', '2022', 'gubernatorial', 'race.', 'Sa', 'pagsulat', 'niining', 'balita', ',', 'wala', 'pay', 'pamahayag', 'gikan', 'sa', 'mga', 'kampo', 'nila', 'ni', 'Teves', 'ug', 'Degamo', 'nunot', 'sa', 'kamanduan', 'sa', 'Comelec.', '#', 'NewsBite', '|', 'with', 'reports', 'from', 'Willard', 'Cheng', ',', '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. | [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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, 3, 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, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 1, 2, 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, 3, 4, 0, 0, 0, 0, 0, 0, 3, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 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, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 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, 1, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4] | cebuaner |
4,552 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DOH', ':', 'PILIPINAS', ',', 'KULANG', 'OG', '106,000', 'KA', 'NURSES', 'Gibutyag', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'niadtong', 'Huwebes', 'nga', 'kulang', 'ang', 'Pilipinas', 'og', '106,000', 'ka', 'nurses', ',', 'taliwala', 'kini', 'sa', ''migration', ''', 'sa', 'mga', 'health', 'workers', 'sa', 'pagpangita', 'og', 'mas', 'maayo', 'nga', 'mga', 'oportunidad.', 'Ganahan', 'ang', 'maong', 'health', 'department', 'nga', 'ipadayon', 'sa', 'nasud', 'ang', '7,000', 'nga', 'tinuig', 'nga', 'deployment', 'cap', 'sa', 'mga', 'bag-ong', 'na-hire', 'nga', 'medical', 'professionals', 'sa', 'gawas', 'sa', 'nasud.', 'Dugang', 'pa', 'ni', 'Vergeire', ',', 'kulang', 'sab', 'ang', 'Pilipinas', 'og', 'doctors', ',', 'pharmacists', ',', 'medical', 'technologists', ',', 'midwives', ',', 'physical', 'therapists', 'ug', 'dentists.', 'Sa', 'pagkakaron', ',', 'ang', 'DOH', 'aduna'y', 'kapin', '2,000', 'ka', 'mga', 'unfilled', 'plantilla', 'positions.', 'Lakip', 'sa', 'maong', 'numero', 'ang', '624', 'nga', 'posisyon', 'alang', 'sa', 'nurses', ',', '1,332', 'alang', 'sa', 'midwives', ',', 'ug', '63', 'alang', 'sa', 'dentists', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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, 1, 0, 0, 0, 0, 5, 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] | cebuaner |
4,553 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KRIMEN', 'SA', 'DUMAGUETE', ',', 'MIUS-OS', 'NGADTO', 'SA', '25.27', '%', 'KARONG', 'TUIGA', 'Mius-os', 'ang', 'mga', 'insidente', 'sa', 'krimen', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'ngadto', 'sa', '25.27', '%', '.', 'Gikan', 'sa', '554', 'nga', 'kinatibuk-ang', 'insidente', 'sa', 'krimen', 'gikan', 'niadtong', 'Enero', 'hangtod', 'Agosto', '31', ',', '2021', ',', 'nahimo', 'na', 'lamang', 'kini', 'nga', '414', 'gikan', 'sa', 'susamang', 'mga', 'bulan', 'sa', 'tuig', '2022.', 'Gibutyag', 'ang', 'maong', 'report', 'ni', 'City', 'Chief-of-Police', 'Lt.', 'Col.', 'Joeson', 'B.', 'Parallag', 'niadtong', 'Lunes', 'atol', 'sa', 'quarterly', 'meeting', 'sa', 'City', 'Peace', 'and', 'Order', 'Council', 'nga', 'gipangulohan', 'ni', 'Vice-Mayor', 'Ma.', 'Isabel', 'Sagarbarria', 'ug', 'Councilor', 'Rey', 'Lyndon', 'Lawas', ',', 'Deputy', 'Mayor', 'for', 'Peace', 'and', 'Order', 'and', 'Illegal', 'Drugs', 'Abuse.', 'Sa', 'laing', 'bahin', ',', 'gibutyag', 'sab', 'sa', 'City', 'Bureau', 'of', 'Fire', 'Protection', 'nga', 'anaa', 'sa', 'P1,087,600.00', 'ang', 'gibanabana', 'nga', 'kadaot', 'sa', 'kabtangan', 'nga', 'na-record', 'sa', '10', 'ka', 'mga', 'insidente', 'sa', 'sunog', 'gikan', 'Enero', 'hangtod', 'Agosto', '2022.', 'Gisubli', 'ni', 'City', 'Fire', 'Marshall', 'Chief', 'Inspector', 'Marlon', 'K.', 'Chomling', 'nga', 'mas', 'ubos', 'kini', 'kung', 'ikompara', 'sa', '27', 'fire', 'incidents', 'niadtong', '2020', 'nga', 'niresulta', 'sa', 'P4.8', 'million', 'nga', 'gibanabanang', 'damage', 'to', 'property', 'ug', '17', 'fire', 'incidents', 'niadtong', '2021', 'nga', 'aduna'y', 'P2.3', 'million', 'nga', 'gibanabanang', 'damage', 'to', 'property.', 'Gilaoman', 'sab', 'nila', 'nga', 'wala', 'na'y', 'dagko', 'nga', 'mga', 'insidente', 'sa', 'sunog', 'nga', 'mahitabo', 'sa', 'katapusang', '3', 'ka', 'bulan', 'sa', '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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 1, 2, 2, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,554 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BRIDE', 'NGA', 'WALA', 'GITUNGHA', 'SA', 'IYANG', 'GROOM', ',', 'GIPADAYON', 'ANG', 'SEREMONYA', ',', 'PARTY', 'UBAN', 'SA', 'IYANG', 'PAMILYA', ',', 'HIGALA', 'Usa', 'ka', 'bride', 'ang', 'wala', 'gitungha', 'sa', 'iyang', 'groom', 'sa', 'ilang', 'kasal', 'apan', 'imbes', 'nga', 'maguol', ',', 'gipadayon', 'sa', 'bride', 'ang', 'pipila', 'ka', 'parte', 'sa', 'seremonya', 'apil', 'na', 'ang', 'party.', 'Sumala', 'pa', 'sa', 'bride', 'nga', 'si', 'Kayley', 'Stead', ',', 'upat', 'na', 'sila', 'katuig', 'nga', 'magkarelasyon.', 'Katapusan', 'niya', 'nga', 'nakit-an', 'ang', 'iyang', 'groom', 'mga', 'alas-4', 'sa', 'hapon', 'sa', 'wala', 'pa', 'ang', 'adlaw', 'sa', 'ilang', 'kasal', 'sa', 'Switzerland.', 'Bisan', 'paman', 'wala', 'ang', 'groom', ',', 'gipadayon', 'ni', 'Kayley', 'ang', 'pipila', 'ka', 'parte', 'sa', 'seremonya', 'diin', 'gisuot', 'niya', 'ang', 'iyang', 'bridal', 'gown', 'ug', 'aduna'y', 'entrance', 'uban', 'sa', 'iyang', 'amahan.', 'Human', 'niini', ',', 'gipadayon', 'sab', 'ni', 'Kayley', 'ang', 'party.', 'Imbes', 'slicing', 'of', 'the', 'cake', ',', 'nahimo', 'kining', 'punching', 'of', 'the', 'cake.', 'Sa', 'iyang', 'first', 'dance', 'nga', 'uban', 'unta', 'niya', 'ang', 'iyang', 'groom', ',', 'ang', 'iyang', 'amahan', 'ug', 'igsuon', 'ang', 'iyang', 'gisayaw.', 'Gisubli', 'ni', 'Kayley', 'nga', 'niadtong', 'nakita', 'niya', 'nga', 'naghilak', 'ang', 'iyang', 'entourage', ',', 'gipili', 'niya', 'nga', 'ipadayon', 'ang', 'party', 'tungod', 'nakagasto', 'naman', 'sila', 'ug', 'uban', 'naman', 'sab', 'niya', 'ang', 'iyang', 'pamilya', 'ug', 'mga', 'higala.', 'Tungod', 'niini', ',', 'gidayeg', 'si', 'Kayley', 'sa', 'iyang', 'mga', 'parente', 'ug', 'mga', 'higala.', 'Nitabang', 'sab', 'sila', 'nga', 'makakolekta', 'og', 'kwarta', 'aron', 'mabawi', 'ang', 'mga', 'gigasto', 'ni', 'Kayley', 'sa', 'kasal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 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, 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, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
4,555 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidayeg', 'sa', 'Taste', 'Atlas', ',', 'usa', 'ka', 'food', 'website', ',', 'ang', 'Lumpiang', 'Shanghai', 'isip', 'ikaduha', 'sa', 'best', 'street', 'food', 'sa', 'kalibutan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,556 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagdala', 'og', 'dakong', 'kadaot', 'sa', '#', 'CentralVietnam', 'ang', 'Typhoon', '#', 'Noru', 'human', 'kini', 'mag-landfall', 'sa', 'ilang', 'nasud', 'ganihang', 'buntag.', 'Ang', 'Typhoon', '#', 'Noru', 'mao', 'sab', 'ang', 'kanhing', 'Bagyong', '#', 'KardingPH', 'nga', 'niagi', 'ug', 'nagbilin', 'og', 'kadaot', 'sa', 'pipila', 'ka', 'mga', 'lugar', '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, 0, 0, 0, 0, 5, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
4,557 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SILLIMAN', 'UNIVERSITY', 'GIILA', 'PAG-USAB', 'ISIP', 'USA', 'KA', 'NATIONAL', 'HISTORICAL', 'LANDMARK', 'Ginganlan', 'pag-usab', 'ang', 'Silliman', 'University', 'isip', 'usa', 'ka', ''national', 'historical', 'landmark', ''', 'kon', 'usa', 'ka', 'nasudnong', 'timaan', 'sa', 'kasaysayan.', 'Giinagurahan', 'ang', 'usa', 'ka', 'marker', 'kon', 'timaan', 'niini', 'niadtong', 'Biyernes', 'sa', 'hapon', ',', 'Setyembre', '23', ',', '2022', 'sa', 'Leopoldo', 'T.', 'Ruiz', 'Administration', 'Grounds.', 'Gipadayag', 'kini', 'ni', 'National', 'Historical', 'Commission', 'of', 'the', 'Philippines', 'Chairman', 'Dr.', 'Rene', 'R.', 'Escalante', 'ug', 'SU', 'President', 'Dr.', 'Betty', 'Cernol', 'McCann.', 'Nihatag', 'sab', 'sa', 'ilang', 'mensahe', 'sila', 'si', 'Dr.', 'Earl', 'Jude', 'Paul', 'Cleope', ',', 'NHCP', 'Commissioner', 'ug', 'SU', 'Vice', 'President', 'for', 'Academic', 'Affairs.', 'Sukad', 'pa', 'niadtong', 'Hunyo', '19', ',', '2002', ',', 'giila', 'na', 'ang', 'SU', 'isip', 'usa', 'ka', 'national', 'historical', 'landmark', 'sa', 'National', 'Historical', 'Commission', 'of', 'the', 'Philippines', '(', 'NHCP', ')', 'pinaagi', 'sa', 'Resolution', 'No.', '7', ',', 'S.', '2002.', 'Nabutang', 'ang', 'SU', 'sa', 'level', '1', 'nga', 'klasipikasyon.', 'Mao', 'kini', 'ang', 'pinakataas', 'nga', 'klasipikasyon', 'kung', 'diin', 'aduna'y', 'opisyal', 'nga', 'pagdeklara', 'ug', 'marker', 'kon', 'timaan', 'gikan', 'sa', 'NHCP.', 'Sa', 'pagkakaron', ',', 'anaa', 'ang', 'karaang', 'landmark', 'sa', 'Silliman', 'Hall', 'ug', 'nakatapot', 'sa', 'pader', 'sa', 'dayong', 'pagsulod', 'sa', 'function', 'hall', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 5, 6, 6, 6, 6, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 1, 2, 2, 0, 3, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 3, 0, 0, 3, 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, 4, 4, 4, 4, 0, 0, 7, 8, 8, 8, 8, 8, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,558 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nibalik', 'na', 'gyud', 'human', 'ang', 'duha', 'ka', 'tuig', 'ang', 'inila', 'nga', 'Jan-Jan', 'Carnival', 'sa', 'Valencia', ',', 'isip', 'pagsaulog', 'sa', 'tinuig', 'nga', 'pista', 'sa', 'maong', 'lungsod', 'karong', 'Oktubre', '12.', 'Mahinumduman', 'nga', 'nawala', 'ang', 'karnabal', 'niadtong', 'tuig', '2020', 'ug', '2021', '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, 0, 0, 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, 7, 0] | cebuaner |
4,559 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BABAYI', 'NGA', 'NAANOD', 'SA', 'SUBA', 'SA', 'TAYASAN', ',', 'NAKIT-AN', 'NA', 'Nakit-an', 'na', 'ang', 'patay'ng', 'lawas', 'sa', 'usa', 'ka', 'babayi', 'nga', 'naanod', 'sa', 'baha', 'sa', 'Barangay', 'Bago', 'sa', 'lungsod', 'sa', 'Tayasan', 'mga', 'alas-7', 'sa', 'buntag', 'karong', 'adlawa', ',', 'Setyembre', '26', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'sa', 'Tayasan', 'ang', 'biktima', 'nga', 'si', 'Maribel', 'Mariano', 'Esteban', ',', '43', ',', 'balo', ',', 'ug', 'lumolupyo', 'sa', 'Sitio', 'Kabangaan', 'sa', 'maong', 'barangay.', 'Sumala', 'pa', 'sa', 'imbestigasyon', ',', 'nilabang', 'kagahapong', 'adlawa', 'ang', 'biktima', 'sa', 'suba', 'uban', 'ang', 'iyang', '19-anyos', 'nga', 'anak', 'ug', '15-anyos', 'nga', 'pag-umangkon.', 'Kusog', 'ang', 'bug-os', 'sa', 'tubig', 'tungod', 'sa', 'baha', 'hinungdan', 'nga', 'naanod', 'sila.', 'Na-rescue', 'ang', 'duha', 'ka', 'bata', ',', 'apan', 'nakaplagan', 'ang', 'patay'ng', 'lawas', 'sa', 'biktima', 'ganihang', 'buntag', 'mga', '300', 'meters', 'sa', 'lugar', 'nga', 'ilang', 'gianuran', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 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] | cebuaner |
4,560 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DAKONG', 'ISDA', ',', 'NAPANA', 'SA', 'RIZAL', 'BOULEVARD', 'Nahimong', 'masuwertehon', 'ang', 'Lunes', 'sa', 'usa', 'ka', 'mamanaay', '(', 'spear', 'fisher', ')', 'human', 'kini', 'nakadakop', 'og', 'dakong', 'mamsa', 'sa', 'kadagatan', 'duol', 'sa', 'Rizal', 'Boulevard', 'ning', 'dakbayan.', 'Moabot', 'og', '20', 'kilogramo', 'ang', 'gibug-aton', 'sa', 'maong', 'nadakpan', 'nga', 'mamsa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,561 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpahigayon', 'og', 'aerial', 'inspection', 'si', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'sa', 'mga', 'apektado', 'nga', 'lugar', 'sa', 'Bagyong', '#', 'KardingPH', 'sama', 'sa', 'Bulacan', ',', 'Nueva', 'Ecija', ',', 'ug', 'Tarlac', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 5, 0, 5, 6, 0, 0, 5, 0] | cebuaner |
4,562 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'kausbanan', 'sa', 'presyo', 'sa', 'gasolina', 'sa', 'Pilipinas', 'SHELL', 'ugmang', 'adlawa', ',', 'Setyembre', '27', ',', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,563 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pinakagrabe', 'nga', 'naigo', 'sa', 'Bagyong', '#', 'KardingPH', 'ang', 'Bongliw', ',', 'usa', 'ka', 'komunidad', 'sa', 'Barangay', 'Rizal', ',', 'Panukulan', ',', 'Polillo', 'Island.', 'Sumala', 'pa', 'ni', 'Fr.', 'Bong', 'Sarabia', ',', 'CM.', 'Bono', 'Mari', 'ug', 'St.', 'Vincent', 'de', 'Paul', 'POGI', 'Social', 'Dev’t', ',', 'Inc.', 'kinsa', 'nagsugod', 'na', 'sab', 'sa', 'ilang', 'pauna', 'nga', 'relief', 'work', 'aron', 'makatabang', 'sa', 'mga', 'residente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 8, 8, 0, 5, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,564 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naguba', 'ang', 'mga', 'kabalayan', 'ug', 'imprastraktura', 'human', 'niigo', 'ang', 'Bagyong', '#', 'KardingPH', 'sa', 'munisipalidad', 'sa', 'Burdeos', 'sa', 'Polillo', 'Island', ',', 'Quezon.', 'Nag-landfall', 'kini', 'sa', 'naasoy', 'nga', 'lugar', 'niadtong', 'Domiggo', 'sa', 'gabie', ',', 'Setyembre', '25', ',', '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, 7, 8, 8, 0, 0, 0, 5, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,565 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naguba', 'ang', 'mga', 'kabalayan', 'ug', 'sakayan', 'sa', 'Barangay', 'Paltic', ',', 'Dingalan', ',', 'Aurora', 'human', 'sa', 'Bagyong', '#', 'KardingPH', 'nga', 'niigo', 'sa', 'Luzon', 'niadtong', 'Dominggo', ',', 'Setyembre', '25', ',', '2022.', 'Nihimo', 'sa', 'ikaduhang', 'pag-landfall', 'ang', 'bagyo', 'sa', 'naasoy', 'nga', 'lungsod', 'kagabii', ',', '8:20', 'sa', '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, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 7, 8, 8, 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] | cebuaner |
4,566 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'klase', 'karong', 'adlawa', '(', 'Sept.', '26', ',', '2022', ')', 'sa', '6', 'ka', 'lugar', 'sa', 'Negros', 'Oriental', 'tungod', 'sa', 'epekto', 'sa', 'Bagyong', '#', 'KardingPH.', 'Kini', 'sigon', 'sa', 'anunsyo', 'sa', 'mga', 'lokal', 'nga', 'kagamhanan', 'sa', 'maong', 'mga', 'lungsod', 'ug', 'dakbayan', ',', 'ingon', 'man', 'sa', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Office', 'sa', 'DepEd', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 3, 4, 4, 4, 4, 4, 0, 3, 4, 4, 0] | cebuaner |
4,567 | 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', 'pampubliko', 'og', 'pribadong', 'eskuwelahan', 'sa', 'lungsod', 'sa', 'Tayasan', 'karong', 'Lunes', ',', 'Sept.', '26', ',', '2022', ',', 'tungod', 'sa', 'Bagyong', '#', 'KardingPH.', 'Kini', 'sigon', 'sa', 'mando', 'nga', 'gipagawas', 'ni', 'Mayor', 'Susano', 'Ruperto', 'karong', 'adlawa', ',', 'Sept.', '25.', 'Sumala', 'pa', 'ni', 'Ruperto', ',', 'gisuspenso', 'niya', 'ang', 'klase', 'sa', 'tanang', 'tunghaan', 'sa', 'iyang', 'lungsod', 'human', 'nga', 'nisaka', 'ang', 'tubig', 'sa', 'mga', 'suba', 'sa', 'Panayawan', 'ug', 'Jilabangan', 'tungod', 'sa', 'walay', 'puas', 'nga', 'pag-ulan', 'didto.', 'Dugang', 'pa', 'sa', 'mayor', ',', 'dunay', 'pipila', 'ka', 'residente', 'nga', 'gikatahong', 'nalumos', 'sa', 'Sitio', 'Cabangahan', ',', 'Barangay', 'Bago', 'human', 'sila', 'nisulay', 'pagtabok', 'sa', 'Panayawan', 'River', 'ganinang', 'alas-6', 'sa', 'hapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 5, 0, 5, 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, 5, 6, 0, 0, 0, 0, 0] | cebuaner |
4,568 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['IKOG', 'NI', 'SUPER', 'TYPHOON', 'KARDING', ',', 'NABATI', 'SA', 'SIQUIJOR', 'Bisan', 'pa', 'man', 'og', 'dili', 'direktang', 'maigo', 'ang', 'probinsya', 'sa', 'Siquijor', 'sa', 'Bagyong', '#', 'KardingPH', ',', 'mabati', 'na', 'ang', 'epekto', 'niini', 'didto.', 'Makita', 'niini', 'nga', 'video', 'nga', 'napalid', 'ang', 'pipila', 'ka', 'tolda', 'sa', 'pantalan', 'sa', 'Siquijor', 'tungod', 'sa', 'kusog', 'nga', 'hangin', 'nga', 'dala', 'sa', 'ikog', 'kon', 'trough', 'sa', 'bagyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 0, 7, 8, 8, 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] | cebuaner |
4,569 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpagawas', 'ang', 'PAGASA', 'og', 'rainfall', 'advisory', 'sa', 'Negros', 'Oriental', ',', 'habagatang', 'Sugbo', ',', 'ug', 'Siquijor.', 'Padayong', 'makasinati', 'ang', 'atong', 'probinsya', 'og', 'mga', 'pag-ulan-ulan', 'karong', 'Domingo', ',', 'Sept.', '25', ',', '2022', ',', 'tungod', 'sa', 'epekto', 'sa', 'Hanging', 'Habagat', 'ug', 'sa', 'trough', 'kon', 'ikog', 'sa', 'Bagyong', '#', 'KardingPH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 6, 0, 0, 5, 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, 7, 8, 8, 0] | cebuaner |
4,570 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SIGNAL', 'NO.', '5', ',', 'GIISA', 'NA', 'TUNGOD', 'SA', 'BAGYONG', 'KARDING', 'Gipaubos', 'na', 'ang', 'pipila', 'ka', 'lugar', 'sa', 'Luzon', 'sa', 'Signal', 'No.', '5', 'tungod', 'sa', 'hulga', 'sa', 'Super', 'Typhoon', '#', 'KardingPH.', 'Ang', 'Signal', 'No.', '5', 'mao', 'nga', 'kinatas-an', 'ang', 'tropical', 'cyclone', 'wind', 'signal', 'sa', 'PAGASA.', 'Gibanabanang', 'makasinati', 'og', 'mga', 'kusog', 'nga', 'ulan', 'ug', 'hangin', 'nga', 'moabot', 'og', '185', 'km', '/', 'h', 'ang', 'maong', 'mga', 'dapit', 'sa', 'mosunod', 'nga', '12', 'oras', 'Samtang', 'diri', 'sa', 'Negros', 'Oriental', ',', 'padayong', 'makasinati', 'ang', 'atong', 'probinsya', 'og', 'mga', 'pag-ulan', 'tungod', 'sa', 'epekto', 'sa', 'Hanging', 'Habagat', 'ug', 'sa', 'trough', 'kon', 'ikog', 'sa', 'bagyong', 'Karding', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 5, 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, 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, 5, 6, 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 |
4,571 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KARDING', ',', 'USA', 'NA', 'KA', 'SUPER', 'TYPHOON', 'Mas', 'nikusog', 'pa', 'ug', 'nahimo', 'nang', 'hingpit', 'nga', 'super', 'typhoon', 'ang', 'Bagyong', '#', 'KardingPH.', 'Kini', 'sumala', 'pa', 'sa', 'latest', 'bulletin', 'sa', 'PAGASA', 'nga', 'gipagawas', 'karong', 'alas-8', 'sa', 'buntag', 'Domingo', ',', 'Sep.', '25', ',', '2022.', 'Bisan', 'pa', 'man', 'og', 'walay', 'tropical', 'cyclone', 'warning', 'signal', 'sa', 'Negros', 'Oriental', 'ug', 'Kabisay-an', ',', 'makasinati', 'gihapon', 'ang', 'atong', 'probinsya', 'og', 'mga', 'pag-ulan', 'ug', 'monsoon', 'rains', 'tungod', 'sa', 'Hanging', 'Habagat', 'nga', 'mas', 'gipakusgan', 'sa', 'bagyo.', 'Ulahing', 'nasuta', 'sa', 'PAGASA', 'ang', 'Bagyong', 'Karding', '230', 'km', 'East', 'of', 'Infanta', ',', 'Quezon.', 'Nagdala', 'kini', 'og', 'hangin', 'nga', 'moabot', 'sa', '185', 'km', '/', 'h', 'ang', 'gikusgon.', 'Ang', 'bugso', 'niini', ',', 'moabot', 'sab', 'og', '230', 'km', '/', 'h.', 'Gipaubos', 'na', 'sa', 'Signal', 'No.', '4', 'ang', 'Polillo', 'Islands', ',', 'samtang', 'anaa', 'sa', 'Signal', 'No.', '3', 'ang', 'pipila', 'ka', 'bahin', 'sa', 'Metro', 'Manila', 'ug', 'Luzon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 3, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 7, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0] | cebuaner |
4,572 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BAKA', 'GUSTO', 'MO-ESKUWELA', '!', 'LOOK', ':', 'Nabulabog', 'ang', 'klase', 'ni', 'Ma'am', 'Neneng', 'Portrias', 'Garaula', 'human', 'aduna'y', 'usa', 'ka', 'puting', 'baka', 'ang', 'nisulod', 'sa', 'iyang', 'classroom', 'niadtong', 'Setyembre', '22', ',', '2022.', 'Sumala', 'pa', 'ni', 'Ma'am', 'Garaula', ',', 'maestra', 'sa', 'grade', 'six', 'sa', 'Larapan', 'Elementary', 'School', 'sa', 'Jagna', 'lalawigan', 'sa', 'Bohol', ',', 'nakuratan', 'siya', 'ug', 'ang', 'iyang', 'tibuok', 'klase', 'sa', 'dihang', 'nisulod', 'ang', 'baka', 'samtang', 'nagklase', 'siya.', 'Nagtinabangay', 'ang', 'mga', 'estudyante', 'sa', 'pag-abog', 'sa', 'baka', 'aron', 'mogawas', 'kini', 'sa', 'classroom.', 'Tungod', 'niini', ',', 'nanawagan', 'sab', 'si', 'Ma'am', 'Garaula', 'sa', 'mga', 'namuhi', 'og', 'hayop', 'duol', 'sa', 'eskuwelahan', 'nga', 'bantayan', 'kini', 'aron', 'dili', 'makabuhi', 'ug', 'mosulod', 'sa', 'classroom', 'sama', 'sa', 'nahitabo', 'nila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 5, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,573 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NATIONWIDE', 'BAKUNAHANG', 'BAYAN', ',', 'IPAHIGAYON', 'SUNOD', 'SEMANA', ';', 'MODAWAT', 'OG', 'WALK-INS', 'Maghatag', 'na', 'sab', 'og', 'primary', 'o', 'booster', 'doses', 'sa', 'Covid-19', 'vaccines', 'ang', 'kagamhanan', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'isip', 'pagsuporta', 'sa', 'Bakunahang', 'Bayan', ':', 'PinasLakas', 'Special', 'Vaccination', 'Days', 'sunod', 'semana', ',', 'Setyembre', '26-30', ',', '2022.', 'Moalagad', 'sa', 'paghatag', 'sa', 'bakuna', 'ang', 'mga', 'healthcare', 'workers', 'ug', 'uban', 'pang', 'government', 'employees', 'nga', 'gi-assign', 'sa', 'City', 'Inter-Agency', 'Task', 'Force', 'for', 'the', 'Management', 'of', 'Emerging', 'Infectious', 'Diseases', 'nga', 'gipangulohan', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo.', 'Gikompirma', 'ni', 'City', 'Health', 'Officer', 'Dr.', 'Maria', 'Sarah', 'B.', 'Talla', 'nga', '3', 'ka', 'vaccination', 'sites', 'ang', 'magdungan', 'sa', 'paghatag', 'og', 'bakuna', 'ngadto', 'sa', 'mga', 'bata', 'nga', 'nag-edad', 'og', '5-17', 'anyos', 'ug', 'sa', 'mga', 'hamtong.', 'Ang', 'vaccination', 'sites', 'mao', 'ang', 'Robinson', 'Place', 'Dumaguete', ',', 'City', 'Health', 'Main', 'Office', ',', 'ug', 'City', 'Health', 'Office', '2', 'sa', 'Barangay', 'Talay.', 'Tumong', 'sa', 'usa', 'ka', 'semana', 'nga', 'national', 'special', 'vaccination', 'drive', 'nga', 'mapakusgan', 'ang', 'mga', 'paningkamot', 'niini', 'aron', 'makab-ot', 'ang', 'national', 'targets', 'sa', 'paghatag', 'og', 'bakuna', 'batok', 'Covid-19.', 'Dili', 'na', 'kinahanglan', 'nga', 'magparehistro', 'tungod', 'modawat', 'sila', 'og', 'mga', 'walk-in', 'clients', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 0, 0, 0, 0, 0, 0, 5, 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 6, 6, 0, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,574 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NAPALGANG', 'PATAYNG', 'LAWAS', 'SA', 'USA', 'KA', 'DALAGA', ',', 'NAILHAN', 'NA', 'Napalgan', 'ang', 'patay'ng', 'lawas', 'sa', 'babayi', 'sa', 'usa', 'ka', 'hotel', 'sa', 'Real', 'St.', 'atbang', 'sa', 'NORECO', '2', 'sa', 'dakbayan', 'Dumaguete', 'ganihang', 'udto', ',', 'Setyembre', '23', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Christine', 'Joy', 'Dumao', 'Labe', ',', '25-anyos', ',', 'ug', 'lumolupyo', 'sa', 'Barangay', 'Inalad', ',', 'Poblacion', ',', 'Pamplona.', 'Sumala', 'pa', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'niagi', 'ang', 'receptionist', 'sa', 'maong', 'hotel', 'sa', 'mga', 'kwarto', 'sa', '4th', 'floor', 'ug', 'gituktok', 'ang', 'room', 'sa', 'biktima', 'apan', 'wala'y', 'nitubag.', 'Pag-abri', 'sa', 'purtahan', ',', 'napalgan', 'niya', 'ang', 'biktima', 'nga', 'wala', 'na'y', 'kinabuhi', 'ug', 'dali', 'nga', 'nitawag', 'og', 'tabang', 'sa', 'kapulisan.', 'Dali', 'sab', 'nga', 'niresponde', 'ang', 'Rescue', 'Emergency', 'Response', 'Team', 'sakay', 'si', 'Jonathan', 'Anggana', ',', 'usa', 'ka', 'Team', 'Leader', 'uban', 'ni', 'Dr.', 'Kenneth', 'Coo', 'kinsa', 'nideklara', 'nga', 'patay', 'na', 'ang', 'biktima', 'mga', '1:18', 'sa', 'hapon.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', '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. | [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, 5, 6, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,575 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PROYEKTO', 'NGA', 'PAGTUKOD', 'OG', 'BOULDER', 'DIKES', ',', 'KANAL', 'SA', 'BANILAD-MANGNAO', ',', 'NAGPADAYON', 'Nagpadayon', 'ang', 'pagtrabaho', 'sa', 'proyekto', 'sa', 'Barangay', 'Banilad', 'hangtod', 'sa', 'Barangay', 'Mangnao', 'nga', 'mokontrolar', 'sa', 'baha', 'ilabi', 'na', 'sa', 'panahon', 'sa', 'ting-ulan', 'ug', 'bagyo.', 'Nigahin', 'ang', 'administrasyon', 'ni', 'Mayor', 'Felipe', 'Remollo', 'og', 'mokabat', 'sa', 'P25', 'milyones', 'nga', 'pondo', 'alang', 'sa', 'maong', 'proyekto.', 'Tumong', 'niini', 'nga', 'palapdan', 'ang', 'mga', 'boulder', 'dikes', ',', 'magbutang', 'og', 'box', 'culvert', 'ug', 'kanal', 'aron', 'makontrol', 'ug', 'mosubay', 'sa', 'naandan', 'nga', 'agianan', 'ang', 'dakong', 'bul-og', 'sa', 'tubig', 'nga', 'gagikan', 'sa', 'Valencia', 'paingon', 'sa', 'Lagnasan', 'Creek', 'ug', 'diretso', 'na', 'sa', 'Ihalason', 'Beach', 'ug', 'dili', 'na', 'moawas', 'sa', 'mga', 'kadalanan', 'ug', 'kabalayan.', 'Nagpadayon', 'ang', 'proyekto', 'apan', 'aduna'y', 'gipanglaktawan', 'usa', 'nga', 'mga', 'apektadong', 'luna', 'tungod', 'sa', 'pagdumili', 'sa', 'mga', 'nagpailang', 'tag-iya', 'niini', 'nga', 'gamiton', 'ang', 'ilang', 'propredad', 'aron', 'agian', 'ug', 'tukuran', 'sa', 'mga', 'boulder', 'dikes', 'aron', 'unta', 'malikayan', 'na', 'ang', 'pag-awas', 'sa', 'tubig', 'o', 'pagbaha', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 6, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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] | cebuaner |
4,576 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NAPALGANG', 'PATAYNG', 'LAWAS', 'SA', 'USA', 'KA', 'DALAGA', ',', 'NAILHAN', 'NA', 'Napalgan', 'ang', 'patay'ng', 'lawas', 'sa', 'babayi', 'sa', 'usa', 'ka', 'hotel', 'sa', 'Real', 'St.', 'atbang', 'sa', 'NORECO', '2', 'sa', 'dakbayan', 'Dumaguete', 'ganihang', 'udto', ',', 'Setyembre', '23', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Christine', 'Joy', 'Dumao', 'Labe', ',', '25-anyos', ',', 'ug', 'lumolupyo', 'sa', 'Barangay', 'Inalad', ',', 'Poblacion', ',', 'Pamplona.', 'Sumala', 'pa', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'niagi', 'ang', 'receptionist', 'sa', 'maong', 'hotel', 'sa', 'mga', 'kwarto', 'sa', '4th', 'floor', 'ug', 'gituktok', 'ang', 'room', 'sa', 'biktima', 'apan', 'wala'y', 'nitubag.', 'Pag-abri', 'sa', 'purtahan', ',', 'napalgan', 'niya', 'ang', 'biktima', 'nga', 'wala', 'na'y', 'kinabuhi', 'ug', 'dali', 'nga', 'nitawag', 'og', 'tabang', 'sa', 'kapulisan.', 'Dali', 'sab', 'nga', 'niresponde', 'ang', 'Rescue', 'Emergency', 'Response', 'Team', 'sakay', 'si', 'Jonathan', 'Anggana', ',', 'usa', 'ka', 'Team', 'Leader', 'uban', 'ni', 'Dr.', 'Kenneth', 'Coo', 'kinsa', 'nideklara', 'nga', 'patay', 'na', 'ang', 'biktima', 'mga', '1:18', 'sa', 'hapon.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', '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. | [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, 5, 6, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,577 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JULIA', 'BARRETTO', 'NAG-RELAX', 'UG', 'NAGPAMASAHE', 'SA', 'PANTAWAN', 'PEOPLE’S', 'PARK', 'Nibisita', 'si', 'Julia', 'Barretto', 'ug', 'iyang', 'grupo', 'sa', 'Pantawan', 'People', ''s', 'Park', 'sa', 'Rizal', 'Boulevard', 'aron', 'masinati', 'ang', 'foot', 'massage', 'ug', 'makita', 'ang', 'mga', 'pamilya', 'nga', 'nakigbahin', 'sa', 'ilang', 'paborito', 'nga', 'mga', 'outdoor', 'hobbies', 'atol', 'sa', 'iyang', 'pag-anhi', 'sa', 'dakbayan.', 'Nakigkita', 'sab', 'si', 'Barretto', 'nila', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo', 'ug', 'SK', 'President', 'Renz', 'Macion', '(', 'Deputy', 'Mayor', 'for', 'Tourism', ')', 'ug', 'gihisgutan', 'ang', 'inspirasyon', 'ug', 'features', 'sa', 'Pantawan', 'People’s', 'Park.', 'I-feature', 'ni', 'Barretto', 'ang', 'dakbayan', 'sa', 'Dumaguete', 'sa', 'iyang', 'travel', 'series', 'nga', '"', 'Juju', 'on', 'the', 'Go', '"', 'sa', 'iyang', 'YouTube', 'channel', 'sa', 'koordinasyon', 'ni', 'City', 'Tourism', 'Officer', 'Jacqueline', 'V.', 'Antonio', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 2, 0, 0, 0, 0, 5, 6, 6, 0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 2, 0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0] | cebuaner |
4,578 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'NAKADAWAT', 'OG', '5-STAR', 'RATING', 'ALANG', 'SA', 'ENERGY', ',', 'FUEL', 'CONSERVATION', 'Nakadawat', 'ang', 'kagamhanan', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'og', 'puntos', 'nga', '94', 'nga', 'katumbas', 'sa', '5', 'star', 'rating', 'ug', 'Grade', 'A', 'alang', 'sa', 'labing', 'maayo', 'nga', 'mga', 'buhat', 'aron', 'pagkonserbar', 'sa', 'elektrisidad', 'ug', 'gasolina.', 'Base', 'kini', 'gipahigayong', 'assessment', 'sa', 'Energy', 'Audit', 'Team', 'gikan', 'sa', 'Department', 'of', 'Energy', '(', 'DOE', ')', 'ug', 'Department', 'of', 'Science', 'and', 'Technology-', 'Industrial', 'Technology', 'Development', 'Institute', '(', 'DOST-ITDI', ')', '.', 'Aduna'y', 'gipahigayong', 'conference', 'uban', 'sa', 'Energy', 'Audit', 'Team', 'aron', 'pagsusi', 'sa', 'mga', 'dokumento', 'ug', 'records', 'nga', 'may', 'kalabutan', 'sa', 'pagkonserbar', 'sa', 'elektrisidad', 'ug', 'gasolina.', 'Nagpahigayon', 'sab', 'ang', 'Energy', 'Audit', 'Team', 'og', 'random', 'checking', 'sa', 'energy-consuming', 'equipment', 'sa', 'mga', 'opisina', 'ug', 'pagsukod', 'sa', 'konsumo', 'sa', 'kuryente', 'sa', 'mga', 'building', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 3, 4, 4, 0, 0, 3, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,579 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['3', 'KA', 'TAAS', 'NGA', 'OPISYAL', 'SA', 'NPA', 'UG', '13', 'UBAN', 'PA', ',', 'MITAHAN', 'SA', 'MGA', 'AWTORIDAD', 'Misurender', 'ang', '16', 'ka', 'giingong', 'rebelde', 'apil', 'na', 'ang', '3', 'ka', 'anaa', 'sa', 'ranggo', 'nga', 'mga', 'lider', 'sa', 'News', 'People', ''s', 'Army', '(', 'NPA', ')', 'ngadto', 'Guihulngan', 'City', 'Task', 'Force', 'to', 'End', 'Local', 'Communist', 'Armed', 'Conflict', 'ug', 'uban', 'pang', 'state', 'troopers', 'niadtong', 'Martes', ',', 'Setyembre', '20', ',', '2022.', 'Nagpahigayon', 'og', 'ceremonial', 'declaration', 'ug', 'turnover', 'sa', 'Barangay', 'Poblacion', 'sa', 'Guilhulngan', 'City', 'nga', 'gipangulohan', 'ni', 'Brig.', 'Gen.', 'Inocencio', 'Pasaporte', ',', '303rd', 'Infantry', 'Brigade', 'commander.', 'Sumala', 'pa', 'sa', 'kasundalohan', ',', 'lakip', 'sa', 'mga', 'misurender', 'ang', 'commanding', 'officer', 'sa', 'Sentro', 'de', 'Grabidad', 'Platoon', ',', 'usa', 'ka', 'political', 'adviser', 'ug', 'medical', 'officer.', 'Nahimo', 'kini', 'silang', 'parte', 'sa', 'Central', 'Negros', '1', ',', 'Komiteng', 'Rehiyon-Negros', ',', 'Cebu', ',', 'Bohol', ',', 'ug', 'Siquijor', '(', 'KR-NCBS', ')', 'ilalom', 'sa', 'Leonardo', 'Panaligam', 'Command.', 'Matod', 'pa', 'sa', 'commanding', 'officer', 'nga', 'giila', 'isip', 'si', 'Ka', 'Jimbo', ',', 'ang', 'kakapoy', ',', 'kahadlok', 'ug', 'kagutom', 'ang', 'pinakalisod', 'nilang', 'nasinati', 'sa', 'kabukiran.', 'Usa', 'sab', 'sa', 'rason', 'sa', 'ilang', 'pagsurender', 'mao', 'ang', 'kamingaw', 'nga', 'ilang', 'gibati', 'para', 'sa', 'ilang', 'pamilya', 'nga', 'dugay', 'na', 'nilang', 'gibiyaan', 'tungod', 'sa', 'mga', 'dili', 'tinuod', 'nga', 'mga', 'saad', 'sa', 'NPA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 2, 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, 3, 0] | cebuaner |
4,580 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JULIA', 'BARRETTO', 'NAKIGPULONG', 'SA', 'MGA', 'LOKAL', 'NGA', 'OPISYAL', 'SA', 'DUMAGUETE', 'Nibisita', 'ang', 'aktres', 'ug', 'blogger', 'nga', 'si', 'Julia', 'Barretto', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo', 'ug', 'nakigkita', 'sa', 'uban', 'pang', 'lokal', 'nga', 'mga', 'opisyal', 'sa', 'city', 'hall', 'kagahapon', 'sa', 'buntag', ',', 'Setyembre', '21', ',', '2022.', 'I-feature', 'ni', 'Barretto', 'ang', 'dakbayan', 'sa', 'Dumaguete', 'sa', 'iyang', 'travel', 'series', 'nga', ''Juju', 'on', 'the', 'Go', ''', 'sa', 'iyang', 'YouTube', 'channel.', 'Sa', 'wala', 'pa', 'ang', 'iyang', 'pagbisita', 'sa', 'dakbayan', ',', 'gi-feature', 'na', 'sab', 'niya', 'ang', 'mga', 'sikat', 'nga', 'local', 'ug', 'foreign', 'tourist', 'destinations', 'sa', 'iyang', 'mga', 'niaging', 'episodes.', 'Gipasiugda', 'niya', 'ang', 'linutoan', ',', 'makasaysayon', 'nga', 'mga', 'dapit', ',', 'natural', 'wonders', 'ug', 'talagsaon', 'nga', 'kasinatian', 'sa', 'maong', 'mga', 'lugar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,581 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SUMC', 'FOUNDATION', 'INCORPORATED', ',', 'NIDONAR', 'OG', 'LIBOAN', 'KA', 'MGA', 'PPE', 'SA', 'DUMAGUETE', 'Nagdonar', 'ang', 'Silliman', 'University', 'Foundation', 'Incorporated', 'og', 'liboan', 'ka', 'mga', 'Personal', 'Protective', 'Equipment', '(', 'PPEs', ')', 'ngadto', 'sa', 'Dumaguete', 'City', 'Health', 'Office.', 'Pagpakita', 'kini', 'nila', 'og', 'suporta', 'sa', 'mga', 'paningkamot', 'sa', 'City', 'Inter-Agency', 'Task', 'Force', 'for', 'the', 'Management', 'of', 'Emerging', 'Infectious', 'Diseases', 'aron', 'makontrolar', 'ang', 'pagkuyanap', 'sa', 'impeksyon', 'sa', 'Covid-19.', 'Gidawat', 'ni', 'Mayor', 'Felipe', 'Antonio', 'B.', 'Remollo', 'ug', 'City', 'Health', 'Officer', 'Dr.', 'Maria', 'Sarah', 'B.', 'Talla', 'ang', 'mga', 'gidonar', 'nga', 'PPEs', 'gikan', 'ni', 'SUMC', 'Foundation', 'Inc.', 'President', 'Roberto', 'D.', 'Montebon', ',', 'SUMC', 'Vice-President', 'for', 'Nursing', 'and', 'Patient', 'Services', 'Fredita', 'Tan', 'ug', 'SUMC', 'VP', 'for', 'Finance', 'ug', 'OIC', 'Pamela', 'Fontanosa.', 'Lakip', 'sa', 'mga', 'gidonar', 'nga', 'PPE', 'ang', '3,500', 'ka', 'isolation', 'kits', ',', '15,000', 'ka', 'piraso', 'sa', 'N95', 'masks', 'ug', '8,000', 'ka', 'coveralls.', 'Magamit', 'kini', 'sa', 'mga', 'health', 'workers', 'ug', 'uban', 'pang', 'Covid-19', 'frontliners', 'nga', 'na-assign', 'sa', '30', 'ka', 'mga', 'barangay', 'sa', 'dakbayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 3, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 1, 2, 2, 0, 3, 0, 0, 0, 0, 0, 0, 3, 4, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,582 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PISI', ',', 'CEMAP', 'NAGPAHIGAYON', 'OG', 'INFORMATION', 'AND', 'EDUCATION', 'CAMPAIGN', 'ALANG', 'SA', 'MGA', 'RETAILER', 'SA', 'KABILYA', 'Nagpahigayon', 'og', 'Information', 'and', 'Education', 'Campaign', 'ang', 'Philippine', 'Iron', 'and', 'Steel', 'Institute', '(', 'PISI', ')', 'ug', 'Cement', 'Manufacturers', 'Association', 'of', 'the', 'Philippines', '(', 'CeMAP', ')', 'alang', 'sa', 'mga', 'retailer', 'aron', 'pag-edukar', 'kung', 'unsa', 'ang', 'insakto', 'nga', 'kalidad', 'sa', 'kabilya', 'nga', 'pwedeng', 'ibaligya.', 'Uban', 'sa', 'Department', 'of', 'Trade', 'and', 'Industry', '(', 'DTI', ')', ',', 'giimbitar', 'nila', 'ang', 'pipila', 'ka', 'mga', 'retailers', 'gikan', 'sa', 'nagkalain-laing', 'bahin', 'sa', 'probinsiya', 'sa', 'Negros', 'Oriental', 'aron', 'maminaw', 'og', 'mapahibalo', 'sa', 'mga', 'bag-ong', 'standard', 'sa', 'kabilya.', 'Sumala', 'pa', 'sa', 'representante', 'sa', 'PISI', ',', 'ang', 'bag-ong', 'standard', 'sa', 'kabilya', 'aduna'y', 'makita', 'nga', 'logo', 'sa', 'naggama', ',', 'sukod', 'sa', 'kabilya', ',', 'color', 'coding', 'ug', 'grado', 'niini.', 'Mahibal-an', 'sab', 'kung', 'insakto', 'ba', 'ang', 'napadala', 'nga', 'kabilya', 'sa', 'aktwal', 'nga', 'gibug-aton', 'ug', 'katas-on', 'niini.', 'Ang', 'PNS', '49', ':', '2020', ',', 'aduna'y', '+', '/', '-', '6', '%', 'tolerance', ',', 'samtang', 'ang', 'PNS', '211', ':', '2002', ',', 'aduna'y', '+', '/', '-', '10', '%', '.', 'Mahibal-an', 'ang', 'pagkwenta', 'niini', 'gamit', 'ang', 'standard', 'formula.', 'Gisubli', 'nila', 'nga', 'angayang', 'mahibalo', 'ang', 'mga', 'retailers', 'niining', 'impormasyon', 'aron', 'dili', 'sila', 'madakpan', 'sa', 'DTI', 'nga', 'magbaligya', 'og', 'mga', 'sub-standard', 'nga', 'mga', 'materyales.', 'Apan', 'matod', 'pa', 'sab', 'sa', 'mga', 'retailers', ',', 'angayan', 'nga', 'unang', 'i-monitor', 'kadtong', 'mga', 'kompanya', 'nga', 'tiggama', 'sa', 'mga', 'kabilya', 'aron', 'dili', 'sila', 'makapalit', 'og', 'mga', 'sub-standard', 'nga', 'mga', 'materyales.', 'Moepekto', 'karong', 'Enero', '1', ',', '2023', 'ang', 'strikto', 'nga', 'pagpatuman', 'sa', 'giingong', 'standard', 'sa', 'kabilya', 'ug', 'magsugod', 'na', 'sila', 'og', 'pakalit', 'nga', 'inspeksyon', 'sa', 'mga', 'tindahan', 'nga', 'gabaligya', 'niining', 'mga', 'materyales.', '#', 'NewsBite', '|', 'via', 'Jan', 'Aarron', 'Dela', 'Torre', ',', 'correspondent'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 2, 2, 2, 0, 0] | cebuaner |
4,583 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DEPED', ':', 'P532-M', 'BUDGET', 'ALANG', 'SA', 'SPED', 'SUNOD', 'TUIG', ',', 'GIBASURA', 'Gibasura', 'ang', 'P532-million', 'nga', 'special', 'education', '(', 'SPED', ')', 'budget', 'sa', '2023', 'National', 'Expenditure', 'Program', '(', 'NEP', ')', '.', 'Gisubli', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'nga', 'orihinal', 'nilang', 'gisugyot', 'ang', 'maong', 'budget', 'apan', 'gibasura', 'kini', 'sa', 'ulahi', ',', 'kini', 'human', 'aduna'y', 'mga', 'netizens', 'nga', 'nireklamo', 'nga', 'dili', 'patas', 'ang', 'zero-budget', 'allocation', 'alang', 'sa', 'SPED', 'program', 'sunod', 'tuig.', 'Sumala', 'pa', 'sa', 'tigpamaba', 'sa', 'DepEd', 'nga', 'si', 'Michael', 'Poa', ',', 'nagbuhat', 'na', 'sila', 'og', '"', 'internal', 'adjustments', '"', 'sa', 'mga', 'pwedeng', 'magamit', 'sa', 'pag-mintinar', 'ug', 'uban', 'pang', 'gasto', 'sa', 'pagpadagan', 'sa', 'mga', 'tunghaan', 'aron', 'masuportahan', 'ang', 'SPED', 'program', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 7, 8, 8, 8, 8, 8, 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, 7, 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, 7, 0, 0] | cebuaner |
4,584 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JUST', 'IN', ':', 'PAG-POSTPONE', 'SA', 'BRGY', ',', 'SK', 'ELECTIONS', ',', 'GIAPROBAHAN', 'NA', 'Giaprobahan', 'sa', 'House', 'of', 'Representatives', 'ang', 'House', 'Bill', '4673', 'nga', 'pag-postpone', 'sa', 'eleksyon', 'sa', 'Barangay', 'ug', 'Sangguniang', 'Kabataan', '(', 'SK', ')', 'karong', 'Disyembre', '5', ',', '2022', 'ngadto', 'sa', 'unang', 'Lunes', 'sa', 'Disyembre', '2023.', 'Aprobado', 'kini', 'sa', 'ikatulo', 'ang', 'ulahing', 'pagbasa', 'nga', 'aduna'y', '264', 'yes', 'votes', ',', '6', 'no', 'votes', 'ug', '3', 'ka', 'abstentions', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 4, 0, 7, 8, 8, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,585 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'ipahigayon', 'nga', 'Local', 'Job', 'Fair', 'sa', 'Robinsons', 'Place', 'sa', 'Barangay', 'Calindagan', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'Sabado', ',', 'Setyembre', '24', ',', '2022.', 'Ipahigayon', 'kini', 'sa', 'City', 'Government', 'sa', 'Dumaguete', 'pinaagi', 'sa', 'Public', 'Employment', 'Service', 'Office', 'ug', 'sa', 'pakigtambayayong', 'sa', 'Make', 'Sense', 'Philippines.', 'Ang', 'mga', 'aplikante', ',', 'mahimong', 'morehistro', 'pinaagi', 'ani', 'nga', 'link', ':', 'https', ':', '/', '/', 'bit.ly', '/', 'DumagueteJobFair', 'Anaa', 'sab', 'ang', 'mga', 'representante', 'gikan', 'sa', 'mga', 'kaubang', 'ahensya', 'sama', 'sa', 'DOLE', ',', 'OWWA', ',', 'ug', 'TESDA', 'aron', 'motubag', 'sa', 'mga', 'pangutana', 'ug', 'kabalaka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 4, 4, 4, 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, 3, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,586 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['RADIO', 'ANNOUNCER', 'SA', 'MABINAY', ',', 'GIDUNGGAB', 'PATAY', 'Patay', 'ang', 'usa', 'ka', 'radio', 'anchor', 'ug', 'commentator', 'sa', 'lungsod', 'sa', 'Mabinay', 'human', 'siya', 'gidunggab', 'niadtong', 'Dominggo', 'sa', 'gabie', ',', 'Setyembre', '18', ',', '2022', ',', 'sa', 'Barangay', 'Himocdongan.', 'Sumala', 'pa', 'ni', 'Police', 'Maj.', 'Paul', 'Vincent', 'Dumaguing', ',', 'hepe', 'sa', 'Mabinay', 'Police', 'Station', ',', 'giila', 'ang', 'biktima', 'nga', 'si', 'Rey', 'Blanco', ',', 'kinsa', 'nagtrabaho', 'sa', 'Power', '102.1', 'DYRY', 'RFM', 'nga', 'usa', 'ka', 'radio', 'station', 'nga', 'nakabase', 'sa', 'Mabinay.', 'Nasayran', 'sa', 'imbestigasyon', 'nga', 'niadto', 'si', 'Blanco', 'sa', 'balay', 'sa', 'igsuon', 'sa', 'suspek', 'kung', 'diin', 'nahitabo', 'ang', 'maong', 'krimen.', 'Gisulayan', 'pagdala', 'sa', 'mga', 'silingan', 'ug', 'opisyal', 'sa', 'barangay', 'si', 'Blanco', 'sa', 'Mabinay', 'Community', 'Hospital', 'apan', 'gideklara', 'kini', 'nga', 'patay', 'na', 'sa', 'nag-atiman', 'nga', 'mananambal.', 'Boluntaryo', 'nga', 'ni-surrender', 'ang', 'suspek', 'sa', 'nagresponde', 'nga', 'mga', 'personahe', 'sa', 'PNP', 'sa', 'maong', 'dapit.', 'Gitanggong', 'na', 'sab', 'siya', 'karon', 'sa', 'naasoy', 'nga', 'municipal', 'police', 'station', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 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, 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, 5, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,587 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'selebrasyon', 'sa', 'Global', 'Handwashing', 'Day', '2022', ',', 'giimbitar', 'sa', 'Metro', 'Dumaguete', 'Water', 'ang', 'mga', 'talentadong', 'dance', 'group', 'sa', 'tibuok', 'probinsya', 'sa', 'Negros', 'Oriental', 'nga', 'moapil', 'sa', 'Hugas', 'Luwas', 'Dance', 'Contest.', 'Ipahigayon', 'kini', 'sa', 'Pantawan', 'People', ''s', 'Park', 'karong', 'Oktubre', '16', ',', '2022', '(', 'Dominggo', ')', ',', 'alas-6', 'sa', 'gabie.', 'Ang', 'online', 'registration', 'ug', 'audition', 'karong', 'Setyembre', '13-26', ',', '2022.', 'Registration', 'link', ':', 'https', ':', '/', '/', 'forms.gle', '/', 'qxKCc7Yu6JSzn1YS9', 'Ang', 'maong', 'kalihukan', ',', 'gipresentar', 'sa', 'Metro', 'Dumaguete', 'Water', 'sa', 'panag-uban', 'sa', 'City', 'Government', 'sa', 'Dumaguete', 'ug', 'sa', 'City', 'Tourism', '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. | [0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 7, 8, 8, 8, 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, 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, 5, 0, 0, 5, 6, 6, 0] | cebuaner |
4,588 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JAPAN', 'GILAOMANG', 'IBALIK', 'ANG', 'VISA-FREE', 'TOURIST', 'TRAVEL', 'KARONG', 'OKTUBRE', 'Gilaoman', 'nga', 'tangtangon', 'sa', 'Japan', 'ang', 'pag-ban', 'sa', 'indibidwal', 'nga', 'tourist', 'visa', 'requirements', 'apil', 'na', 'ang', 'limitasyon', 'sa', 'adlaw-adlaw', 'nga', 'pag-abot', 'sa', 'mga', 'turista', 'sugod', 'karong', 'Oktubre.', 'Sumala', 'pa', 'sa', 'Nikkei', 'niadtong', 'Huwebes', ',', 'tumong', 'sa', 'nasud', 'nga', 'makabenepisyo', 'sa', 'pag-uswag', 'sa', 'global', 'tourism.', 'Sa', 'pagsunod', 'sa', 'maong', 'kabag-ohan', ',', 'dili', 'na', 'kinahanglanon', 'sa', 'Japan', 'ang', 'visa', 'alang', 'sa', 'short-term', 'travelers', 'gikan', 'sa', 'United', 'States', 'ug', 'pipila', 'ka', 'mga', 'nasud.', 'Matod', 'pa', 'sa', 'maong', 'media', 'outlet', ',', 'kuhaon', 'sab', 'ang', 'adlaw-adlaw', 'nga', 'entry', 'cap', 'nga', 'anaa', 'sa', '50,000', 'ka', 'mga', 'tawo.', 'Dugang', 'pa', ',', 'gilaoman', 'nga', 'ianunsyo', 'ni', 'Japanese', 'Prime', 'Minister', 'Fumio', 'Kishida', 'ang', 'mga', 'pagbag-o', 'sa', 'umalabot', 'nga', 'mga', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [5, 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, 3, 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, 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, 7, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,589 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibahaan', 'ang', 'Nangka', 'Elementary', 'School', 'sa', 'dakbayan', 'sa', 'Bayawan', 'tungod', 'sa', 'binuntagay', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'habagat', 'nga', 'gipakusog', 'sa', 'usa', 'ka', 'severe', 'Tropical', 'Storm', '“Nanmadol”', 'karong', 'adlawa', 'Setyembre', '16', ',', '2022.', 'Tungod', 'sa', 'lain', 'nga', 'panahon', 'ug', 'pagbaha', ',', 'nasuspende', 'ang', 'klase', 'sa', 'naasoy', 'nga', 'lugar.', 'Gilaoman', 'nga', 'mawala', 'ang', 'epekto', 'sa', 'severe', 'Tropical', 'Storm', 'karong', 'gabie', 'o', 'ugmang', 'adlawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 0, 0, 0, 5, 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] | cebuaner |
4,590 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'PATAY', 'HUMAN', 'MALUMOS', 'SA', 'SUBA', 'SA', 'SIBULAN', 'Usa', 'ka', 'lalaki', 'ang', 'patay', 'human', 'nalumos', 'sa', 'suba', 'sa', 'Barangay', 'Ajong', 'sa', 'lungsod', 'sa', 'Sibulan', 'niadtong', 'Huwebes', ',', 'Setyembre', '15', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'sa', 'Sibulan', 'ang', 'biktima', 'nga', 'si', 'Jomar', 'Dela', 'Cruz', 'Mengote', ',', '29-anyos', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'barangay.', 'Sumala', 'pa', 'sa', 'imbestigasyon', ',', 'nakita', 'pa', 'nga', 'buhi', 'ang', 'biktima', 'sa', 'iyang', 'igsuon', 'nga', 'si', 'Joel', 'nga', 'nagkaon', 'og', 'paniudto', 'mga', 'alas-12', 'sa', 'udto', 'sa', 'maong', 'adlaw.', 'Paghuman', 'og', 'kaon', ',', 'nidiresto', 'kini', 'og', 'adto', 'sa', 'iyang', 'naandan', 'nga', 'lugar', 'aron', 'mopahuway', 'sa', 'kilid', 'sa', 'suba.', 'Pagkagabie', ',', 'gipangita', 'na', 'ni', 'Joel', 'ang', 'iyang', 'igsuon', 'tungod', 'wala', 'kini', 'nipauli', 'para', 'sa', 'panihapon.', 'Gibulong', 'ni', 'Joel', 'ang', 'iyang', 'igsuon', 'uban', 'sa', 'iyang', 'mga', 'silingan', ',', 'apan', 'nakit-an', 'na', 'lamang', 'niya', 'kini', 'nga', 'naglutaw', 'sa', 'suba.', 'Dali', 'kini', 'niyang', 'gikuha', 'sa', 'tubig', 'ug', 'gisulay', 'pagtabang.', 'Gidala', 'sa', 'Negros', 'Oriental', 'Provincial', 'Hospital', '(', 'NOPH', ')', 'ang', 'biktima', 'apan', 'gideklarar', 'kini', 'nga', 'dead', 'on', 'arrival', 'sa', 'doktor', 'nga', 'nag-duty.', 'Dugang', 'pa', 'sa', 'ni', 'Joel', 'nga', 'aduna'y', 'sakit', 'nga', 'epilepsy', 'ang', 'iyang', 'igsuon', 'ug', 'aduna', 'na', 'sab', 'kini', 'ginamintinar', 'nga', 'tambal.', 'Ikaduha', 'na', 'sab', 'nga', 'gi-atake', 'ang', 'biktima', 'karong', 'bulana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,591 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'dako', 'punoan', 'sa', 'Acasia', 'ang', 'nibalabag', 'sa', 'dalan', 'human', 'kini', 'matumba', 'tungod', 'sa', 'kusog', 'nga', 'ulan', 'dala', 'sa', 'habagat', 'nga', 'gipakusog', 'sa', 'usa', 'ka', 'severe', 'Tropical', 'Storm', '“Nanmadol”', 'sa', 'Barangay', 'Bonawon', ',', 'Siaton', 'ganihang', 'buntag', ',', 'Setyembre', '16', ',', '2022.', 'Gisugdan', 'dayon', 'ang', 'pagkuha', 'sa', 'kahoy', 'nga', 'natumba', 'aron', 'dili', 'kini', 'makasagabal', 'sa', 'kadalanon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 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 |
4,592 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['METRO', 'DUMAGUETE', 'WATER', ',', 'NANGHATAG', 'OG', 'CELLPHONES', 'PARA', 'SA', 'PAGLUSAD', 'SA', 'BARANGAY', 'HOTLINES', 'Nanghatag', 'og', 'mga', 'cellphone', 'ang', 'Metro', 'Dumaguete', 'Water', 'ngadto', 'sa', '30', 'ka', 'barangay', 'ning', 'dakbayan', 'alang', 'sa', 'paglusad', 'sa', 'Barangay', 'hotlines', ',', 'uban', 'sa', 'pakigtambayayong', 'sa', 'ilang', 'sister', 'company', 'nga', 'PLDT-SMART', 'Communications.', 'Sumala', 'pa', 'ni', 'MDW', 'Chief', 'Operating', 'Officer', 'David', 'Berba', ',', 'tumong', 'sa', 'program', 'ang', 'pagtukod', 'og', 'sayon', 'ug', 'paspas', 'nga', 'linya', 'sa', 'komunikasyon', 'tali', 'sa', 'mga', 'barangay', 'ug', 'MDW', 'ug', 'uban', 'pang', 'ahensya', ',', 'ilabi', 'na', 'ang', 'tanang', 'sakop', 'sa', 'mga', 'barangay.', 'Gawas', 'sa', 'pagpalig-on', 'sa', 'pag-apil', 'sa', 'mga', 'barangay', 'sa', 'pag-report', 'sa', 'mga', 'pagtulo', 'sa', 'tubig', ',', 'makatabang', 'sab', 'ang', 'mga', 'telepono', 'sa', 'pag-report', 'sa', 'mga', 'kabalaka', 'bahin', 'sa', 'kaluwasan', 'ug', 'seguridad', 'ug', 'uban', 'pang', 'administratibong', 'responsibilidad', 'sa', 'mga', 'Barangay.', 'Nakadawat', 'og', 'tagsa-tagsa', 'ka', 'android', 'mobile', 'smart', 'phone', 'si', 'ABC', 'President', 'Dionie', 'Amores', 'ug', 'uban', 'pang', 'mga', 'Punong', 'Barangay', 'gikan', 'sa', 'MDW', ',', 'uban', 'ang', 'usa', 'ka', 'SIM', 'card', 'ug', 'startup', 'load', 'gikan', 'sa', 'PLDT-SMART', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 3, 4, 0, 0, 0, 3, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
4,593 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['WHO', ':', 'KATAPUSAN', 'SA', 'PANDEMYA', 'SA', 'COVID-19', ',', 'MAKITA', 'NA', 'Wala', 'pa', 'sa', 'posisyon', 'ang', 'kalibutan', 'aron', 'tapuson', 'ang', 'pandemya', 'sa', 'Covid-19', ',', 'apan', 'malaomon', 'ang', 'panglantaw', 'sa', 'pangulo', 'sa', 'World', 'Health', 'Organization', '(', 'WHO', ')', 'nga', 'nakita', 'na', 'nila', 'ang', 'katapusan', 'sa', 'pandemya.', 'Mao', 'kini', 'ang', 'iyang', 'gibutyag', 'niadtong', 'Miyerkules', ',', 'Setyembre', '14', ',', '2022.', 'Mao', 'kini', 'ang', 'pinakamadasigon', 'nga', 'assessment', 'gikan', 'sa', 'UN', 'agency', 'sukad', 'kini', 'nagdeklara', 'sa', 'usa', 'ka', 'international', 'emergency', 'niadtong', 'Enero', '2020', 'ug', 'nagsugod', 'sa', 'paghulagway', 'sa', 'Covid-19', 'isip', 'usa', 'ka', 'pandemya', 'tulo', 'ka', 'bulan', 'ang', 'milabay.', 'Nitumaw', 'ang', 'virus', 'sa', 'China', 'niadtong', 'ulahing', 'bahin', 'sa', '2019.', 'Halos', '606', 'milyon', 'ang', 'nataptan', 'niini', 'ug', 'hinungdan', 'sa', 'pagkamatay', 'sa', 'hapit', '6.5', 'milyon', 'ka', 'mga', 'tawo.', 'Nagkaguliyang', 'sab', 'ang', 'ekonomiya', 'sa', 'kalibutan', 'ug', 'hilabihan', 'nga', 'panginahanglan', 'sa', 'pag-atiman', 'sa', 'kahimsog.', 'Nakatabang', 'ang', 'paghatag', 'sa', 'mga', 'bakuna', 'ug', 'mga', 'terapiya', 'aron', 'pagpugong', 'sa', 'kamatayon', 'ug', 'pagpaospital.', 'Nitumaw', 'sab', 'ang', 'Omicron', 'variant', 'sa', 'ulahing', 'bahin', 'sa', 'niaging', 'tuig', 'nga', 'dili', 'kaayo', 'grabe', 'ang', 'sakit.', 'Gi-report', 'sab', 'sa', 'UN', 'agency', 'nga', 'ang', 'natala', 'nila', 'nga', 'mga', 'kamatayon', 'tungod', 'sa', 'Covid-19', 'sa', 'niaging', 'semana', ',', 'mao', 'ang', 'pinakamaba', 'sukad', 'pa', 'niadtong', 'Marso', '2020.', 'Bisan', 'paman', ',', 'giawhag', 'pag-usab', 'niya', 'ang', 'tanang', 'mga', 'nasud', 'nga', 'ipadayon', 'ang', 'ilang', 'mga', 'palisiya', 'ug', 'palig-onon', 'kini', 'alang', 'sa', 'Covid-19', 'ug', 'sa', 'mga', 'umalabot', 'nga', 'virus.', 'Giawhag', 'sab', 'niya', 'ang', 'tanan', 'sa', 'pagbakuna', 'og', '100', '%', 'sa', 'ilang', 'high-risk', 'groups', 'ug', 'ipadayon', 'ang', 'pag-test', 'sa', 'virus', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 7, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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] | cebuaner |
4,594 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GISUGYOT', 'NGA', 'SIM', 'REGISTRATION', ',', 'GIHISGUTAN', 'PAG-USAB', 'SA', 'SENADO', 'Gidepensahan', 'pag-usab', 'ni', 'Senador', 'Grace', 'Poe', 'ang', 'gisugyot', 'niya', 'nga', 'balaod', 'nga', 'nagtinguha', 'nga', 'himuong', 'mandatory', 'ang', 'SIM', 'registration', 'sa', 'nasud.', 'Sa', 'sesyon', 'sa', 'Senado', 'niadtong', 'Miyerkules', ',', 'giingon', 'ni', 'Senador', 'Poe', 'na', 'kon', 'ugaling', 'mahimong', 'balaod', 'ang', 'iyang', 'Senate', 'Bill', '1310', 'o', 'SIM', 'Registration', 'Act', ',', 'mas', 'masiguro', 'ang', 'seguridad', 'sa', 'mga', 'mogamit', 'niini', 'batok', 'sa', 'mga', 'scammer', 'ug', 'uban', 'pang', 'kawatan.', 'Miangkon', 'si', 'Poe', 'nga', 'dili', 'usa', 'ka', '"', 'silver', 'bullet', '"', 'ang', 'iyang', 'gisugyot', 'nga', 'balaod', 'aron', 'mapunggan', 'ang', 'krimen', 'nga', 'may', 'kalabot', 'sa', 'sayop', 'nga', 'paggamit', 'sa', 'mga', 'dili', 'rehistrado', 'nga', 'sim', ',', 'apan', 'usa', 'kini', 'ka', 'maayo', 'nga', 'pagsugod.', 'Makatabang', 'sab', 'kuno', 'ang', 'SIM', 'registration', 'sa', 'mga', 'delivery', 'riders', 'nga', 'kasagaran', 'sab', 'nga', 'mabiktima', 'sa', 'mga', 'scammers.', 'Bag-ohay', 'lamang', ',', 'gimandoan', 'sab', 'sa', 'National', 'Telecommunications', 'Commission', '(', 'NTC', ')', 'ang', 'tanang', 'tiggama', 'ug', 'tigbaligya', 'og', 'telepono', 'sa', 'pag-edukar', 'sa', 'mga', 'konsumidor', 'bahin', 'sa', '"', 'text', 'blocking', 'capabilities', '"', 'sa', 'ilang', 'napalit', 'nga', 'telepono', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 7, 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, 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, 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 |
4,595 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INFLATION', 'RATE', 'SA', 'CENTRAL', 'VISAYAS', ',', 'NISAKA', 'NGADTO', 'SA', '7.4', '%', 'Natala', 'sa', 'Central', 'Visayas', 'ang', 'pinakataas', 'nga', 'inflation', 'rate', 'niadtong', 'Agosto', ',', 'mao', 'kini', 'ang', 'gisubli', 'sa', 'Philippine', 'Statistics', 'Authority', 'sa', 'rehiyon', '(', 'PSA-7', ')', '.', 'Gipakita', 'sa', 'bag-ong', 'report', 'sa', 'PSA-7', ',', 'niabot', 'sa', '7.4', '%', 'ang', 'inflation', 'rate', 'sa', 'rehiyon', 'niadtong', 'Agosto', ',', 'mao', 'kini', 'pinakataas', 'sukad', 'pa', 'niadtong', '2019.', 'Nitaas', 'kini', 'ngadto', 'sa', '1.5', 'points', 'kon', 'itandi', 'sa', '6.9', '%', 'niadtong', 'Hulyo.', 'Gigamit', 'ang', 'inflation', 'sa', 'pagsukod', 'sa', 'kinatibuk-ang', 'pagsaka', 'sa', 'presyo', 'sa', 'mga', 'palaliton', 'ug', 'serbisyo.', 'Nagpasabot', 'sab', 'kini', 'sa', 'paghinay', 'sa', 'pagpalit.', 'Matod', 'pa', 'sa', 'PSA-7', ',', 'ang', 'probinsya', 'sa', 'Cebu', 'ang', 'aduna'y', 'pinakataas', 'nga', 'natalang', 'inflation', 'niadtong', 'Agosto', 'nga', 'anaa', 'sa', '10.7', '%', ',', 'samtang', 'nagsugod', 'sa', 'pagmaba', 'ang', 'inflation', 'rate', 'sa', 'Siquijor', 'nga', 'anaa', 'sa', '8.6', '%', '.', 'Sa', 'bahin', 'sa', 'mga', 'highly', 'urbanized', 'cities', 'sa', 'Central', 'Visayas', ',', 'ang', 'Mandaue', 'City', 'ang', 'aduna'y', 'pinakataas', 'nga', 'inflation', 'rate', 'nga', 'anaa', 'sa', '3.9', '%', 'ug', 'gisundan', 'sa', 'Cebu', 'City', 'nga', 'anaa', 'sa', '3.2', '%', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 3, 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, 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, 5, 6, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0] | cebuaner |
4,596 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SRA', 'NI-ISYU', 'OG', 'IMPORT', 'ORDER', 'SA', 'HANGTOD', '150,000', 'METRIC', 'TONS', 'SA', 'KALAMAY', 'Gitugotan', 'sa', 'Sugar', 'Board', 'sa', 'Sugar', 'Regulatory', 'Administration', 'ang', 'pag-import', 'og', 'hangtod', 'sa', '150,000', 'metric', 'tons', 'sa', 'refined', 'sugar', 'aron', 'pagdugang', 'sa', 'suplay.', 'Gibutang', 'ang', 'importation', 'plan', 'alang', 'sa', 'pag-ani', 'sa', 'tuig', '2022-2023', 'sa', 'Sugar', 'Order', 'no.', '2', 'nga', 'gipirmahan', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'Alang', 'sa', 'industrial', 'use', 'ang', 'katunga', 'sa', 'kalamay', 'nga', 'i-import', ',', 'samtang', 'ang', 'laing', 'katunga', 'niini', 'mao'y', 'alang', 'sa', 'mga', 'konsumidor.', 'Sa', 'mga', 'niaging', 'semana', ',', 'nitaas', 'ang', 'presyo', 'sa', 'kalamay', 'tungod', 'sa', 'giingong', 'kakulang', 'sa', 'suplay.', 'Sa', 'wala', 'pa', 'kini', ',', 'ni-isyu', 'na', 'sab', 'ang', 'SRA', 'board', 'og', 'Sugar', 'Order', 'no.', '1', 'kung', 'diin', 'igahin', 'ang', 'tanang', 'locally', 'produce', 'nga', 'kalamay', 'gikan', 'Setyembre', '2022', 'hangtod', 'Agosto', '2023', 'alang', 'sa', 'domestic', 'use.', 'Ang', 'maong', 'Order', ',', 'gipirmahan', 'nila', 'ni', 'Presidente', 'Marcos', 'ug', 'Undersecretary', 'Diminggo', 'Panganiban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 0, 0, 1, 2, 0] | cebuaner |
4,597 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', ',', 'MAGPAHIGAYON', 'OG', 'FAMILY', 'FUN', 'RUN', ',', 'HEALTHY', 'DANCING', 'KARONG', 'SEPT.', '25', 'NGA', 'ADUNAY', 'CASH', 'PRIZES', 'Giimbitar', 'ang', 'tanan', 'nga', 'moapil', 'sa', 'Family', 'Fun', 'Run', 'ug', 'Health', 'Dancing', 'sa', 'Pantawan', 'People', ''s', 'Park', 'Rizal', 'Boulevard', 'nga', 'magsugod', 'alas-6', 'sa', 'buntag', 'karong', 'Dominggo', ',', 'Setyembre', '25', ',', '2022.', 'Parte', 'kini', 'sa', 'opening', 'salvo', 'sa', 'Family', 'Week', 'ug', 'aduna'y', 'mga', 'papremyong', 'salapi', 'alang', 'sa', 'mga', 'partisipante', 'sa', 'maong', 'mga', 'kalingawan.', 'Ang', 'tema', 'sa', 'selebrasyon', 'sa', 'Family', 'Week', 'karong', 'tuiga', 'mao', 'ang', '"', 'Urbanisasyon', 'at', 'Pamilyang', 'Pilipino', ':', 'Magka-agapay', 'sa', 'Pagpapatibay', 'at', 'Pagpapaunlad', 'ng', 'Bansa.', '"', 'Aduna', 'kini', 'bug-os', 'nga', 'suporta', 'gikan', 'sa', 'City', 'Government', 'of', 'Dumaguete', ',', 'Mayor', 'Felipe', 'Antonio', 'B.', 'Remollo', ',', 'nagkalain-laing', 'civil', 'society', 'groups', 'ug', 'religious', 'organizations', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 7, 8, 8, 0, 7, 8, 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,598 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'CITY', 'POLICE', 'STATION', ',', 'RANK', 'NO.', '1', 'SA', 'KAMPANYA', 'BATOK', 'ILEGAL', 'NGA', 'SUGAL', 'Nakadawat', 'ang', 'Dumaguete', 'City', 'Police', 'Station', 'og', '5', 'ka', 'awards', 'ug', 'pag-ila', 'atol', 'sa', 'tradisyonal', 'nga', 'Flag', 'Raising', 'Ceremony', 'niadtong', 'Lunes', ',', 'Setyembre', '12', ',', '2022.', 'Nagpadayag', 'sa', 'ilang', 'pagpasalamat', 'ang', 'mga', 'opisyal', 'ug', 'kawani', 'sa', 'Dumaguete', 'City', 'Police', 'Station', 'sa', 'awards', 'ug', 'pag-ila', 'nga', 'ilang', 'nadawat', 'gikan', 'sa', 'Negros', 'Oriental', 'Police', 'Provincial', 'Office.', 'Nisaad', 'sab', 'sila', 'sa', 'pagbuhat', 'og', 'dugang', ',', 'pagpauswag', 'ug', 'pagbag-o', 'aron', 'sa', 'paghimo', 'sa', 'labing', 'angay', 'nga', 'mga', 'buluhaton', 'sa', 'kapulisan', 'aron', 'mapanalipdan', 'ang', 'siyudad', 'ug', 'mahimong', 'mas', 'malinawon', 'kini', 'nga', 'puy-an', ',', 'trabahoan', 'ug', 'tukuran', 'og', 'mga', 'negosyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 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, 0, 3, 4, 4, 4, 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, 0, 0, 0, 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 |
4,599 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AMERIKA', 'NANGINAHANGLAN', 'OG', 'MGA', 'NURSE', ';', 'P400K', 'ANG', 'SUWELDO', 'Giingnong', 'nagkinahanglan', 'ang', 'Amerika', 'og', 'mga', 'registered', 'nurse', 'nga', 'moabot', 'ang', 'sweldo', 'ngadto', 'sa', 'P400,000', 'ug', 'aduna'y', 'signing', 'bonus', 'nga', '$', '1,000.', 'Sumala', 'pa', 'sa', 'report', 'ni', 'Joseph', 'Morong', 'sa', 'GMA', 'News', '"', '24', 'Oras', '"', 'niadtong', 'Lunes', ',', 'giingon', 'sa', 'usa', 'ka', 'kompanya', 'sa', 'Amerika', 'nga', 'nagkinahanglan', 'sila', 'og', 'anaa', 'sa', '1,000', 'ka', 'mga', 'registered', 'nurse', 'ug', '200', 'ka', 'mga', 'medical', 'technologist.', 'Gawas', 'sa', 'sweldo', 'nga', 'moabot', 'sa', 'P400,000', ',', 'immigrant', 'visa', 'ang', 'giingon', 'ihatag', 'ug', 'ang', 'kompanya', 'na', 'ang', 'bahala', 'sa', 'tanan.', 'Matod', 'pa', 'ni', 'Janessa', 'Medina-Bacolod', 'sa', 'Ruru', 'Global', 'Recruitment', 'Services', ',', 'aduna', 'pa'y', 'signing', 'bonus', 'nga', '$', '1,000', 'ang', 'madawat.', 'Gikatahong', 'ang', 'nagkatigulang', 'nga', 'populasyon', 'sa', 'Amerika', 'ang', 'rason', 'sa', 'mga', 'amo', 'kung', 'nganong', 'nagkinahanglan', 'sila', 'og', 'daghan', 'nga', 'mga', 'nurse.', 'Gawas', 'sa', 'Amerika', ',', 'nagkinahanglan', 'sab', 'og', 'mga', 'nurse', 'sa', 'Middle', 'East', 'sama', 'sa', 'Saudi', 'Arabia.', 'Sa', 'laing', 'bahin', ',', 'dili', 'lamang', 'healthcare', 'workers', 'ang', 'gikinahanglan.', 'Aduna'y', 'mga', 'trabaho', 'sa', 'mga', 'nasud', 'sa', 'Middle', 'East', 'nga', 'gikinahanglan', 'alang', 'sa', 'tourism', 'ug', 'hotel', 'industry', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4, 4, 4, 4, 4, 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, 1, 2, 0, 3, 4, 4, 4, 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, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 6, 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] | cebuaner |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.