Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,600 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pagkagabii', ',', 'si', 'Perez', 'nagkanayon', 'nga', 'ilang', 'gikahinabi', 'si', 'Cabiging', 'kalabot', 'sa', 'mga', 'pasangil', 'sa', 'mga', 'bata', 'apan', 'nihimakak', 'kini', 'pag-una', ',', 'apan', 'sa', 'pagpangutana', 'kalabot', 'ni', 'Richard', 'nga', 'iyang', 'na-uyab', ',', 'diha', 'na', 'kini', 'niangkon', 'sa', 'iyang', 'sayop', 'ug', 'dakong', 'gusto', 'sa', 'maong', '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, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,601 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Biyernes', ',', 'sa', 'dihang', 'nagkuha', 'og', 'affidavit', 'ang', 'mga', 'pulis', 'gikan', 'sa', 'mga', 'bata', 'ang', '16', 'anyos', 'sab', 'nga', 'si', 'Ruel', 'ang', 'nitug-an', 'sa', 'kapulisan', 'nga', 'usa', 'ka', 'higayon', ',', 'nakighilawas', 'kaniya', 'si', 'Cabiging', 'bugti', 'sa', 'P40', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0] | cebuaner |
5,602 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paambit', 'ni', 'Perez', 'nga', 'tungod', 'sa', 'hitabo', ',', 'gipaubos', 'na', 'nila', 'ang', 'mga', 'bata', 'og', 'stress', 'debriefing', 'sanglit', 'kini', 'na-trauma', 'sa', 'hitabo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,603 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sab', 'niini', ',', 'ipaubos', 'ni', 'Perez', 'og', 're-organization', 'ang', 'HOME', 'Center', 'ug', 'balikon', 'sa', 'pag-orient', 'ang', 'mga', 'staff', 'sa', 'ilang', 'trabaho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,604 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Adunay', '30', 'ka', 'kawani', 'ang', 'CSWS', 'sa', 'maong', 'pasilidad', 'nga', 'adunay', 'sud', 'nga', '75', 'ka', 'mga', 'bata', 'diin', '14', 'niini', 'mga', 'children', 'in', 'conflict', 'with', 'the', 'law', 'ug', '20', 'ka', 'children', 'at', 'risk', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 0, 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 |
5,605 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Personal', 'nga', 'giinspeksyon', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'ang', 'mga', 'lugar', 'sa', 'Eastern', 'Visayas', 'nga', 'apektado', 'sa', 'pagkusokuso', 'sa', 'Bagyong', 'Urduja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 6, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
5,606 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sakay', 'sa', 'chopper', ',', 'nag-aerial', 'inspection', 'ang', 'presidente', 'aron', 'personal', 'nga', 'ma-assess', 'ang', 'gibilin', 'nga', 'kadaot', 'sa', 'kalamidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,607 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'lungsod', 'naglakip', 'sa', 'Daanbantayan', ',', 'Medellin', ',', 'Sogod', ',', 'San', 'Remigio', ',', 'Madridejos', ',', 'Santa', 'Fe', ',', 'Bantayan', ',', 'Tabogon', ',', 'ug', 'Borbon', 'ug', 'siyudad', 'sa', 'Bogo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 0] | cebuaner |
5,608 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'PDRRMO', 'head', 'Baltazar', 'Tribunalo', 'nga', 'way', 'namatay', 'sa', 'kalamidad', 'apan', 'dunay', 'mga', 'karsada', 'ug', 'pananom', 'ang', 'nangaguba', 'tungod', 'sa', 'kakusog', 'sa', 'hangin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,609 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Provincial', 'Board', 'Member', 'Celestino', '“Tining”', 'Martinez', 'III', 'niingon', 'nga', 'tungod', 'sa', 'deklarasyon', 'makagamit', 'na', 'ang', 'maong', 'mga', 'LGU', 'sa', 'ilang', 'calamity', 'funds', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,610 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'artsidyosesis', 'pinaagi', 'sa', 'Caritas', 'Cebu', 'magpadala', 'og', 'hinabang', 'sa', 'mga', 'pamilya', 'nga', 'apektado', 'sa', 'kalamidad', 'ug', 'nanghangyo', 'usab', 'sa', 'publiko', 'nga', 'mobuhat', 'usab', 'sa', 'ingon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,611 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'PDRRM', 'Council', 'nakatala', 'og', 'unom', 'ka', 'major', 'landslides', 'sa', 'Biliran', 'diin', 'gikabalak-an', 'nga', 'mosaka', 'pa', 'ang', 'gidaghanon', 'sa', 'mga', 'nangamatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,612 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Health', 'Secretary', 'Francisco', 'Duque', 'III', 'nipahibawo', 'nga', 'naggahin', 'ang', 'ilang', 'buhatan', 'og', 'P4.5', 'milyones', 'nga', 'balor', 'sa', 'mga', 'tambal', 'alang', 'sa', 'Eastern', 'Visayas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,613 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Forensic', 'expert', 'sa', 'Public', 'Attorney’s', 'Office', '(', 'PAO', ')', ',', 'Dr.', 'Erwin', 'Erfe', ',', 'ang', 'mangu', 'sa', 'pag-ugkat', 'ug', 'pag-eksamin', 'sa', 'nahabilin', 'nga', 'bahin', 'sa', 'lawas', 'ni', 'anhing', 'Arsobispo', 'Teofilo', 'Camomot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
5,614 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Padre', 'Mhar', 'Vincent', 'Balili', ',', 'vice', 'postulator', 'sa', 'kawsa', 'pagpabayaw', 'pagkasantos', 'ni', 'Camomot', ',', 'nibutyag', 'nga', 'usa', 'ka', 'anthropologist', 'si', 'Erfe', 'nga', 'nagbase', 'sa', 'kaulohan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] | cebuaner |
5,615 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Enero', '3', ',', 'himuon', 'ang', 'pag-ugkat', 'ug', 'pag-eksamin', 'sa', 'unsay', 'nahabilin', 'sa', 'lawas', 'ni', 'Camomot', ',', 'nga', 'molungtad', 'og', 'labing', 'dugay', 'walo', 'ka', 'oras', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,616 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugdan', 'kini', 'og', 'misa', 'nga', 'pangulohan', 'ni', 'Palma', 'kauban', 'sa', 'Board', 'of', 'Consultors', 'sa', 'artsidyosesis', 'sa', 'Sugbo', ',', 'DST', 'ug', 'mga', 'kabanay', 'ni', 'Camomot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, 0, 1, 0] | cebuaner |
5,617 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'ni', 'Balili', 'apil', 'sa', 'mga', 'rason', 'sa', 'pag-abli', 'sa', 'lubnganan', 'ni', 'Camomot', 'ang', 'pagsukod', 'kon', 'pila', 'ang', 'gitas-on', 'ug', 'kon', 'unsay', 'estado', 'sa', 'nahabilin', 'nga', 'lawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,618 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Balili', 'nipaambit', 'nga', 'nisugyot', 'si', 'Padre', 'Samson', 'Silloriquez', ',', 'ang', 'main', 'postulator', 'sa', 'kawsa', 'ni', 'Camomot', ',', 'nga', 'magkuha', 'og', 'bahin', 'sa', 'nahabilin', 'nga', 'lawas', 'niini', 'aron', 'maoy', 'dad-on', 'didto', 'sa', 'Roma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,619 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibarugan', 'sa', 'labing', 'taas', 'nga', 'opisyal', 'sa', 'artsidyosesis', 'sa', 'Sugbo', 'nga', 'dili', 'matawag', 'nga', 'kaminyuon', 'ang', 'panaghiusa', 'sa', 'managsama', 'og', 'gender', 'sama', 'sa', 'babaye', 'ug', 'babaye', 'o', 'lalake', 'ug', 'lalake', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,620 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'arsobispo', 'nipasabot', 'ang', 'simbahan', 'nagsige', 'og', 'tudlo', 'nga', 'ang', 'pagpakasal', 'sa', 'managsama', 'og', 'gender', 'dili', 'matawag', 'nga', 'kaminyuon', 'kay', 'alang', 'sa', 'simbahan', 'ang', 'kaminyuon', 'alang', 'lang', 'kini', 'sa', 'usa', 'ka', 'lalake', 'tali', 'sa', 'usa', 'ka', 'babaye', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,621 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'presidente', 'namulong', 'nga', 'ubos', 'sa', 'iyang', 'administrasyon', 'makasiguro', 'og', 'kaangayan', 'ug', 'suporta', 'ang', 'LGBT', 'nga', 'nakapaabiba', 'nila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0] | cebuaner |
5,622 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Duterte', 'nidugang', 'nga', 'lisod', 'na', 'nga', 'ipamugos', 'nga', 'ipatuman', 'ang', 'dugay', 'na', 'nga', 'moralidad', 'sa', 'moderno', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,623 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Suma', 'pa', 'niini', 'kon', 'unsay', 'makapalipay', 'alang', 'sa', 'LGBT', 'community', 'iya', 'kining', 'tumanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0] | cebuaner |
5,624 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugod', 'karong', 'semanaha', ',', 'subsub', 'na', 'ang', 'kampanya', 'sa', 'kapulisan', 'sa', 'tibuok', 'rehiyon', 'siyete', 'batok', 'sa', 'ilegal', 'nga', 'drugas', 'human', 'sila', 'hatagi', 'na', 'usab', 'ug', 'pagtugot', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'nga', 'molusad', 'og', 'anti-drug', 'operation', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 2, 0, 0, 0, 0, 0, 0] | cebuaner |
5,625 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaubos', 'una', 'sa', 'seminar', 'batok', 'sa', 'pagtamod', 'sa', 'tawhanong', 'katungod', 'ang', 'mga', 'sakop', 'sa', 'Drug', 'Enforcement', 'Unit', 'sa', 'dili', 'pa', 'sila', 'molusad', 'sa', 'anti', 'drug', 'operation', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,626 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Chief', 'Supt.', 'Jose', 'Mario', 'Espino', 'nga', 'sama', 'sa', 'ilang', 'gihimo', 'sa', 'nanglabay', 'nga', 'mga', 'buwan', 'diin', 'walay', 'mga', 'nakalas', 'kung', 'walay', 'drug', 'personalities', 'nga', 'mosukol', 'sa', 'kapulisan', 'mao', 'usab', 'ang', 'ilang', 'buhaton', '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, 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] | cebuaner |
5,627 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dunay', 'mga', 'kausaban', 'sa', 'pagpatuman', 'sa', 'ilang', 'anti', 'drug', 'operation', 'kini', 'sama', 'sa', 'pagpahibawo', 'sa', 'PDEA', 'usa', 'ka', 'oras', 'sa', 'dili', 'pa', 'ang', 'operation', 'kung', 'ang', 'ilang', 'subject', 'usa', 'ka', 'high', 'value', 'target', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,628 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Estrikto', 'karon', 'ang', 'kapulisan', 'sa', 'ilang', 'pagbalik', 'sa', 'gubat', 'batok', 'sa', 'ilegal', 'nga', 'drugas', 'sama', 'sa', 'pagsunod', 'sa', 'police', 'operational', 'procedures', ',', 'pagtamod', 'sa', 'tawhanong', 'katungod', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 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 |
5,629 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kung', 'ang', 'ilang', 'operasyon', 'makapatay', 'sa', 'drug', 'personality', 'nga', 'misukol', 'sa', 'mga', 'police', 'diha-diha', 'dayon', 'molusad', 'ug', 'imbestigasyon', 'ang', 'PRO-7', 'aron', 'masuta', 'kinsa', 'ang', 'nakapusil', 'ug', 'unsa', 'nga', 'armas', 'ang', 'gigamit', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,630 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisaad', 'usab', 'sa', 'heneral', 'nga', 'abli', 'sa', 'publiko', 'ang', 'ilang', 'imbestigasyon', 'dihadiha', 'dayon', 'kung', 'giunsa', 'sa', 'pagpusil', 'sa', 'pulis', 'ang', 'drug', 'personality', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,631 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Awhag', 'sa', 'PRO-7', 'sa', 'mga', 'target', 'sa', 'anti', 'drug', 'operation', 'nga', 'motahan', 'nalang', 'sa', 'kapulisan', 'ug', 'dili', 'mopakita', 'ug', 'pagsukol', 'aron', 'dili', 'sila', 'madisgrasya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,632 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'maglisud', 'ang', 'kapulisan', 'ning', 'ilang', 'pagbalik', 'sa', 'kampanya', 'batok', 'sa', 'ilegal', 'nga', 'drugas', 'sanglit', 'nagpadayon', 'ang', 'gihimong', 'monitoring', 'sa', 'PNP', 'ug', 'sa', 'PDEA-7.D'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 0, 0, 3] | cebuaner |
5,633 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Presyo', 'sa', 'gipamaligya', 'nga', 'pabuto', 'sa', 'barangay', 'Babag', ',', 'dakbayan', 'sa', 'Lapu-Lapu', 'ang', 'misaka', 'gumikan', 'sa', 'mga', 'nagasto', 'sa', 'mga', 'manufacturer', 'og', 'firecracker', '/', 'pyrotechnic', 'sa', 'ilang', 'paglukat', 'og', 'permit', 'o', 'lisensiya', 'alang', 'sa', 'maong', '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, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,634 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagsugod', 'niadtong', 'Disyembre', '16', 'ug', 'motapas', 'sa', 'Disyembre', '31', ',', '2017', 'ang', 'pag-display', 'sa', 'mga', 'pabuto', 'sa', 'daplin', 'sa', 'kalsada', 'sa', 'Barangay', 'Babag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,635 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'niining', 'pagsuwat', 'minus', 'pa', 'ang', 'mga', 'namalit', 'gawas', 'niadtong', 'wholesaler', 'nga', 'taga', 'lalawigan', 'sa', 'Negros', 'Oriental', 'nga', 'nangumpra', 'aron', 'usab', 'iya', 'nga', 'ibaligya', 'sa', 'ilang', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,636 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'sa', 'mga', 'manindahay', 'nga', 'modagsa', 'ang', 'ilang', 'mga', 'kostumer', 'sa', 'mga', 'adlaw', 'nga', 'duol', 'na', 'ang', 'kasaulogan', 'sa', 'Pasko', 'ug', 'pagsugat', 'sa', 'Bag-ong', 'Tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,637 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'sa', 'ilang', 'mga', 'produkto', 'kansang', 'presyo', 'ni-umento', 'naglakip', 'sa', 'kwitis', 'gikan', 'sa', 'tag-P2', 'mapalit', 'na', 'sa', 'tag-P3', 'ang', 'buok', ',', '‘whistle', 'bomb’', 'ug', '‘shot', 'gun’', 'gikan', 'sa', 'P4', 'ngadto', 'na', 'sa', 'tag-P5', ',', 'judas', 'belt', 'tag-P500', 'ang', 'usa', 'ka', 'dupa', 'nga', 'gitas-on', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,638 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'mga', 'komon', 'nga', '‘pyrotechnic’', 'ang', '‘bomb', 'shell’', 'mapalit', 'gikan', 'sa', 'P5,000', 'ngadto', 'sa', 'P7,000', 'depende', 'sa', 'klase', 'niini', 'samtang', 'ang', '‘beauty', 'in', 'sky’', 'gikan', 'sa', 'P2,500', 'ngadto', 'na', 'sa', 'P3,000', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,639 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibalo', 'ang', 'publiko', 'nga', 'gilugwayan', 'ang', 'pagdawat', 'ug', 'pag-ilis', 'sa', 'old', 'banknotes', 'o', 'karaang', 'kuwarta', 'hangtod', 'sa', 'Disyembre', '29', ',', '2017', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,640 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'ikatulo', 'na', 'nga', 'higayon', 'ang', 'paglugway', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', '(', 'BSP', ')', 'ang', 'deadline', 'sa', 'pagpailis', 'sa', 'karaan', 'ug', 'paggamit', 'na', 'sa', 'new', 'sesign', 'sereis', '(', 'NDS', ')', 'sa', 'kuwarta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,641 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Atty.', 'Leonides', 'B.', 'Sumbi', ',', 'BSP', 'Regional', 'Director', ',', 'nga', 'daghan', 'pa', 'ang', 'wala', 'nakahibalo', 'sa', 'kausaban', 'sa', 'old', 'banknotes', 'hangtod', 'sa', 'new', 'banknotes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0] | cebuaner |
5,642 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'ni', 'Atty.', 'Sumbi', 'ang', 'kadaghanan', 'nga', 'sa', 'katong', 'mga', 'adunay', 'kwarta', 'gipahibalo', 'nga', 'dili', 'nalang', 'maghuwat', 'pa', 'sa', 'sunod', 'nga', 'extension', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,643 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Adunay', 'sab', 'uban', 'nga', 'mga', 'banko', 'nga', 'gihangyo', 'sa', 'pagdawat', 'apan', 'boluntaryo', 'kini', 'sa', 'ilang', 'parte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,644 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'wala', 'nay', 'panahon', 'ang', 'bangko', 'para', 'molinya', 'pa', 'ani', 'didto', 'ug', 'maong', 'gipangitaan', 'na', 'kini', 'og', 'pamaagi', 'sa', 'BSP', 'aron', 'dili', 'sila', 'malangan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 0, 0, 0, 0, 0] | cebuaner |
5,645 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'laing', 'bahin', ',', 'gipagawas', 'nasad', 'sa', 'BSP', 'ang', 'bag-ong', '5', 'peso', 'coin', 'sa', 'niaging', 'Nobiyembre', '30', 'nga', 'anaa', 'ang', 'nawng', 'ni', 'Bonifacio', 'apan', 'limitado', 'ra', 'ang', 'supply', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
5,646 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagka', 'karon', 'gipangitaan', 'pa', 'og', 'pamaagi', 'sa', 'mga', 'tigdumala', 'para', 'makita', 'ug', 'mklaro', 'ang', 'kalahian', 'niani', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,647 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'bag-ong', '5', 'peso', 'coin', 'kay', 'NGC', 'coin', 'ug', 'apil', 'na', 'kini', 'sa', 'bag-ong', 'henerasyon', 'sa', 'peso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,648 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dako', 'nga', 'porsyento', 'nga', 'adunay', 'mga', 'peke', 'nga', 'salapi', 'gikan', 'sa', '100', 'ngadto', 'sa', '1000', 'mahitungod', 'kay', 'dako', 'ang', 'bili', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,649 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kuspaw', 'ang', 'printa', 'human', 'lisod', 'ang', 'pagkopya', 'sa', 'printing', 'machine', 'sa', 'pagpronta', 'og', 'kuwarta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,650 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Modus', 'ang', 'pagdalisali', 'sa', 'pagpalit', 'aron', 'dili', 'makita', 'pag-ayo', 'ang', 'kuwarta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,651 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'mangingilad', 'nagpahimulos', 'sa', 'publiko', 'tungod', 'kay', 'nahibal-an', 'nila', 'nga', 'ang', 'tanan', 'nagdali', 'labina', 'sa', 'panahon', 'sa', 'pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,652 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nihatag', 'siya', 'og', 'tambag', 'ngadto', 'sa', 'mga', 'kahera', 'nga', 'kung', 'makadawat', 'sila', 'og', 'peke', 'nga', 'kuwarta', 'ihatag', 'ni', 'nila', 'sa', 'ilahang', 'mga', 'amo', 'aron', 'mailisan', 'ug', 'ma-report', 'kini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,653 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'niya', 'nga', 'taplan', 'sab', 'sa', 'mga', 'agalon', 'ang', 'nalugi', 'nga', 'halin', 'sa', 'ilang', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,654 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Natanggong', 'karon', 'sa', 'prisohan', 'ang', 'nagpaila', 'nga', 'bodyguard', 'sa', 'gideklarar', 'sa', 'korte', 'nga', 'mayor', 'sa', 'lungsod', 'sa', 'Tuburan', 'nga', 'si', 'Daphne', 'Lagon', ',', 'human', 'giingong', 'nagpabuto', 'kini', 'sa', 'iyang', 'armas', 'sa', 'The', 'Distillery', 'Bar', 'kagahapon', 'sa', 'kaadlawon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0] | cebuaner |
5,655 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'Lagon', 'wala', 'pa', 'pormal', 'makalingkod', 'sa', 'posisyon', 'tungod', 'sa', 'padayong', 'legal', 'nga', 'away', 'sa', 'Commission', 'on', 'Elections', '(', 'Comelec', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0] | cebuaner |
5,656 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nisuway', 'pa', 'pag-ikyas', 'si', 'Arban', 'apan', 'dihang', 'nisakay', 'na', 'kini', 'og', 'taxi', ',', 'nakatulog', 'kini', 'ug', 'dili', 'mapukaw', 'sa', 'drayber', 'hinungdan', 'nga', 'gihatod', 'kini', 'sa', 'Mabolo', 'Police', 'Station', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 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 |
5,657 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'paghatod', 'niini', ',', 'anaa', 'didto', 'ang', 'duha', 'ka', 'reklamante', 'diin', 'positibo', 'nga', 'gitudlo', 'si', 'Arban', 'hinungdan', 'nga', 'gipriso', 'kini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] | cebuaner |
5,658 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'inisyal', 'nga', 'pakisusi', 'sa', 'Mabolo', 'Police', ',', 'nasayran', 'nga', 'kuyog', 'ni', 'Lagon', 'ang', 'iyang', 'mga', 'sakop', 'ug', 'mga', 'bodyguard', ',', 'usa', 'na', 'niini', 'si', 'Arban', ',', 'sa', 'maong', 'bar', 'sa', 'Brgy.', 'Kasambagan', ',', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
5,659 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingon', 'nga', 'dili', 'moubos', 'sa', '20', 'ka', 'mga', 'tawo', 'ang', 'ilahang', 'grupo', 'ug', 'didto', 'sila', 'sa', 'ikaduhang', 'andana', 'nagpahimutang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,660 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisuwayan', 'ni', 'Lagon', 'pag-uwang', 'si', 'Arban', 'apan', 'nibunot', 'kini', 'sa', 'iyang', '.45', 'kalibre', 'nga', 'armas', 'ug', 'nagpabuto', 'sa', 'sawog', 'diin', 'naigo', 'ang', 'tiles', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,661 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'gihimo', 'ni', 'Arban', ',', 'nagkagubot', 'ang', 'bar', 'ug', 'daghang', 'nangabuak', 'sulod', 'niini', 'nga', 'nagkantidad', 'sumatotal', 'og', 'P50,000', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,662 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'insidente', ',', 'gipakalma', 'usab', 'ni', 'Jake', 'Mendez', ',', '43', ',', 'ang', 'iyang', 'mga', 'kauban', 'apan', 'kalit', 'lang', 'sila', 'giduol', 'ni', 'Arban', 'ug', 'giingon', 'nga', 'gitiunan', 'kini', 'og', 'armas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,663 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dihang', 'nidangop', 'sa', 'Mabolo', 'Police', 'ang', 'representante', 'sa', 'The', 'Distillery', 'Bar', 'ug', 'si', 'Mendez', 'aron', 'magpa-blotter', ',', 'naabtan', 'nila', 'nga', 'nadala', 'sa', 'usa', 'ka', 'sakop', 'sa', 'police', 'station', 'si', 'Arban', 'human', 'kini', 'gidala', 'sa', 'usa', 'ka', 'taxi', 'driver', 'kay', 'grabe', 'ang', 'katulog', 'dihang', 'gibiyahe', 'kini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 0, 0, 0, 5, 6, 6, 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, 0, 0, 0, 0, 0] | cebuaner |
5,664 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitawagan', 'sa', 'Superbalita', 'Cebu', 'si', 'Lagon', 'aron', 'makuha', 'iyang', 'habig', 'apan', ',', 'dili', 'kini', 'motubag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,665 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mayor', 'Democrito', 'Diamante', 'niklaro', 'nga', 'siya', 'pa', 'ang', 'naglingkod', 'nga', 'mayor', 'sa', 'Tuburan', 'kay', 'padayon', 'pa', 'nga', 'gidungog', 'sa', 'Comelec', 'en', 'banc', 'ang', 'iyang', 'giduso', 'nga', 'petition', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0] | cebuaner |
5,666 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Col.', 'Medel', 'Aguilar', ',', 'tigpamaba', 'sa', 'Centcom', ',', 'ningon', 'nga', 'nakig-alayon', 'na', 'sila', 'sa', 'mga', 'lider', 'sa', 'nagkalainlaing', 'sektor', 'aron', 'makabantay', 'ug', 'makatabang', 'sa', 'pagpaniid', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,667 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Coast', 'Guard', 'Station', 'Cebu', 'Commander', 'Jerome', 'Cayabyab', 'nibutyag', 'nga', 'nakabalik', 'na', 'og', 'biyahe', 'ang', 'mga', 'sakyanan', 'sa', 'kadagatan', 'human', 'nalibkas', 'na', 'ang', 'Tropical', 'Cyclone', 'Warning', 'Signal', 'sa', 'Sugbo', 'ug', 'ubang', 'bahin', 'sa', 'Kabisay-an', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
5,668 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Miabot', 'og', '62', 'ka', 'mga', 'sakyanan', 'sa', 'kadagatan', 'ang', 'wa', 'makabiyahe', 'gumikan', 'sa', 'dautang', '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, 0, 0, 0] | cebuaner |
5,669 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'si', 'Daanbantayan', 'Mayor', 'Vicente', 'Loot', 'niingon', 'nga', 'nakabalik', 'na', 'sa', 'ilang', 'pinuy-anan', 'ang', 'kapin', 'sa', '4,000', 'ka', 'mga', 'pamilya', 'nga', 'namakwit', 'gumikan', 'sa', 'bagyong', 'Urduja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
5,670 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', '“Urduja”', 'gipaabot', 'mogawas', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', 'karong', 'Miyerkules', 'apan', 'nagbilin', 'kini', 'og', 'kadaot', 'sa', 'amihanang', 'Sugbo', 'ug', 'ubang', 'bahin', 'sa', 'Kabisay-an', 'labi', 'na', 'sa', 'lalawigan', 'sa', 'Biliran', 'diin', 'dul-an', 'sa', '20', 'ka', 'tawo', 'ang', 'nangamatay', 'sa', 'landslide', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,671 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'Urduja', ',', 'laing', 'tropical', 'depression', 'ang', 'naa', 'gawas', 'sa', 'PAR', 'nga', 'base', 'sa', 'data', 'sa', 'Pagasa', 'anaa', 'sa', 'gilay-on', 'nga', '1,950', 'ka', 'kilometros', 'sa', 'silangan', 'sa', 'Mindanao', 'nga', 'nagdala', 'og', 'hangin', 'nga', 'may', 'gikusgon', 'nga', '40', 'kilometros', 'duol', 'sa', 'iyang', 'sentro', 'ug', 'pag-unos', 'nga', 'moabot', 'sa', '50', 'kilometros', 'matag', 'takna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,672 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'nakapabor', 'sa', 'CeMFed', ',', 'matod', 'niya', ',', 'gilangkoban', 'kini', 'sagad', 'og', 'mga', 'mayor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,673 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tumong', 'sa', 'pederalismo', 'nga', 'matubag', 'ang', 'mga', 'suliran', 'sa', 'kalisod', 'ug', 'underdevelopment', 'sa', 'mga', 'rehiyon', ',', 'masulbad', 'ang', 'dugay', 'nang', 'problema', 'sa', 'armado', 'ug', 'rebeldeng', 'mga', 'grupo', 'sa', 'Mindanao', ',', 'ug', 'kurapsyon', 'ug', 'kahiwian', 'sa', 'gobiyerno', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,674 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'nagmalampuson', 'nga', 'fire', 'truck', 'christmas', 'decoration', 'niadtong', 'nakalabay', 'nga', 'tuig', ',', 'laing', 'panagsangka', 'ang', 'mahitabo', 'karong', 'Sabado', ',', 'Disyembre', '23', 'diin', '13', 'ka', 'mga', 'dakbayan', 'ug', 'lungsod', 'ang', 'mosalmot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,675 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Sabado', ',', 'dihang', 'gipangulohan', 'ni', 'Bureau', 'of', 'Fire', 'Protection', '(', 'BFP', ')', '7', 'Regional', 'Director', 'SSupt.', 'Samuel', 'Tadeo', 'ang', 'paglusad', 'sa', 'panagsangka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0] | cebuaner |
5,676 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Tadeo', 'nga', 'sa', 'Central', 'Visayas', 'lang', 'adunay', 'ingon', 'niini', 'nga', 'panagsangka', 'sa', 'mga', 'maanindot', 'nga', 'dekorasyon', 'sa', 'fire', 'trucks', 'alang', 'sa', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,677 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Sabado', ',', 'adunay', 'parade', 'nga', 'pagahimuon', 'diin', 'tanang', 'mga', 'fire', 'truck', 'sa', 'estasyon', 'nga', 'niapil', 'moparada', 'kini', 'sa', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,678 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dunganan', 'kini', 'nila', 'sa', 'paghatag', 'og', 'pamphlets', 'nga', 'aduna', 'kini', 'safety', 'tips', 'alang', 'sa', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,679 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'kompetisyon', ',', 'pagpahibawo', 'usab', 'ang', 'tumong', 'sa', 'BFP', '7', 'sa', 'publiko', 'nga', 'di', 'kini', 'magpasagad', 'ug', 'mag-amping', 'karong', 'holiday', 'season', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,680 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Cebu', 'City', 'adunay', 'P186.34', 'milyones', 'nga', 'bayranan', 'gikan', 'niadtong', '2014', 'hangtod', 'first', 'semester', 'sa', 'tuig', '2017', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,681 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'korte', 'nipabor', 'nga', 'bayran', 'una', 'sa', 'siyudad', 'ang', 'P56', 'milyones', 'sa', 'school', 'year', '2014-2015', 'ug', 'asikasuhon', 'ang', 'nagsunod', 'nga', 'mga', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,682 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nalangan', 'ang', 'pagbayad', 'sa', 'City', 'Hall', 'sa', 'tag', 'P10,000', 'nga', 'tuition', 'fee', 'sa', 'city', 'scholars', 'nga', 'nitungha', 'sa', 'ACT', 'gumikan', 'sa', 'kanhi', 'kaso', 'nga', 'giatubang', 'ni', 'Kongresista', 'Rodrigo', '“Bebot”', 'Abellanosa', ',', 'ang', 'tag-iya', 'sa', 'maong', 'tunghaan', 'nga', 'gihukman', 'nga', 'guilty', 'sa', 'Ombudsman', ',', 'apan', 'ang', 'Sandiganbayan', 'nibasura', 'sa', 'kasong', 'graft', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0] | cebuaner |
5,683 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Young', 'nibutyag', 'subay', 'sa', 'pasalig', 'sa', 'tagdumala', 'sa', 'ACT', 'nga', 'kon', 'mahuman', 'na', 'unya', 'og', 'submit', 'ang', 'mga', 'city', 'scholar', 'sa', 'ilang', 'clearance', ',', 'makuha', 'na', 'unya', 'nila', 'ang', 'ilang', 'credentials', 'sulod', 'lang', 'sa', 'usa', 'ka', 'semana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,684 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'credentials', 'unang', 'gipugngan', '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, 0, 0, 0, 0, 0] | cebuaner |
5,685 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibawo', 'usab', 'sa', 'kanhi', 'bise', 'mayor', 'nga', 'maoy', 'chairman', 'sa', 'committee', 'on', 'education', 'nga', 'nagtagbo', 'na', 'usab', 'sila', 'uban', 'sa', 'mga', 'opisyal', 'sa', 'City', 'Accountant', ',', 'City', 'Treasurer', 'ug', 'Legal', 'Office', 'aron', 'sa', 'pag-asikaso', 'sa', 'P56', 'milyones', 'aron', 'ibayad', 'sa', 'ACT', 'nga', 'posibleng', 'ma-release', 'karong', 'Enero', 'sunod', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,686 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Young', 'nga', 'asikasuhon', 'usab', 'nila', 'ang', 'mga', 'memorandum', 'sa', 'kapin', '20', 'ka', 'mga', 'eskwelahan', 'lakip', 'na', 'ang', 'ACT', 'aron', 'mahimoan', 'og', 'memorandum', 'sanglit', 'gikan', '2015', 'hangtod', '2017', ',', 'wala', 'kini', 'memorandum', 'sa', 'siyudad', 'diin', 'ang', 'ilang', 'kontrata', 'ipaubos', 'sa', 'review', 'matag', 'tulo', 'ka', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 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] | cebuaner |
5,687 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaubos', 'na', 'sa', 'state', 'of', 'calamity', 'ang', 'dakbayan', 'sa', 'Ormoc', 'gumikan', 'sa', 'dakong', 'kadaut', 'nga', 'namugna', 'sa', 'bagyong', 'Urduja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
5,688 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tuyo', 'sa', 'pagdeklara', 'aron', 'mapagawas', 'ang', 'calamity', 'fund', 'sa', 'usa', 'ka', 'lokal', 'nga', 'kagamhanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,689 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'City', 'Councilor', 'Rolando', 'Villasencio', 'nga', 'giseguro', 'nila', 'nga', 'nasunod', 'ang', 'criteria', 'sa', 'pagdeklarar', 'og', 'state', 'of', 'calamity', 'pinaagi', 'sa', 'usa', 'ka', 'resolusyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0] | cebuaner |
5,690 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'nga', '40', 'porsiyento', 'sa', 'populasyon', 'sa', 'dakbayan', 'ang', 'naapektuhan', 'sa', 'bagyo', 'ug', 'may', 'halapad', 'nga', 'kadaot', 'sa', 'agriculture', 'crops', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,691 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'maoy', 'nahimong', 'basehan', 'sa', 'konseho', 'sa', 'pagdeklarar', 'og', 'state', 'of', 'calamity', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,692 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gahapon', 'sa', 'alas', '8', 'sa', 'buntag', ',', 'mianam-anam', 'na', 'paghubas', 'ang', 'tubig', 'baha', 'ug', 'nasud', 'na', 'sa', 'mga', 'rescuer', 'ang', 'mga', 'lugar', 'nga', 'wa', 'nila', 'masud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,693 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Anaa', 'sa', 'tandugon', 'nga', 'kahimtang', 'ang', 'usa', 'ka', '8-anyos', 'nga', 'batang', 'lalake', 'human', 'madasmagi', 'og', 'motorsiklo', 'niadtong', 'Biyernes', 'didto', 'sa', 'Barangay', 'Calanggaman', ',', 'lungsod', 'sa', 'Ubay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 5, 0] | cebuaner |
5,694 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'ang', 'biktima', 'nga', 'si', 'Mark', 'Dave', 'Alaba', ',', '8', ',', 'ug', 'residente', 'sa', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,695 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'drayber', 'sa', 'motorsiklo', 'giila', 'nga', 'si', 'Julius', 'Castino', ',', '20', ',', 'ulitawo', 'ug', 'molupyo', 'sa', 'Barangay', 'Bulilis', 'sa', 'samang', 'lungsod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0] | cebuaner |
5,696 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakisusi', 'sa', 'kapulisan', ',', 'nasayran', 'nga', 'padung', 'ang', 'motorsiklo', 'nga', 'gimaneho', 'ni', 'Castino', 'sa', 'Poblacion', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 5, 0] | cebuaner |
5,697 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pag-abot', 'sa', 'dapit', 'nahitaboan', ',', 'kalit', 'lang', 'nga', 'nitabok', 'si', 'Alaba', 'sa', 'pikas', 'karsada', 'kay', 'niapas', 'kini', 'sa', 'iyang', 'inahan', 'hinungdan', 'nga', 'siya', 'nadasmagan', 'sa', 'motorsiklo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,698 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niabot', 'na', 'sa', '4,126', 'ka', 'mga', 'pamilya', 'ang', 'gipabakwit', 'sa', 'kagamhanan', 'sa', 'lungsod', 'sa', 'Daanbantayan', 'gikan', 'sa', '20', 'ka', 'mga', 'barangay', 'tungod', 'sa', 'bagyong', 'Urduja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
5,699 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghan', 'usab', 'sa', 'mga', 'dapit', 'sa', 'amihanan', 'ang', 'naigo', 'sa', 'bronwout', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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