Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,000 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Presidente', 'Ferdinand', '"', 'Bongbong', '"', 'Marcos', 'Jr.', 'sa', 'Domingo', 'ngadto', 'sa', 'mga', 'Pilipino', 'nga', 'mahimong', '"', 'conveyors', 'of', 'truth', '"', 'ug', '"', 'better', 'agents', 'of', 'change', '"', 'pinaagi', 'sa', 'pag-ila', 'kang', 'Kristo', 'atol', 'sa', 'selebrasyon', 'sa', 'Semana', 'Santa.', 'Sa', 'iyang', 'mensahe', ',', 'gipahimug-atan', 'ni', 'Marcos', 'ang', 'kamahinungdanon', 'sa', 'pagtultol', 'sa', 'hunahuna', 'ug', 'lihok', 'sa', 'usa', 'ka', 'tawo', 'ngadto', 'sa', 'pagkabanhaw', 'sa', 'Ginoo', 'ug', 'sa', 'kalampusan', 'nga', 'gihatag', 'niini', 'hangtod', 'karon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 2, 2, 2, 2, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 7, 8, 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, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,001 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibawo', 'sa', 'Cebu', 'Cordova', 'Link', 'Expressway', 'Corp', '(', 'CCLEC', ')', 'karong', 'Biyernes', ',', 'Marso', '31', ',', 'nga', 'nahuman', 'na', 'niini', 'ang', 'P60', 'milyones', 'nga', 'on-grid', 'ug', 'hybrid', 'solar', 'farm', 'nga', 'gipaabot', 'nga', 'makamugna', 'og', 'dili', 'mominus', '50,400', 'kilowatt', 'hours', '(', 'kWh', ')', 'nga', 'kuryente', 'matag', 'buwan.', 'Ang', 'pagkompleto', 'sa', 'on-grid', 'ug', 'hybrid', 'solar', 'farm', 'gipaabot', 'nga', 'makadaginot', 'sa', 'gasto', 'sa', 'paggamit', 'sa', 'kuryente', 'sa', 'CCLEX.', 'Ang', 'solar', 'farm', 'magsuplay', 'sa', 'adlaw-adlaw', 'nga', 'panginahanglanon', 'sa', 'kuryente', 'sa', 'expressway', 'alang', 'sa', 'mga', 'suga', 'sa', 'dalan', ',', 'mga', 'lawak', 'sa', 'pagkontrolar', 'sa', 'trapiko', ',', 'mga', 'closed-circuit', 'television', 'camera', ',', 'mga', 'sistema', 'sa', 'impormasyon', 'sa', 'panahon', 'sa', 'dalan', ',', 'mga', 'karatula', 'sa', 'mensahe', 'sa', 'variable', ',', 'ug', 'mga', 'toll', 'plaza.', 'ingon', 'ni', 'Allan', 'Alfon', ',', 'CCLEC', 'president', 'og', 'general', 'manager.', 'Ang', 'solar', 'farm', 'gipaabot', 'nga', 'adunay', 'carbon', 'emission', 'savings', 'nga', '237,082', 'kilograms', 'o', 'katumbas', 'sa', '7,076', 'ka', 'punoan', 'sa', 'kahoy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 4, 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, 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, 1, 2, 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] | cebuaner |
5,002 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'pinakaulahing', 'report', 'sa', 'HIV', '/', 'Aids', 'Registry', 'of', 'the', 'Philippines', ',', 'adunay', '1,292', 'ka', 'bag-ong', 'kaso', 'sa', 'HIV', 'ang', 'gitaho', 'niadtong', 'Pebrero', '2023.', '"', 'Ang', 'kasagaran', 'nga', 'adlaw-adlaw', 'nga', 'kaso', 'sa', 'Pebrero', '2023', 'anaa', 'sa', '47', ',', '"', 'ang', 'Department', 'of', 'Health', '(', 'DOH', ')', 'miingon.', 'Ang', 'pakighilawas', 'mao', 'ang', 'kasagarang', 'hinungdan', 'sa', 'impeksyon', 'nga', 'adunay', '1,277', 'ka', 'mga', 'kaso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,003 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Misaad', 'si', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'nga', 'ang', 'iyang', 'administrasyon', 'magpadayon', 'sa', 'pagpangita', 'og', 'mga', 'solusyon', 'aron', 'mapalambo', 'pa', 'ang', 'industriya', 'sa', 'kadagatan', 'sa', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,004 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naguol', 'si', 'Sen.', 'Robinhood', 'Padilla', ',', 'chairman', 'sa', 'Senate', 'Committee', 'on', 'Constitutional', 'Amendments', 'and', 'Revision', 'of', 'Codes', ',', 'nga', 'sa', '24', 'ka', 'mga', 'senador', ',', 'upat', 'ra', 'sila', 'si', 'Senador', 'Ronald', 'Dela', 'Rosa', ',', 'Francis', 'Tolentino', ',', 'ug', 'Bong', 'Go', 'ang', 'misuporta', 'sa', 'Cha-cha.', 'Labing', 'menos', 'siyam', 'ka', 'boto', 'ang', 'gikinahanglan', 'alang', 'sa', 'sugyot', 'nga', 'mapasar', 'ang', 'plenaryo', 'alang', 'sa', 'deliberasyon', 'ug', 'debate.', 'Bisan', 'pa', 'niini', ',', 'giingong', 'nangapud-apod', 'gihapon', 'ang', 'buhatan', 'ni', 'Padilla', 'og', 'mga', 'kopya', 'sa', 'committee', 'report', 'ngadto', 'sa', 'tanang', 'mga', 'senador', 'sa', 'panghinaot', 'nga', 'ilang', 'basahon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 0, 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, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,005 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kining', 'diskwento', 'sa', 'presyo', 'sa', 'liquefied', 'petroleum', 'gas', '(', 'LPG', ')', 'katumbas', 'sa', 'P100.98', 'ngadto', 'sa', 'P101.20', 'sa', 'halin', 'sa', '11-kg.', 'tangke', 'sa', 'LPG', 'sa', 'panimalay.', 'Mius-os', 'sab', 'og', 'P5', 'ang', 'presyo', 'sa', 'AutoLPG', 'matag', 'litro', 'sa', 'Cleanfuel', ';', 'Ang', 'Petron', 'mius-os', 'og', 'P5.14', 'matag', 'litro', ';', 'samtang', 'ang', 'Phoenix', 'nagkantidad', 'og', 'P5.15', 'matag', 'litro', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0] | cebuaner |
5,006 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ako', 'diay', 'si', 'kuan', 'dili', 'ko', 'sugtan', 'mag', 'uyab', 'pero', 'paminyo'on', 'ko'g', 'americano.', '#', 'PilipinasToday', '#', 'Bench', '#', 'KathrynBernardo'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,007 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naay', 'nilabay', 'nga', 'wakwak', 'gabie', 'ba', ',', 'akong', 'gisinggitan', ''sana', 'all', 'palaagon', 'og', 'gabie'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,008 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unsa', 'ang', 'imong', 'panaad', 'matag', 'Semana', 'Santa', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
5,009 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lisod', 'man', 'gani', 'touhan', 'ang', ''on', 'the', 'way', ''', 'kana', 'pa', 'kahang', ''i', 'will', 'stay', ''', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,010 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'koy', 'labot', 'sa', 'April', 'Fools', ''', 'Day.', 'Adlaw-adlaw', 'man', 'pud', 'ko', 'giilad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 8, 0, 0, 0, 0, 0, 0] | cebuaner |
5,011 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Malipayong', 'gipaambit', 'sa', 'Fil-Am', 'Hollywood', 'actress', 'nga', 'si', 'Vanessa', 'Hudgens', 'ang', 'iyang', 'kasinatian', 'samtang', 'nagsuroysuroy', 'sa', 'Pilipinas.', 'Namatikdan', 'niya', 'nga', 'ang', 'mga', 'Pilipino', 'kanunay', 'nga', 'mahigalaon', 'ug', 'malipayon', 'nga', 'ingon', 'sa', 'wala’y', 'lain', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 7, 5, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,012 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SANA', 'ALL', '!', 'Para', 'adunay', 'igong', 'panahon', 'ang', 'mga', 'kawani', 'sa', 'gobyerno', 'sa', 'pagbiyahe', 'sa', 'lainlaing', 'rehiyon', 'sa', 'nasud', 'tungod', 'kay', 'una', 'nang', 'gideklarar', 'sa', 'gobyerno', 'nga', 'regular', 'holiday', 'ang', 'Abril', '6', 'ug', '7', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,013 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Trending', 'karon', 'sa', 'Twitter', 'ang', 'appointment', 'sa', 'Fil-Am', 'Hollywood', 'actress', 'nga', 'si', 'Vanessa', 'Hudgens', 'isip', 'Global', 'Tourism', 'Ambassador', 'of', 'the', 'Philippines', ',', 'kay', 'matud', 'pa', 'sa', 'netizens', ',', 'mas', 'deserving', 'ang', 'social', 'media', 'influencer', 'nga', 'si', 'Bretman', 'Rock', 'o', 'ang', 'Korean', 'pop', 'star', 'nga', 'kanhi', 'artista', 'dinhi.', 'Philippines', 'Sandara', 'Park', 'mao', 'kini', 'nga', 'titulo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 7, 0, 0, 0, 7, 5, 0, 0, 0, 1, 2, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 5, 1, 2, 0, 0, 0, 0, 0] | cebuaner |
5,014 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Andam', 'na', 'ang', 'Mactan-Cebu', 'International', 'Airport', 'sa', 'pagdagsa', 'sa', 'mga', 'pasahero', 'nga', 'mobiyahe', 'alang', 'sa', 'Semana', 'Santa', ',', 'nga', 'magsugod', 'ugma', ',', 'Dominggo', ',', 'Abril', '2', ',', 'matod', 'ni', 'Mactan-Cebu', 'International', 'Airport', 'Authority', 'General', 'Manager', 'ug', 'CEO', 'Julius', 'Neri', 'Jr.', 'karong', 'Sabado', ',', 'Abril', '1', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0] | cebuaner |
5,015 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'tigpamaba', 'sa', 'Partido', 'ng', 'Manggagawa', '(', 'PM', ')', '-Cebu', 'nga', 'si', 'Dennis', 'Derige', 'niingon', 'sa', 'SunStar', 'Cebu', 'karong', 'Biyernes', ',', 'Marso', '31', ',', 'nga', 'human', 'sa', 'Semana', 'Santa', 'moduso', 'sila', 'og', 'motion', 'for', 'reconsideration', 'nunot', 'sa', 'pagsalikway', 'sa', 'Regional', 'Tripartite', 'Wages', 'and', 'Productivity', 'Board', '(', 'RTWPB', '7', ')', 'sa', 'hangyo', 'sa', 'ilang', 'P100', 'across-the-board', 'nga', 'usbaw', 'sa', 'suholan', 'tungod', 'sa', 'teknikalidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,016 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Layo', 'sa', 'naandan', 'nga', 'hitsura', 'sa', 'batang', 'lalaki', 'ug', 'babaye', 'sa', 'eskuylahan', 'sa', 'iyang', 'mga', 'POV', 'vids', 'sa', 'TikTok', ',', 'ang', 'content', 'creator', 'nga', 'si', 'Esnyr', 'Ranollo', 'miabli', 'ug', 'nagpakita', 'sa', 'glamorous', 'nga', 'bahin', 'sa', 'bag-o', 'lang', 'natapos', 'nga', 'Star', 'Magical', 'Prom', '2023', ',', 'nagsul-ob', 'sa', 'iyang', 'suit', 'ug', 'kurbata', 'nga', 'adunay', 'twist', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 7, 0, 0, 0, 0, 0, 0, 1, 2, 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] | cebuaner |
5,017 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Murag', 'na', 'april', 'fools', 'man', 'ko', 'ani', 'ba', ',', 'hali', 'man', 'ning', 'John', 'Wick', 'akong', 'nakitan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 2, 0, 0, 0] | cebuaner |
5,018 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unsay', 'prank', 'na', 'gihimo', 'sa', 'imo', 'nga', 'dili', 'nimo', 'malimtan', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,019 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unsa', 'ang', 'imong', 'panaad', 'sa', ''Semana', 'Santa', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
5,020 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ingna', 'imong', 'friend', 'nga', 'kusog', 'sa', 'unli', 'rice', ':', ''Testingan', 'nato', 'bi', 'kung', 'gasipa', 'na', 'ning', 'bata', '.', '''] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,021 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mahilom', 'ug', 'yano', 'ang', 'paghandum', 'ni', 'Presidente', 'Bongbong', 'Marcos', 'sa', 'Semana', 'Santa', ',', 'matod', 'niya', 'sa', 'dihang', 'nahinabi', 'sa', 'pagbukas', 'sa', 'tindahan', 'sa', 'Kadiwa', 'sa', 'Presidente', 'sa', 'Limay', ',', 'Bataan', 'karong', 'Biyernes', ',', 'Marso', '31.', 'Matod', 'ni', 'Marcos', ',', 'nangandam', 'na', 'ang', 'gobyerno', 'alang', 'sa', 'umaabot', 'nga', 'Semana', 'Santa', 'nga', 'matud', 'pa', 'gamay', 'ra', 'ang', 'kausaban', 'tungod', 'sa', 'padayong', 'pag-init', 'sa', 'panahon.', 'ingon', 'ni', 'Marcos', ',', 'gisuguro', 'niya', 'nga', 'andam', 'ang', 'iyang', 'Gabinete', 'sa', 'selebral', 'sa', 'nasud', 'karong', 'Kuwaresma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
5,022 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pinakaulahing', 'Facebook', 'post', 'ni', 'Senador', 'Sonny', 'Angara', 'karong', 'Biyernes', ',', 'Marso', '31', ',', 'iyang', 'giingon', 'nga', 'ang', 'mga', 'Pilipino', 'kinahanglang', 'magplano', 'sa', 'ilang', 'long', 'weekend', 'agig', 'paghandum', 'sa', 'Semana', 'Santa', 'ug', 'Piyesta', 'Opisyal', ',', 'lakip', 'ang', 'hashtags', 'nga', '#', 'VisitAurora', 'ug', '#', 'Baler', 'to', 'gi-endorso', 'ang', 'probinsya', 'sa', 'Aurora', 'alang', 'sa', 'matahum', 'nga', 'mga', 'baybayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 0, 0, 0, 0, 0, 0, 7, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,023 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PALIHOG', 'BAYAD', 'NA', ''', 'LOOK', ':', 'Listahan', 'sang', 'utang', 'nga', 'naga', 'lab-ot', 'linibo', 'sa', 'isa', 'ka', 'tyangge', 'nga', 'nahamtang', 'sa', 'Talisay', ',', 'Negros', 'Occidental.', '"', 'Palihog', 'bayad', 'na', ',', 'indi', 'na', 'maghulat', 'nga', 'mag', 'action', 'ang', 'association', ',', '"', 'pahayag', 'sang', 'tag-iya', 'sang', 'tyangge', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,024 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'interbyu', 'sa', 'TV', 'host', 'nga', 'si', 'Kim', 'Atienza', 'sa', ''Fast', 'Talk', 'with', 'Boy', 'Abunda', ',', ''', 'masaligon', 'nga', 'niingon', 'si', 'Kim', 'nga', 'segurado', 'siyang', 'moadto', 'sa', 'langit', 'human', 'siya', 'mamatay.', 'Sa', 'dihang', 'gipangutana', 'ni', 'Boy', 'kon', 'sa', 'unsang', 'paagi', 'siya', 'nakaseguro', ',', 'si', 'Kim', 'mitubag', ':', '"', 'Because', 'I', ''ve', 'accepted', 'the', 'Lord', 'Jesus', 'Christ.', '"', 'Matud', 'pa', 'ni', 'Kim', 'nga', 'moadto', 'siya', 'sa', 'langit', 'dili', 'tungod', 'sa', 'iyang', 'maayong', 'binuhatan', 'kondili', 'tungod', 'sa', 'iyang', 'pagsalig', 'sa', 'Ginoo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,025 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'pampasaherong', 'bus', 'sa', 'Rural', 'Transit', 'Mindanao', 'ang', 'nabangga', 'sa', 'usa', 'ka', 'cargo', 'truck', 'nga', 'gikargahan', 'og', 'isda', 'mga', 'alas', '3:00', 'sa', 'kaadlawon', 'niadtong', 'Huwebes', ',', 'Marso', '30', ',', 'sa', 'lungsod', 'sa', 'Gitagum', ',', 'Misamis', 'Oriental.', 'Lima', 'ang', 'patay', ',', 'samtang', '13', 'ang', 'naangol', ',', 'matod', 'ni', 'P', '/', 'Major', 'Dennis', 'Cerrilla', ',', 'hepe', 'sa', 'Gitagum', 'Police', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 0] | cebuaner |
5,026 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Donald', 'Trump', 'ang', 'unang', 'kanhi', 'presidente', 'sa', 'Estados', 'Unidos', 'nga', 'gipasakaan', 'og', 'kasong', 'kriminal', 'human', 'ang', 'grand', 'jury', 'sa', 'New', 'York', 'mimando', 'karong', 'Huwebes', ',', 'Marso', '30', ',', 'nga', 'pasakaan', 'og', 'kaso', 'ang', '76-anyos', 'nga', 'Republikano', 'tungod', 'sa', 'giingong', 'pagbayad', 'og', '$', '130,000', '(', 'P7', 'milyon', ')', 'sa', 'usa', 'ka', 'hamtong', 'nga', 'artista', 'sa', 'pelikula', 'nga', 'si', 'Stormy', 'Daniels', 'pipila', 'ka', 'semana', 'sa', 'wala', 'pa', 'ang', 'eleksyon', 'aron', 'dili', 'kini', 'mogawas', 'aron', 'ibutyag', 'ang', 'giingong', 'relasyon', 'nila', 'usa', 'ka', 'dekada', 'na', 'ang', 'milabay.', 'Gitawag', 'ni', 'Trump', 'nga', 'ang', 'pakaso', 'sa', 'iayahon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
5,027 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Lt.', 'Col.', 'Ardioleto', 'Cabagnot', ',', 'hepe', 'sa', 'Carcar', 'City', 'Police', ',', 'sa', 'pakighinabi', 'sa', 'SunStar', 'Cebu', ',', 'nagkanayon', 'nga', 'unom', 'ka', 'buok', 'chewing', 'gum', 'nga', 'mobalor', 'og', 'P84', 'ang', 'giingong', 'gikaon', 'sa', 'teenager', 'utility', 'worker', 'nga', 'taga', 'Barangay', 'Valencia.', 'Matod', 'ni', 'Cabagnot', 'nga', 'ang', 'suspek', 'gibuhian', 'niadtong', 'Marso', '27', 'human', 'nakighinabi', 'sa', 'grocery', '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, 1, 2, 0, 0, 0, 3, 4, 4, 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, 5, 6, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,028 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Miabot', 'na', 'sa', 'P1,100,000', 'ang', 'reward', 'sa', 'bisan', 'kinsa', 'nga', 'makatabang', 'pagsulbad', 'sa', 'pagpatay', 'kang', 'Reyna', 'Leanne', 'Daguinsin', ',', 'ang', '22-anyos', 'nga', 'estudyante', 'sa', 'De', 'La', 'Salle', 'University', '(', 'DLSU', ')', '-Dasmariñas', ',', 'kinsa', 'gipugos', 'sa', 'pagsulod', 'sa', 'iyang', 'dorm', ',', 'gitulis', ',', 'ug', 'gidunggab', 'ang', '14.', 'mga', 'higayon', 'sa', 'lalaki', 'nga', 'suspek', 'kaniadtong', 'Marso', '28', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,029 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'singer-songwriter', 'nga', 'si', 'Moira', 'Dela', 'Torre', 'nag-tweet', 'sa', 'iyang', 'Twitter', 'niadtong', 'Marso', '29.', 'Gikatakdang', 'ipagawas', 'ni', 'Moira', 'ang', 'iyang', 'bag-ong', 'album', 'nga', ''EME', ''', 'karong', 'Biyernes', ',', 'Marso', '29', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,030 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'isog', 'nga', 'pamahayag', 'ni', 'Sen.', 'Robinhood', 'Padilla', 'kon', 'dakpon', 'sa', 'International', 'Criminal', 'Court', '(', 'ICC', ')', 'ang', 'iyang', 'kaubang', 'Senador', 'Ronald', 'Bato', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
5,031 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makita', 'sa', 'CCTV', 'footage', 'ang', 'lalaki', 'nga', 'suspek', 'nga', 'misaka', 'sa', 'rooftop', 'sa', 'usa', 'ka', 'establisemento', 'aron', 'makaabot', 'sa', 'dormitoryo', 'sa', 'biktima', 'nga', 'si', 'Queen', 'Leanne', 'Daguinsin', 'mga', 'ala', '1:00', 'sa', 'kaadlawon', 'niadtong', 'Marso', '28.', 'Sa', 'laing', 'footage', ',', 'nigawas', 'sa', 'dormitoryo', 'ang', 'suspek', 'nga', 'nagbitbit', 'og', 'pipila', 'ka', 'mga', 'butang.', 'Ang', 'suspek', 'nagsul-ob', 'og', 'blue', 'nga', 'T-shirt', ',', 'itom', 'nga', 'short', ',', 'itom', 'nga', 'kalo', ',', 'ug', 'puti', 'nga', 'tsinelas', 'Sa', 'Facebook', 'post', ',', 'ang', 'Cavite', 'Police', 'Provincial', 'Office', 'nanawagan', 'sa', 'publiko', 'nga', 'mokontak', 'sa', 'pinakaduol', 'nga', 'police', 'station', 'kung', 'aduna', 'silay', 'impormasyon', 'bahin', 'sa', 'kaso.', 'Sa', 'samang', 'higayon', ',', 'ang', 'kagamhanang', 'probinsiyal', 'ni', 'Cavite', 'Gov.', 'nitanyag', 'og', 'tag', 'P300,000', 'si', 'Jonvic', 'Remulla', 'ug', 'P300,000', 'sa', 'buhatan', 'ni', 'Sen.', 'Bong', 'Revilla', 'alang', 'sa', 'dinaliang', 'pagkasikop', 'sa', 'suspek.', 'Si', 'Daguinsin', ',', '24', ',', 'graduating', 'computer', 'science', 'student', 'sa', 'De', 'La', 'Salle', 'University', '(', 'DLSU', ')', '–Dasmariñas', ',', 'napalgang', 'patay', 'niadtong', 'Marso', '28', 'sulod', 'sa', 'iyang', 'dormitory', 'room', 'nga', 'dunay', '14', 'ka', 'samaD', 'dinunggaban', 'sa', 'nagkadaiyang', 'parte', 'sa', 'lawas', 'sadirang', 'Marso', '28', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 7, 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, 5, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,032 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Sen.', 'Ronald', ''Bato', ''', 'Dela', 'Rosa', 'niingon', 'nga', 'si', 'Presidente', 'Bongbong', 'Marcos', 'misaad', 'kaniya', 'nga', 'magpabilin', 'siyang', 'luwas', 'batok', 'sa', 'International', 'Criminal', 'Court', '(', 'ICC', ')', ',', 'nga', 'karon', 'nag-imbestigar', 'sa', 'drug', 'war.', 'Gihangyo', 'usab', 'ni', 'Dela', 'Rosa', 'ang', 'iyang', 'kauban', 'nga', 'si', 'Sen.', 'Francis', 'Tolentino', 'nga', 'mahimong', 'iyang', 'abogado', 'kalabot', 'sa', 'drug', 'war', 'nga', 'gisusi', 'na', 'sa', 'ICC.', 'matod', 'ni', 'Dela', 'Rosa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 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, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0] | cebuaner |
5,033 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sorry', 'mao', 'ra', 'ni', 'amoang', 'kasilyas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,034 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-flexible', 'sa', 'aktres', 'nga', 'si', 'AJ', 'Raval', 'ang', 'iyang', 'hulagway', 'samtang', 'gipakita', 'nga', 'natangtang', 'na', 'ang', 'iyang', 'breast', 'implants', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,035 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'House', 'Speaker', 'Martin', 'Romuladez', 'maoy', 'temporaryong', 'mohulip', 'kang', 'Negros', 'Oriental', '3rd', 'District', 'Rep.', 'Arnolfo', 'Teves', 'Jr.', ',', 'kinsa', 'gihatagan', 'og', '60', 'ka', 'adlaw', 'nga', 'suspensiyon', 'kalabot', 'sa', 'alegasyon', 'nga', 'nalambigit', 'siya', 'sa', 'pagpatay', 'kang', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'karong', 'Marso', '4', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0] | cebuaner |
5,036 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghan', 'sa', 'mga', 'naluwas', 'ug', 'naluwas', 'ang', 'niambak', 'sa', 'dagat', 'human', 'nataranta', 'samtang', 'nagdilaab', 'ang', 'barko', 'ug', 'sa', 'dagat', 'naluwas', 'sila', 'sa', 'mga', 'personahe', 'sa', 'Philippine', 'Coast', 'Guard', '(', 'PCG', ')', ',', 'Philippine', 'Navy', ',', 'laing', 'lantsa', ',', 'ug', 'mga', 'mananagat', ',', 'sumala', 'ni', 'Basilan', 'Si', 'Gov.', 'Jim', 'Hataman-Salliman', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 4, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0] | cebuaner |
5,037 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Pope', 'Francis', 'gidala', 'sa', 'Gemelli', 'hospital', 'sa', 'Roma', 'human', 'nagreklamo', 'sa', 'kalisud', 'sa', 'pagginhawa', 'sa', 'miaging', 'mga', 'adlaw.', 'Base', 'sa', 'test', ',', 'kompirmado', 'nga', 'adunay', 'respiratory', 'infection', 'ang', '86-anyos', 'nga', 'Santo', 'Papa', ',', 'apan', 'negatibo', 'kini', 'sa', 'COVID-19.', 'saad', 'sa', 'official', 'statement', 'sa', 'Vatican', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 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, 0, 7, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,038 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ALAGANG', 'ANGARA', 'Nag-tweet', 'nitong', 'Miyerkules', ',', 'Marso', '29', ',', 'si', 'Pamilya', ',', 'Pasyente', 'at', 'Persons', 'with', 'Disabilities', '(', 'P3WD', ')', 'Party-list', 'Rep.', 'Rowena', 'Guanzon', 'para', 'magpaabot', 'ng', 'pasasalamat', 'kay', 'Senator', 'Sonny', 'Angara', 'na', 'tumulong', 'daw', 'sa', 'mga', 'gastusin', 'sa', 'ospital', 'ng', 'isang', 'batang', 'taga-Negros', 'Occidental', 'na', 'may', 'Down', 'Syndrome', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 7, 8, 0] | cebuaner |
5,039 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Reyna', 'Leanne', 'Daguinsin', ',', '24', ',', 'lumad', 'nga', 'taga', 'Pila', ',', 'Laguna', ',', 'graduating', 'computer', 'science', 'student', 'sa', 'De', 'La', 'Salle', 'University', '(', 'DLSU', ')', '-Dasmariñas', 'sa', 'Cavite', ',', 'patay', 'na', 'dihang', 'nadiskubrehan', 'sa', 'caretaker', 'sa', 'dormitoryo', 'alas', '4:40', 'sa', 'hapon', 'niadtong', 'Martes', ',', 'Marso', '28.', 'Base', 'sa', 'imbestigasyon', ',', 'misulod', 'sa', 'kwarto', 'sa', 'biktima', 'ang', 'lalaki', 'nga', 'suspek', 'dihang', 'niagi', 'kini', 'sa', 'bentana', ',', 'ug', 'nadiskubrehan', 'nga', 'nawala', 'ang', 'pipila', 'ka', 'gamit', 'sa', 'estudyante', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 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, 0, 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 |
5,040 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaambit', 'sa', 'Filipina-American', 'actress', 'nga', 'si', 'Vanessa', 'Hudgens', 'ang', 'pipila', 'ka', 'mga', 'hulagway', 'sa', 'iyang', 'biyahe', 'sa', 'El', 'Nido', ',', 'Palawan.', 'Ang', 'Hollywood', 'Superstar', 'naa', 'sa', 'nasud', 'alang', 'sa', 'usa', 'ka', 'dokumentaryo', 'sa', 'pagbiyahe', 'nga', 'nagsusi', 'sa', 'iyang', 'gigikanan', 'nga', 'Filipino', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
5,041 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibasol', 'ni', 'Senador', 'Ramon', ''Bong', ''', 'Revilla', 'Jr', 'Siya', 'adunay', 'mga', 'bato', 'sa', 'iyang', 'apdo', 'tungod', 'sa', 'pagkaon', 'sa', 'mga', 'chicharong', 'bulaklak', ',', 'nga', 'gitangtang', 'sa', 'mga', 'doktor', 'kaniadtong', 'Marso', '28', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 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] | cebuaner |
5,042 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'dili', 'pa', 'modagsa', 'ang', 'mga', 'magpapanaw', 'atol', 'sa', 'Semana', 'Santa', ',', 'ang', 'Police', 'Regional', 'Office', '(', 'PRO', ')', '-Central', 'Visayas', 'mopakatap', 'og', 'mga', 'pulis', 'aron', 'masiguro', 'ang', 'kaluwasan', 'sa', 'publiko', 'sa', 'mga', 'lugar', 'nga', 'gipaabot', 'nga', 'ilang', 'bisitahan', ',', 'sama', 'sa', 'mga', 'simbahan', ',', 'shopping', 'mall', ',', 'resort', ',', 'restawran', ',', 'ug', 'mga', 'terminal', 'sa', 'transportasyon', 'sa', 'Abril', '2-8.', 'matod', 'ni', 'Police', 'Lt.', 'Col.', 'Carlos', 'Lacuesta', 'Jr.', 'ng', 'Regional', 'Community', 'Affairs', 'and', 'Development', 'Division', 'of', 'PRO-7'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 3, 4, 4, 4, 4, 4, 4, 4] | cebuaner |
5,043 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pamilya', 'ni', 'Darlene', 'Uy', 'nitanyag', 'og', 'P50,000.00', 'nga', 'reward', 'money', 'kang', 'bisan', 'kinsa', 'nga', 'makatudlo', 'sa', 'iyang', 'nahimutangan', ',', 'ug', 'gipasaka', 'kini', 'ngadto', 'sa', 'P250,000.00', 'dihang', 'wala', 'pay', 'balita', 'ang', 'kabanay', 'sa', 'dalaga.', 'Kaniadtong', 'Martes', ',', 'Marso', '28', ',', 'napalgan', 'si', 'Uy', 'daplin', 'sa', 'sapa', ',', 'luyo', 'sa', 'ilang', 'balay', ',', 'ug', 'gidala', 'dayon', 'sa', 'Catbalogan', 'Doctors', 'Hospital', 'alang', 'sa', 'gikinahanglang', 'eksaminasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
5,044 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Taas', 'kuno'g', 'standards', ',', 'pero', 'nihilak', 'sa', 'ka-talking', 'stage', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,045 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Morag', 'curious', 'sila', 'si', 'Miss', 'Globe', '2003', 'ug', 'Miss', 'Earth', '2015', 'Priscilla', 'Meirelles', 'ug', 'bana', 'sa', 'aktor', 'nga', 'si', 'John', 'Estrada', 'tungod', 'kay', 'lakip', 'sila', 'sa', 'mga', 'curious', 'ug', 'curious', 'nga', 'followers', 'ni', 'Prescilla', 'sa', 'iyang', 'Instagram.', 'Nagpasalamat', 'ang', 'beauty', 'queen', 'sa', 'mga', 'tubag', 'nga', 'iyang', 'nakuha', 'ug', 'pipila', 'sa', 'mga', 'tubag', 'nga', 'iyang', 'nakuha', 'mao', 'ang', ':', 'hitad', ',', 'anaconda', ',', 'linta', ',', 'haliparot', 'ug', 'Makati', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,046 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahalipayan', 'ni', 'Presidente', 'Ferdinand', 'Marcos', ',', 'Jr', 'karong', 'Martes', 'ang', 'iyang', 'gisundan', ',', 'si', 'kanhi', 'Presidente', 'Rodrigo', 'Duterte', ',', 'sa', 'iyang', 'ika-78', 'nga', 'adlawng', 'natawhan.', 'Sa', 'usa', 'ka', 'video', 'message', ',', 'si', 'Marcos', 'niingon', 'nga', 'nakasabot', 'na', 'siya', 'sa', 'kalisod', 'sa', 'pagka', 'chief', 'executive', 'sa', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,047 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Alden', 'Richards', 'nga', 'dili', 'kini', 'ang', 'una', 'niyang', 'kasinatian', 'sa', 'scuba', 'diving', 'apan', 'kini', 'ang', 'unang', 'higayon', 'nga', 'nanguha', 'siya', 'og', 'basura', 'ilawom', 'sa', 'dagat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 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] | cebuaner |
5,048 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Capt.', 'Ar-jay', 'Dangarang', ',', 'hepe', 'sa', 'Isabela', 'Municipal', 'Police', ',', 'nga', 'wala', 'pa', 'nila', 'matino', 'ang', 'pagkatawo', 'sa', 'mga', 'biktima', ',', 'kinsa', 'gipusil', 'sa', 'Sitio', 'Galo', 'sa', 'Barangay', '5', ',', 'mga', 'alas', '11:00', 'sa', 'buntag', 'niadtong', 'Martes', ',', 'Marso', '28.', 'Layo', 'sa', 'silingang', 'mga', 'balay', 'ang', 'crime', 'scene', ',', 'busa', 'limitado', 'ang', 'impormasyon', 'sa', 'kapolisan', 'bahin', '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, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,049 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['matod', 'ni', 'DepEd', 'spokesperson', 'Michael', 'Poa.', 'dugang', 'pa', 'nya.', 'Karong', 'Martes', ',', 'Marso', '28', ',', 'si', 'Senador', 'Sherwin', 'Gatchalian', ',', 'chairman', 'sa', 'Senate', 'Committee', 'on', 'Basic', 'Education', ',', 'niingon', 'nga', 'panahon', 'na', 'nga', 'mobalik', 'sa', 'nasud', 'ang', 'summer', 'vacation', 'sa', 'mga', 'tinun-an', 'sa', 'Abril-Mayo', 'inay', 'sa', 'Hulyo-Agosto', 'tungod', 'sa', 'insidente', 'sa', 'Cabuyao', 'City.', ',', 'Laguna', 'diin', 'kapin', 'sa', 'usa', 'ka', 'gatos', 'ka', 'mga', 'tinun-an', 'ang', 'nalandig', 'sa', 'tambalanan', 'human', 'nangasakit', 'atol', 'sa', 'fire', 'drill', 'sa', 'tunghaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 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 |
5,050 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'uga', 'ug', 'init', 'nga', 'panahon', 'mahimong', 'hinungdan', 'sa', 'pagkunhod', 'sa', 'suplay', 'sa', 'tubig', 'sa', 'Cebu', ',', 'matod', 'sa', 'meteorologist', 'sa', 'estado', 'nga', 'nitagna', 'usab', 'nga', 'dako', 'ang', 'posibilidad', 'sa', 'El', 'Niño', 'phenomenon.', 'matod', 'ni', 'ngr.', 'Alfredo', 'F.', 'Quiblat', 'Jr.', ',', 'chief', 'of', 'the', 'Philippine', 'Atmospheric', 'Geophysical', 'and', 'Astronomical', 'Services', 'Administration', '(', 'Pagasa', ')', 'Visayas', 'Regional', 'Services', 'Division.', '"', 'Daan', 'na', 'ra', 'ba', 'ta', 'nagkulang', 'sa', 'tubig', '(', 'Nagkulang', 'na', 'tayo', 'sa', 'tubig', ')', '"', 'dugang', 'pa', 'niya.', 'Gitambagan', 'ni', 'Quiblat', 'ang', 'publiko', 'sa', 'Openline', 'Forum', 'kaniadtong', 'Martes', ',', 'Marso', '28', ',', '2023', ',', 'nga', 'gamiton', 'ang', 'tubig', 'sa', 'labing', 'epektibo', 'nga', 'mahimo', 'alang', 'sa', 'personal', 'ug', 'panimalay', 'nga', 'konsumo', ',', 'lakip', 'ang', 'pag-inom', ',', 'pag-andam', 'sa', 'pagkaon', ',', 'pagkaligo', ',', 'paglaba', 'sa', 'mga', 'sinina', 'ug', 'pinggan', ',', 'pag-flush', 'sa', 'mga', 'toilet', 'bowl', ',', 'pagbisbis', 'sa', 'mga', 'tanum', 'ug', 'pagmintinar', 'sa', 'mga', 'pool.', 'Ang', 'pinakataas', 'nga', 'heat', 'index', ',', 'sa', 'pagkakaron', ',', 'natala', 'sa', '39°C', 'niadtong', 'Sabado', ',', 'Marso', '25', ',', 'sumala', 'sa', 'state', 'weather', 'bureau', 'sa', 'Mactan', ',', '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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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] | cebuaner |
5,051 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'grabeng', 'hulga', 'sa', 'iyang', 'kinabuhi', ',', 'ingon', 'man', 'sa', 'iyang', 'pamilya', ',', 'kini', 'gikompirmar', 'karong', 'Martes', ',', 'Marso', '28', ',', 'ni', 'Atty.', 'Ferdinand', 'Topacio', 'nga', 'wala', 'gihapon', 'makabalik', 'sa', 'nasud', 'si', 'Negros', 'Oriental', '3rd', 'District', 'Rep.', 'Arnolfo', 'Teves', 'nga', 'atubangon', 'ang', 'pasangil', 'batok', 'kaniya', 'kalabot', 'sa', 'pagpatay', 'kang', 'Negros', 'Oriental', 'Governor', 'Roel', 'Degamo.', 'Si', 'Teves', 'maoy', 'usa', 'sa', 'gidudahang', 'utok', 'sa', 'pagpatay', 'kang', 'Degamo', 'karong', 'Marso', ',', 'diin', 'walo', 'pa', 'ang', 'napatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 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, 5, 6, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,052 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'ka', 'video', 'sa', 'aktuwal', 'nga', 'pagluwas', 'sa', 'siyam', 'ka', 'Congolese', 'nga', 'mga', 'minero', 'nga', 'natanggong', 'gikan', 'sa', 'usa', 'ka', 'nahugno', 'nga', 'minahan', 'sa', 'bulawan', 'nahimong', 'viral.', 'Makita', 'sila', 'nga', 'tagsa-tagsa', 'nga', 'migawas', 'gikan', 'sa', 'titip', 'nga', 'pag-abli', 'sa', 'minahan', ',', 'samtang', 'ang', 'yuta', 'nga', 'nagtabon', 'sa', 'tunnel', 'nga', 'ilang', 'gigawasan', 'nagpadayon', 'sa', 'pagkahugno.', 'Samtang', ',', 'ang', 'mga', 'nagtan-aw', 'sa', 'rescue', 'operation', 'mosinggit', 'sa', 'kalipay', 'matag', 'higayon', 'nga', 'adunay', 'maluwas', 'nga', 'minero', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,053 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaambit', 'sa', 'aktres', 'nga', 'si', 'Vanessa', 'Hudgens', 'ang', 'mga', 'eksena', 'gikan', 'sa', 'iyang', 'paglakaw', 'sa', 'kaadlawon', 'sa', 'Palawan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,054 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'David', 'LIcauco', 'hapit', 'na', 'moundang', 'sa', 'showbiz', 'aron', 'motutok', 'sa', 'iyang', 'mga', 'negosyo', 'sa', 'wala', 'pa', 'siya', 'gianggaan', 'og', 'Sa', 'vlog', 'sa', 'beteranong', 'showbiz', 'insider', 'nga', 'si', 'Ogie', 'Diaz', ',', 'gibutyag', 'ni', 'David', ':', 'Nganong', 'gidawat', 'man', 'niya', 'ang', 'papel', 'ni', 'Fidel', '?', 'pag-angkon', 'ni', 'David', ',', 'ug', 'midugang', 'nga', 'gibubo', 'niya', 'ang', 'iyang', 'tinigom', 'sa', '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. | [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, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,055 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'bay', ''Gibiyaan', ''', 'dira', 'para', 'maka-relate', 'ra', 'pud', 'ko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,056 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PARI', 'PAJUD', ',', 'MOHIMO', 'OG', 'INAGANI', '(', 'sad', ')', 'Subay', 'sa', 'warrant', 'of', 'arrest', 'nga', 'giluwatan', 'ni', 'Sagay', 'Regional', 'Trial', 'Court', 'Judge', 'Reginald', 'Fuentebella', ',', 'usa', 'ka', '62', 'anyos', 'nga', 'paring', 'Katoliko', ',', 'lumad', 'nga', 'taga', 'Barangay', 'Balatocan', 'sa', 'Looc', ',', 'Romblon', ',', 'nasikop', 'sa', 'dakbayan', 'sa', 'Bacolod', 'tungod', 'sa', 'kasong', 'pagpanglugos.', 'Ang', 'suspek', 'gitanggong', 'na', 'sa', 'Sagay', 'City', 'Police', 'Station', 'ug', 'walay', 'girekomendar', 'nga', 'piyansa', 'alang', 'kaniya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 5, 6, 0, 5, 6, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,057 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'nay', 'sunod', 'nga', 'himuon', 'ang', 'gobyerno', 'human', 'gibasura', 'sa', 'International', 'Criminal', 'Court', '(', 'ICC', ')', 'ang', 'hangyo', 'niini', 'nga', 'suspensuhon', 'ang', 'imbestigasyon', 'sa', 'ICC', 'sa', 'drug', 'war', 'sa', 'nasod', ',', 'matod', 'ni', 'Presidente', 'Bongbong', 'Marcos.', 'ingon', 'ni', 'Marcos.', 'Gisubli', 'ni', 'Marcos', 'nga', 'ang', 'gobyerno', 'sa', 'Pilipinas', 'dili', 'mokooperar', 'sa', 'ICC', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0] | cebuaner |
5,058 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'heat', 'index', 'sa', 'Manila', 'anaa', 'sa', '33', 'degrees', 'Celsius', 'sumala', 'sa', 'website', 'sa', 'Weather', 'Atlas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
5,059 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'grabeng', 'hulga', 'sa', 'iyang', 'kinabuhi', ',', 'ingon', 'man', 'sa', 'iyang', 'pamilya', ',', 'kini', 'gikompirmar', 'karong', 'Martes', ',', 'Marso', '28', ',', 'ni', 'Atty.', 'Ferdinand', 'Topacio', 'nga', 'wala', 'gihapon', 'makabalik', 'sa', 'nasud', 'si', 'Negros', 'Oriental', '3rd', 'District', 'Rep.', 'Arnolfo', 'Teves', 'nga', 'atubangon', 'ang', 'pasangil', 'batok', 'kaniya', 'kalabot', 'sa', 'pagpatay', 'kang', 'Negros', 'Oriental', 'Governor', 'Roel', 'Degamo.', 'Si', 'Teves', 'maoy', 'usa', 'sa', 'gidudahang', 'utok', 'sa', 'pagpatay', 'kang', 'Degamo', 'karong', 'Marso', ',', 'diin', 'walo', 'ang', 'napatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 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, 5, 6, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,060 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MULI', ',', 'HAPPY', 'BIRTHDAY', 'TATAY', 'DIGONG', '!', ''', 'Gitimbaya', 'ni', 'Sen.', 'Si', 'Pang', 'si', 'Bong', 'Go', 'kaniadto.', 'Rodrigo', 'Duterte', 'sa', 'iyang', 'ika-78', 'nga', 'adlawng', 'natawhan', 'karong', 'adlawa', ',', 'Marso', '28', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,061 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaambit', 'ni', 'Issa', 'Pressman', 'ang', 'nindot', 'nga', 'mga', 'litrato', 'niya', 'kauban', 'sa', 'rumored', 'boyfriend', 'nga', 'si', 'James', 'Reid', 'sa', 'Instagram.', 'caption', 'ni', 'Issa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 7, 0, 0, 1, 0] | cebuaner |
5,062 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AMPING', 'PIRMI', ''', 'Sa', 'tweet', 'karong', 'Martes', ',', 'Marso', '28', ',', 'si', 'Senador', 'Sonny', 'Angara', 'mipaambit', 'og', 'mga', 'tips', 'gikan', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'kon', 'unsay', 'angayang', 'buhaton', 'kung', 'adunay', 'posibleng', 'heat', 'stroke', 'o', 'pagkalipong', 'niining', 'hilabihan', 'ka', 'init', 'nga', 'panahon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,063 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisulayan', 'sa', 'Offshore', 'Combat', 'Force', 'sa', 'Philippine', 'Fleet', 'ang', 'ilang', 'bag-ong', 'nakuha', 'nga', 'Bullfighter', 'Chaff', 'Decoy', 'sakay', 'sa', 'BRP', 'Jose', 'Rizal', '(', 'FF150', ')', 'ug', 'BRP', 'Antonio', 'Luna', '(', 'FF151', ')', 'duol', 'sa', 'kadagatan', 'sa', 'Zambales', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 7, 8, 8, 8, 8, 8, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 5, 0] | cebuaner |
5,064 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'usa', 'ka', 'nationwide', 'poll', 'nga', 'gihimo', 'niadtong', 'Disyembre', '10-14', ',', '2022', 'sa', '1,200', 'ka', 'adult', 'nga', 'respondents', ',', '91', '%', 'kanila', 'miuyon', 'sa', 'Executive', 'Order', 'No.', '7', 'ni', 'Presidente', 'Bongbong', 'Marcos', 'nga', 'nagtugot', 'sa', 'boluntaryong', 'paggamit', 'sa', 'mga', 'maskara', 'sulod', 'ug', 'gawas', 'sa', 'balay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,065 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Presidente', 'Bongbong', 'Marcos', 'mipahibalo', 'nga', 'ang', 'Pilipinas', '"', '[', 'mo', ']', 'wala', '"', 'sa', 'bisan', 'unsang', 'pakiglambigit', 'sa', 'ICC', 'human', 'kini', 'misalikway', 'ang', 'apela', 'sa', 'Pilipinas', 'nga', 'suspensuhon', 'ang', 'imbestigasyon', 'batok', 'sa', 'drug', 'war.', 'mipahayag', 'ni', 'Marcos', 'nga', 'wala', 'sakop', 'sa', 'gahom', 'sa', 'ICC', '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, 1, 2, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0] | cebuaner |
5,066 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pito', 'ka', 'mga', 'tawo', ',', 'lakip', 'ang', 'tulo', 'ka', 'mga', 'bata', ',', 'gipusil', 'ug', 'gipatay', 'ni', 'Audrey', 'Hale', ',', '28', ',', 'samtang', 'siya', 'misulod', 'sa', 'The', 'Covenant', 'School', 'sa', 'Nashville.', 'Dihang', 'miresponde', 'ang', 'mga', 'polis', ',', 'gipusil', 'sila', 'ni', 'Hale', ',', 'hangtod', 'nga', 'napusilan', 'usab', 'siya', 'ug', 'namatay.', 'Ang', 'mga', 'biktima', 'giila', 'nga', 'sila', 'si', 'Evelyn', 'Dieckhaus', ',', 'Hallie', 'Scruggs', ',', 'ug', 'William', 'Kinney', ',', 'mga', 'estudyante', 'nga', 'nuybe', 'anyos', 'pa', ';', 'ug', 'Cynthia', 'Peak', ',', '61', ';', 'Katherine', 'Koonce', ',', '60', ';', 'ug', 'Mike', 'Hill', ',', '61', 'anyos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0] | cebuaner |
5,067 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'gibasura', 'sa', 'International', 'Criminal', 'Court', '(', 'ICC', ')', 'ang', 'hangyo', 'sa', 'gobyerno', 'sa', 'Pilipinas', 'nga', 'suspensuhon', 'ang', 'imbestigasyon', 'sa', 'duguong', 'kampanya', 'batok', 'sa', 'ilegal', 'nga', 'drugas', 'sa', 'administrasyon', 'ni', 'kanhi', 'Presidente', 'Rodrigo', 'Duterte', ',', 'si', 'Justice', 'Secretary', 'Crispin', 'Remulla', 'igo', 'na', 'lang', 'nisulti', 'sa', 'ICC', 'og', '"', 'good', 'luck', '"', 'ug', 'miinsister', 'nga', 'wala', 'kini', 'awtoridad.', 'sa', 'pagpahigayon', 'og', 'imbestigasyon', 'diin', 'kini', 'walay', 'hurisdiksyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 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] | cebuaner |
5,068 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ibinahagi', 'ni', 'dating', 'Senator', 'Kiko', 'Pangilinan', 'ang', 'isang', 'touching', 'encounter', 'niya', 'sa', 'isang', 'tagasuporta', 'na', 'umiyak', 'dahil', 'sa', 'pagkabigo', 'ng', 'Leni-Kiko', 'tandem', 'noong', '2022', 'national', 'elections', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
5,069 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kagamhanan', 'sa', 'lalawigan', 'sa', 'Sugbo', 'nihulga', 'niadtong', 'Lunes', ',', 'Marso', '27', ',', 'nga', 'mopasaka', 'og', 'kasong', 'administratiba', 'ug', 'kriminal', 'batok', 'sa', 'Bureau', 'of', 'Animal', 'Industry', '(', 'BAI', ')', 'tungod', 'sa', ''over', 'declaration', 'of', 'ASF', 'outbreak', ''', 'sa', 'Sugbo.', 'matod', 'sa', 'hepe', 'sa', 'Provincial', 'Legal', 'Office', 'nga', 'si', 'Atty.', 'Donato', 'Villa', 'Jr', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 7, 0, 0, 0, 5, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 1, 2, 2, 0] | cebuaner |
5,070 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'mismong', 'adlaw', 'sa', 'iyang', 'natawhan', 'karong', 'Marso', '28', ',', 'gibasura', 'sa', 'ICC', 'ang', 'apela', 'sa', 'gobyerno', 'sa', 'Pilipinas', 'nga', 'suspensuhon', 'mao', 'ni', 'ang', 'imbestigasyon', 'batok', 'sa', 'drug', 'war', 'ni', 'kanhi', 'Presidente', 'Rodrigo', 'Duterte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
5,071 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dayun', 'gi-patyan', 'pa', 'gud', 'kag', 'electric', 'fan', 'bisag', 'natulog', ',', 'murag', 'dili', 'pamilya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,072 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'mismong', 'adlaw', 'sa', 'iyang', 'adlawng', 'natawhan', 'karong', 'Marso', '28', ',', 'gibasura', 'sa', 'ICC', 'ang', 'apela', 'sa', 'gobyerno', 'sa', 'Pilipinas', 'nga', 'suspensuhon', 'mao', 'ni', 'ang', 'imbestigasyon', 'batok', 'sa', 'drug', 'war', 'ni', 'kanhi', 'Presidente', 'Rodrigo', 'Duterte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
5,073 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dako', 'ang', 'papel', 'ni', 'kanhi', 'Presidente', 'Rodrigo', 'Duterte', 'sa', 'katumanan', 'sa', 'House', 'of', 'Hope', 'tungod', 'kay', 'nidonar', 'siya', 'og', 'house', 'and', 'lot', 'para', 'sa', 'foundation.', 'Ang', 'pag-ayo', 'ug', 'pagkakabig', 'sa', 'balay', 'ngadto', 'sa', '"', 'cancer', 'haven', '"', 'nahimong', 'posible', 'pinaagi', 'sa', 'kooperasyon', 'ug', 'donasyon', 'sa', 'mga', 'higala', 'ug', 'supporters', 'ni', 'Duterte.', 'Nitambong', 'usab', 'sa', 'maong', 'kalihukan', 'si', 'Senador', 'Christopher', '"', 'Bong', '"', 'Go', ',', 'kanhi', 'presidential', 'assistant', 'ni', 'Duterte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,074 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DISGRASYA', 'SA', 'BACAY', ',', 'MINGLANILLA', 'CEBU', 'Mga', 'softdrinks', 'nausik', 'kay', 'natagak', 'sa', 'dalan', 'gikan', 'sa', 'delivery', 'truck', 'sa', 'Bacay', ',', 'Tulay', 'Minglanilla'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6] | cebuaner |
5,075 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'suspek', 'mao', 'ang', 'duha', 'ka', 'biyahero', 'nga', 'niagi', 'sa', 'AFP-PNP', 'Border', 'Control', 'Point', 'sa', 'Lasang', ',', 'Bunawan', ',', 'Davao', 'City', 'sayo', 'ning', 'Domingo', ',', 'Marso', '26', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,076 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['hambal', 'ni', 'Provincial', 'Administrator', 'Rayfrando', 'Diaz', 'II', ',', 'para', 'ini', 'sa', 'mga', 'magkadto', 'nga', 'turista', 'sa', 'probinsiya', 'atoll', 'sa', 'Semana', 'Santa', 'ug', 'Panaad', 'Festival.', 'Dugang', 'pa', 'niya', 'nga', 'isa', 'lang', 'ka', 'pasyente', 'ang', 'na-confine', 'sa', 'Cadiz', 'District', 'Hospital', ',', 'ang', 'nabilin', 'nga', 'COVID-19', 'facility', 'sa', 'probinsiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 7, 0, 0, 0, 0] | cebuaner |
5,077 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KADAKO', '!', 'Sa', 'imbestigasyon', 'sa', 'Bureau', 'of', 'Fire', 'Protection', '(', 'BFP', ')', 'Kapalong', 'nasayran', 'nga', '“electrical', 'ignition', 'cause', 'by', 'loose', 'connection”', 'ang', 'hinungdan', 'sa', 'sunog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,078 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaambit', 'sa', 'Kapuso', 'actress', 'nga', 'si', 'Kyline', 'Alcantara', 'sa', 'iyang', 'Instagram', 'Story', 'ang', 'litrato', 'nila', 'uban', 'sa', ''Annaliza', ''', 'co-stars', 'nga', 'sila', 'si', 'Denise', 'Laurel', 'ug', 'Andrea', 'Brillantes', 'sa', 'dihang', 'nagkita', 'sila', 'sa', 'BLACKPINK', ''Born', 'Pink', ''', 'concert', 'sa', 'Philippine', 'Arena', 'sa', 'Bulacan', 'niadtong', 'Marso', '26.', 'saad', 'ni', 'Kyline', 'sa', 'kanyang', 'Instagram', 'story', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0] | cebuaner |
5,079 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitandi', 'ni', 'House', 'tourism', 'committee', 'vice', 'chairman', 'Rep.', 'Marvin', 'Rillo', 'ang', 'oil', 'spill', 'sa', 'Oriental', 'Mindoro', 'sa', 'pagkalunod', 'sa', 'laing', 'oil', 'tanker', 'sa', 'Guimaras', 'niadtong', '2006.', 'ani', 'Rillo.', 'Ug', 'sukad', 'sa', 'pagkalunod', 'sa', 'MT', 'Solar', '17', 'ka', 'tuig', 'na', 'ang', 'milabay', ',', 'si', 'Rillo', 'miingon', 'nga', 'ang', 'inflation-adjusted', 'claims', 'alang', 'sa', 'kadaot', 'nga', 'gipahinabo', 'sa', 'MT', 'Princess', 'Empress', 'oil', 'spill', 'posibling', 'molapas', 'sa', '₱1.1', 'bilyon.', 'matud', 'ni', 'Rillo', 'Sukad', 'sa', 'pagkalunod', 'sa', 'dagat', 'sa', 'Naujan', 'kaniadtong', 'Pebrero', '28', ',', 'labing', 'menos', '149,000', 'ka', 'mga', 'tawo', 'ang', 'naapektuhan', ',', 'lakip', 'ang', 'kapin', 'sa', '13,000', 'nga', 'mga', 'mangingisda', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 2, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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] | cebuaner |
5,080 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Instagram', 'post', 'niadtong', 'weekend', ',', 'nag-pre-birthday', 'celebration', 'ang', 'South', 'Korean', 'star', 'atol', 'sa', 'ilang', 'BORN', 'PINK', 'concert', 'sa', 'Philippine', 'Arena', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 7, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 7, 8, 0, 0, 5, 6, 0] | cebuaner |
5,081 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naghimo', 'og', 'survey', 'ang', 'Victoria', 'Milan', ',', 'usa', 'ka', 'dating', 'website', 'para', 'sa', 'mga', 'naay', 'nang', 'karelasyon', 'o', 'asawa', ',', 'og', 'nahibalan', 'ang', 'siyam', 'ka', 'industriya', 'nga', 'ang', 'mga', 'empleyado', 'kay', 'nag-una', 'sa', 'mga', 'nigamit', 'sa', 'ilang', 'serbisyo.', 'Uban', 'sa', 'cheaters’', 'list', 'ang', 'mga', 'empleyado', 'sa', '(', '1', ')', 'financial', '(', 'bankers', ',', 'brokers', ',', 'analysts', ')', ',', '(', '2', ')', 'aviation', '(', 'pilots', ',', 'flight', 'attendants', ')', ',', '(', '3', ')', 'healthcare', '(', 'doctors', ',', 'nurses', ')', ',', '(', '4', ')', 'Business', '(', 'CEOs', ',', 'managers', ',', 'secretaries', ')', ',', '(', '5', ')', 'sports', '(', 'athletes', ',', 'instructors', ')', ',', '(', '6', ')', 'arts', '(', 'musicians', ',', 'models', ',', 'actors', ',', 'photographers', ')', ',', '(', '7', ')', 'nightlife', 'industry', '(', 'DJs', ',', 'dancers', ',', 'waiters', ')', ',', '(', '8', ')', 'communication', '(', 'journalists', ',', 'public', 'relations', ',', 'communicators', ')', ',', 'og', '(', '9', ')', 'legal', '(', 'lawyers', ',', 'secretaries', ',', 'prosecutors', ',', 'judges', ')', 'sectors', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,082 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'press', 'conference', ',', 'si', 'Justice', 'Secretary', 'Boying', 'Remulla', 'niingon', 'nga', 'duha', 'ngadto', 'sa', 'tulo', 'ka', 'tawo', 'ang', 'ilang', 'gikonsiderar', 'nga', 'utok', 'sa', 'pagpatay', 'kang', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo.', 'Matod', 'ni', 'Remulla', ',', 'nag-uswag', 'ang', 'imbestigasyon', ',', 'ug', '"', 'kana', 'ang', 'direksyon', 'nga', 'among', 'gipadulngan', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,083 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitubag', 'ang', 'vlogger', '/', 'entrepreneur', 'nga', 'si', 'Viy', 'Cortez', 'sa', 'usa', 'ka-komento', 'sa', 'usa', 'ka', 'netizen', 'nga', 'naingon', 'nga', 'dili', 'pa', 'raw', 'kalakaw', 'ang', 'walong', 'bulan', 'nga', 'si', 'Baby', 'Kidlat', 'kay', 'giaanad', 'daw', 'ni', 'Viy', 'og', 'kugos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 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, 1, 0, 0, 0] | cebuaner |
5,084 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Malipayong', 'gi-share', 'sa', 'Kapuso', 'singer', 'nga', 'si', 'Golden', 'Cañedo', 'sa', 'iyang', 'Facebook', 'ang', 'video', 'nga', 'ni', 'attend', 'siya', 'sa', 'concert', 'sa', 'sikat', 'nga', 'K-pop', 'girl', 'group', 'na', 'BLACKPINK', 'sa', 'Philippine', 'Arena', 'sa', 'Bulacan', 'sa', 'miaging', 'Sabado', ',', 'Marso', '25', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,085 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nipatigbabaw', 'ang', 'kasuko', 'sa', 'social', 'media', 'personality', 'ug', 'partner', 'ni', 'Whamos', 'Cruz', 'nga', 'si', 'Antonette', 'Gail', 'Del', 'Rosario', 'sa', 'gi-edit', 'nga', 'litrato', 'sa', 'ilang', 'anak', 'nga', 'si', 'Baby', 'Meteor', 'kansang', 'ulo', 'gibutang', 'sa', 'lawas', 'sa', 'unggoy.', 'Wala', 'makapugong', 'si', 'Antonette', 'ug', 'gikulata', 'ang', 'basher', ':', '“Wala', 'kang', 'karapatang', 'babuyin', 'yung', 'picture', 'ng', 'bata.', 'Walang', 'kinalaman', 'yung', 'bata', 'para', 'ganyanin', 'mo', 'at', 'higit', 'sa', 'lahat', ',', 'WALA', 'KANG', 'KARAPATAN', 'DAHIL', 'HINDI', 'MO', 'ANAK', 'YAN', '!', '!', 'GRABE', 'NAKAKANGINIG', 'KA', 'NG', 'DUGO', '...', '"', 'Sa', 'usa', 'ka', 'post', ',', 'gihulga', 'ni', 'Antonette', 'nga', '"', 'mapapahimas-rehas', '"', 'bisan', 'kinsa', 'nga', 'nag-insulto', 'sa', 'iyang', 'anak.', 'ani', 'Antonette', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,086 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Suno', 'sa', 'pila', 'ka', 'saksi', 'sang', 'aksidente', ',', 'gintinguhaan', 'sang', 'teenager', 'nga', 'kuhaon', 'ang', 'tumbler', 'nga', 'ginhaboy', 'sang', 'isa', 'sa', 'mga', 'triathletes', 'para', 'malikawan', 'nga', 'madisgrasya', 'sa', 'duwa', ',', 'apang', 'sang', 'kuhaon', 'na', 'sang', 'pamatan-on', 'ang', 'tumbler', ',', 'nabunggoan', 'niya', 'ang', 'isa', 'ka', 'madasig', 'nga', 'triathlete', 'nga', 'wala', 'pa', 'nganli.', 'Ang', 'mga', 'personahe', 'sa', 'Bureau', 'of', 'Fire', 'Protection', '(', 'BFP', ')', '-Panabo', 'City', 'daling', 'midala', 'sa', 'biktima', 'sa', 'tambalanan', ',', 'samtang', 'ang', 'triathlete', 'mibarog', 'ug', 'mitapos', 'sa', 'lumba.', 'Apan', 'matud', 'sa', 'inahan', 'sa', 'biktima', 'nga', 'na-comatose', 'ang', 'iyang', 'anak', 'human', 'sa', 'operasyon', 'tungod', 'sa', 'aksidente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,087 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Guys', ',', 'tip', 'naman', ':', 'Unsaon', 'pagsingil', 'sa', 'utang', 'sa', 'imong', 'ex', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,088 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KANANG', 'LIMA', 'DAYON', 'KA', 'SONG', 'IMONG', 'GIBUTANG', 'SA', 'VIDEOKE', ',', 'BISAG', 'DILI', 'IKAW', 'ANG', 'NI-ARKILA'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,089 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unsay', 'sign', 'nga', 'dato', 'ang', 'pamilya', 'sa', 'pikas', 'cottage', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,090 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'gi-base', 'sa', 'gipaabot', 'nga', 'pagtungha', 'sa', 'crescent', 'moon', 'karong', 'tuiga.', 'Sa', 'panahon', 'sa', 'Ramadan', ',', 'ang', 'mga', 'Muslim', 'naglikay', 'sa', 'pagkaon', ',', 'pag-inom', ',', 'pagpanigarilyo', ',', 'ug', 'pakighilawas', 'gikan', 'sa', 'pagsubang', 'sa', 'adlaw', 'hangtod', 'sa', 'pagsalop', 'sa', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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] | cebuaner |
5,091 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DAOG', 'NA', 'SAB', 'Ang', 'Super', 'Lotto', '6', '/', '49', 'jackpot', 'prize', 'nadaogan', 'na', 'karong', 'Martes', ',', 'Marso', '21', ',', 'uban', 'sa', 'winning', 'number', 'combination', 'nga', '12-29-35-34-13-08'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,092 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mokabat', 'sa', '25', 'ka', 'gidudahang', 'mga', 'manggugubat', 'ang', 'nasikop', 'sa', 'kapulisan', 'sa', 'giingong', 'illegal', 'nga', 'away', 'sa', 'Upper', 'Tabik', ',', 'Barangay', 'Tabok', ',', 'Mandaue', 'City', ',', 'Cebu', 'niadtong', 'Marso', '19', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0] | cebuaner |
5,093 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihimakak', 'ni', 'Senador', 'Robin', 'Padilla', 'nga', 'interesado', 'siya', 'nga', 'mahimong', 'Bise', 'Presidente', 'sa', 'nasod', ',', 'apan', 'posibleng', 'modagan', 'siya', 'sa', 'ikaduhang', 'termino', 'sa', 'Senado', 'kon', 'mausab', 'ang', 'politikanhong', 'istruktura', '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, 1, 2, 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] | cebuaner |
5,094 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'video', 'ni', 'Negros', 'Oriental', '3rd', 'District', 'Rep.', 'Arnolfo', 'Teves', 'Jr.', 'post', 'sa', 'iyang', 'Facebook', ',', 'iyang', 'gibutyag', 'nga', 'posibleng', 'may', 'kalambigitan', 'sa', 'e-cock', 'ang', 'pressure', 'kaniya.', 'Matod', 'niya', 'ganahan', 'siya', 'sa', 'e-cocktails', 'nga', '"', 'masolo', '"', ',', 'nga', 'nahibaw-an', 'niyang', 'supak', 'si', 'Presidente', 'Bongbong', 'Marcos.', 'Matod', 'niya', 'nga', 'nisulti', 'siya', 'kay', 'nitug-an', 'siya', 'nga', 'duna', 'siyay', '"', 'order', '"', 'gikan', 'sa', 'Palasyo', 'aron', 'iduso', 'siya', 'sa', 'pagpatay', 'kang', 'kanhi', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', ',', 'apan', 'nagtuo', 'siya', 'nga', 'dili', 'kana', 'mahimo', 'ni', 'Marcos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 6, 6, 6, 0, 1, 2, 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, 0, 0, 0, 7, 8, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,095 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'barangay', 'nga', 'gibantayan', 'sa', 'dakbayan', 'sa', 'Sugbo', 'mao', 'ang', 'Inayawan', ',', 'Bulacao', ',', 'Cogon', 'Pardo', ',', 'Toong', ',', 'Buhisan', ',', 'Tisa', ',', 'Sudlon', '2', ',', 'Cambinocot', ',', 'Busay', ',', 'Budlaan', 'ug', 'San', 'Jose.', 'Si', 'City', 'Agriculturist', 'Joey', 'Baclayon', 'niingon', 'nga', 'base', 'sa', 'ilang', 'gihimong', 'mga', 'eksaminasyon', ',', 'walay', 'bakas', 'sa', 'maong', 'virus', 'sa', 'mga', 'nahisgutang', '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. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,096 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitangtang', 'ni', 'Cebu', 'Gov', 'Gwendolyn', 'Garcia', 'ang', 'tanan', 'nga', 'nagkontrolar', 'sa', 'paglihok', 'sa', 'mga', 'kahayupan', 'ug', 'karne', 'sa', 'baboy', 'sa', 'probinsiya', ',', 'nunot', 'sa', 'pagka-detect', 'sa', 'African', 'swine', 'fever', '(', 'ASF', ')', 'sa', 'unom', 'ka', 'dapit', 'sa', 'probinsiya', 'karong', 'buwana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,097 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Jail', 'Senior', 'Inspector', 'Ellen', 'Rose', 'Saragena', ',', 'hepe', 'sa', 'Bureau', 'of', 'Jail', 'Management', 'and', 'Penology', '(', 'BJMP', ')', '-Community', 'Relations', 'Section', ',', 'niingon', 'sa', 'Kafehan', 'sa', 'Dawaw', 'niadtong', 'Marso', '20', ',', 'nga', 'ang', 'mga', 'persons', 'deprived', 'of', 'liberty', '(', 'PDLs', ')', 'nga', 'baldado', ',', 'masakiton', ',', 'ug', 'senior', 'citizens.', 'nagpabilin', 'sa', 'Davao', 'City', 'Jail', 'Annex', ',', 'apan', 'karon', 'naapil', 'na', 'usab', 'ang', 'mga', 'miyembro', 'sa', 'LGBT', 'community.', 'Pagklaro', 'ni', 'Saragena', ',', 'dili', 'kini', 'diskriminasyon', 'tungod', 'kay', 'ila', 'kining', 'gihimo', 'aron', 'malikayan', 'ang', 'pag-abuso', 'ug', 'impeksyon', 'gumikan', 'sa', 'paghuot', 'sa', 'sulod', 'sa', 'city', 'jail', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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] | cebuaner |
5,098 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ubos', 'sa', 'National', 'Pabahay', 'Para', 'sa', 'Pilipino', 'Housing', 'program', 'sa', 'administrasyong', 'Marcos', ',', '10', 'ka', 'mga', 'high-rise', 'building', ',', 'nga', 'adunay', 'kinatibuk-ang', '8,000', 'ka', 'units', ',', 'ang', 'tukoron', 'sa', 'South', 'Road', 'Properties', '(', 'SRP', ')', 'alang', 'sa', 'mga', 'pamilyang', 'apektado', 'sa', 'three-meter', 'easement', 'rule.', 'pagpatay', ',', 'ingon', 'man', 'kadtong', 'gisunog', 'sa', 'siyudad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,099 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Sen.', 'Sonny', 'Anagara', ',', 'dili', 'dayon', 'masulbad', 'ang', 'problema', 'sa', 'pampublikong', 'transportasyon', ',', 'tungod', 'kay', 'kinahanglan', 'pa', 'kini', 'nga', 'moagi', 'sa', 'makuti', 'nga', 'pagtuon', 'ug', 'dili', 'masulbad', 'sa', 'pipila', 'lang', 'ka', 'adlaw.', 'Mao', 'nga', 'iyang', 'gisang-at', 'ang', 'Senate', 'Bill', 'No.', '1005', ',', 'o', 'ang', 'Sustainable', 'Transportation', 'Act.', 'Kahinumduman', 'nga', 'bag-ohay', 'lang', 'gilusad', 'sa', 'mga', 'drayber', 'sa', 'jeepney', 'ug', 'UV', 'Express', 'ang', 'dakong', 'pagpahunong', 'batok', 'sa', 'gitakdang', 'jeepney', 'phaseout', ',', 'may', 'kalabotan', 'sa', 'PUV', 'Modernization', 'program', '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, 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, 7, 8, 8, 8, 0, 0, 0, 7, 8, 8, 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] | cebuaner |
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