Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,400 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihangyo', 'ni', 'Fr.', 'Aladdin', 'Luzon', ',', 'director', 'sa', 'safety', ',', 'security', ',', 'peace', 'and', 'order', ',', 'ang', 'mga', 'tawo', 'sa', 'dili', 'pagdala', 'og', 'mga', 'backpack', 'aron', 'dili', 'malangan', 'ang', 'pagsulod', 'sanglit', 'estrikto', 'ang', 'security', 'ug', 'gidili', 'usab', 'ang', 'balloon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,401 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Adunay', 'tulo', 'ka', 'mga', 'entrance', 'nga', 'nahimutang', 'sa', 'D.', 'Jakosalem', ',', 'Plaza', 'Sugbo', 'ug', 'P.', 'Burgos', 'ug', 'ang', 'ubang', 'mga', 'pultahan', 'gitagana', 'para', 'sa', 'exits', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,402 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mobutang', 'usab', 'og', 'labing', 'minos', 'pito', 'ka', 'LED', 'screen', 'ang', 'Basilica', 'palibot', 'sa', 'simbahan', 'para', 'sa', 'mga', 'tawo', 'nga', 'dili', 'na', 'makasulod', 'gumikan', 'sa', 'kadaghan', 'sa', 'motambong', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,403 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Walk', 'with', 'Mary', 'gikan', 'sa', 'Barangay', 'Guadalupe', 'paingon', 'sa', 'Fuente', 'Osmeña', 'dayon', 'sa', 'paingon', 'sa', 'Basilica', ',', 'iparada', 'ang', 'mga', 'imahen', 'ngadto', 'sa', 'Saint', 'Joseph', 'National', 'Shrine', 'nga', 'gikatakdang', 'moabot', 'alas', '9', 'sa', 'buntag', 'nga', 'pagasundan', 'og', 'misa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 6, 0, 0, 5, 6, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,404 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Magsugod', 'ang', 'send-off', 'mas', 'Enero', '20', 'alas', '2', 'sa', 'kadlawon', 'ug', 'inig', 'ka', 'alas', '3', 'sugdan', 'ang', 'Traslacion', 'paingon', 'ngadto', 'sa', 'Virgen', 'de', 'Regla', 'National', 'Shrine', 'sa', 'Lapu-Lapu', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 5, 6, 0] | cebuaner |
5,405 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alas', '4', 'sa', 'kadlawon', 'moabot', 'ang', 'mga', 'imahen', 'didto', 'sa', 'Birhen', 'sa', 'Regla', 'ug', 'ipahigayon', 'ang', 'welcome', 'mass', 'dayon', 'mag-motorcade', 'paingon', 'ngadto', 'sa', 'dockyard', 'sa', 'Naval', 'Forces', 'Central', 'sa', 'barangay', 'Canjulao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 5, 0] | cebuaner |
5,406 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'naandan', 'ang', 'Galleon', 'sa', 'mga', 'Ouano', ',', 'niining', 'puntoha', 'ang', 'gamiton', 'mao', 'ang', 'barko', 'sa', 'Navy', 'kansang', 'fluvial', 'procession', 'sugdan', 'alas', '6', 'sa', 'buntag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,407 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mosubay', 'gihapon', 'ang', 'mga', 'barko', 'sa', 'karaang', 'rota', 'ug', 'motuyok', 'sa', 'Mactan', 'Channel', 'sa', 'dili', 'pa', 'molabang', 'paingon', 'sa', 'Pier', 'Uno', 'sa', 'alas', '8', 'sa', 'buntag', 'diin', 'ipahigayon', 'ang', 'mubo', 'nga', 'foot', 'procession', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,408 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Galleon', 'nga', 'sakyanan', 'ni', 'Sto.', 'Niño', 'adunay', '150', 'ka', 'mga', 'tawo', 'nga', 'gitugotang', 'makasakay', ',', '50', 'gikan', 'sa', 'Navy', 'ug', '100', 'gikan', 'sa', 'Basilica', 'diin', 'gidili', 'ang', 'pagpalupad', 'og', 'mga', 'balloon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,409 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'procession', 'inig', 'ka', 'hapon', ',', 'giawhag', 'sa', 'mga', 'pareng', 'Agustino', 'nga', 'ang', 'tanan', 'magdala', 'og', 'rosario', 'ug', 'mag-ampo', 'ug', 'dili', 'mag-estorya', 'sa', 'ilang', 'mga', 'kuyo', 'sanglit', 'gituyo', 'ang', 'maong', 'kalihukan', 'para', 'maghinusol', 'sa', 'mga', 'sala', 'ug', 'magpasalamat', 'sa', 'mga', 'grasya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,410 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'na', 'tugotan', 'ni', 'Mayor', 'Tomas', 'Osmeña', 'nga', 'makaabli', 'pa', 'og', 'balik', 'ang', 'Vic', 'Enterprises', 'nga', 'nahimutang', 'sa', 'barangay', 'Mabolo', 'human', 'niya', 'gipasirado', 'ang', 'ilang', 'tindahan', 'ug', 'bodega', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 4, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,411 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', ',', 'ang', 'personnel', 'sa', 'City', 'Hall', 'nipasirado', 'sa', 'ilang', 'tindahan', 'ug', '12', 'ka', 'bodega', 'nga', 'maoy', 'gipundohan', 'sa', 'ilang', 'construction', 'materials', 'gumikan', 'sa', 'kakuwang', 'sa', 'ilang', 'business', 'permit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,412 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'nibutyag', 'nga', 'makabalik', 'lang', 'ang', 'operasyon', 'sa', 'maong', 'hardware', 'kon', 'siya', 'pusilon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
5,413 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mayor', 'nagkanayon', 'nga', 'dili', 'siya', 'maoy', 'angay', 'basulon', 'sa', 'mga', 'gatusan', 'ka', 'mga', 'kawani', 'sa', 'Vic', 'Enterprises', 'sanglit', 'iyang', 'gipadayag', 'igo', 'ra', 'siyang', 'nagpatuman', 'sa', 'balaod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,414 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '12', 'ka', 'warehouse', 'sa', 'Vic', 'Enterprises', 'gihulagway', 'ni', 'Osmeña', 'nga', 'walay', 'permit', 'ug', 'bisan', 'pending', 'permit', 'application', 'wala', 'siyay', 'nakita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,415 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', 'nga', 'dili', 'angay', 'mabalaka', 'ang', 'mga', 'kawani', 'sanglit', 'adunay', 'daghang', 'kwarta', 'ang', 'ilang', 'amo', 'ug', 'mahimo', 'silang', 'bayaran', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,416 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitug-an', 'ni', 'Osmeña', 'nga', 'mokita', 'kini', 'og', 'P1', 'bilyon', 'matag', 'buwan', 'ug', 'nakapalit', 'kini', 'og', 'P600', 'milyones', 'nga', 'propriyedad', 'sa', 'Mandaue', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,417 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'sa', 'kadagkoan', 'sa', 'Grab', 'nga', 'nisunod', 'sila', 'sa', 'balaod', 'sa', 'ilang', 'serbisyo', 'sa', 'Grab', 'Express', 'diin', 'pag-deliver', 'og', 'parcel', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,418 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'sukwahi', 'sa', 'taho', 'sa', 'gihimo', 'sa', 'mga', 'biker', 'sa', 'kakompetinsya', 'niini', 'nga', 'Angkas', 'Padala', 'nga', 'susama', 'lang', 'og', 'habalhabal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,419 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niini', ',', 'gi-monitor', 'ang', 'Angkas', 'Padala', 'biker', 'sa', 'Land', 'Transportation', 'Franchising', 'and', 'Regulatory', 'Board', '(', 'LTFRB', ')', '7', 'tungod', 'kay', 'illegal', 'kini', 'ilang', 'gihimo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,420 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Leo', 'Gonzales', ',', 'pangulo', 'sa', 'public', 'affairs', 'sa', 'Grab', ',', 'nga', 'alang', 'lang', 'gyud', 'sa', 'packages', ',', 'documents', 'o', 'goods', 'ang', 'Grab', 'Express', 'nga', 'gilusad', 'sa', 'Sugbo', 'niadtong', 'Oktubre', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
5,421 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niini', 'nga', 'courier', 'service', 'lang', 'ang', 'Grab', 'Express', 'ug', 'dili', 'kini', 'susama', 'sa', 'Grab', 'Bike', 'nga', 'gipahunong', 'karon', 'sa', 'gobyerno', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,422 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadto', 'lang', 'Martes', 'dihang', 'hingpit', 'nga', 'gilusad', 'sa', 'Sugbo', 'ang', 'Angkas', 'Padala', 'nga', 'padayon', 'karong', 'gi-monitor', 'sa', 'LTFRB', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
5,423 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahunong', 'karon', 'sa', 'maong', 'ahensiya', 'ang', 'Angkas', 'nga', 'habalhabal', 'type', 'tungod', 'kay', 'usa', 'kini', 'ka', 'kolurom', 'ug', 'wala’y', 'prangkisa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,424 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Angkas', ',', 'isip', 'pasaheroang', 'sakyanan', ',', 'gisuspenso', 'sa', 'LTFRB', 'human', 'supak', 'kini', 'sa', 'lagda', 'sa', 'balaud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,425 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'moubos', 'sa', 'P6', 'milyones', 'ang', 'igahin', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'sa', 'Talisay', 'sa', 'ila', 'unyang', 'pagsalmot', 'sa', 'Sinulog', '2018', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,426 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Budget', 'Officer', 'Edgar', 'Mabunay', 'nga', 'ang', 'labing', 'siguro', 'mosalmot', 'ang', 'dakbayan', 'sa', 'Talisay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 5, 0] | cebuaner |
5,427 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gibutyag', 'nga', 'dili', 'pa', 'siya', 'makatino', 'kon', 'pilay', 'igahin', 'sa', 'Talisay', 'tungod', 'kay', 'mag-agad', 'sila', 'sa', 'desisyon', 'ni', 'Mayor', 'Eduardo', 'Gullas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
5,428 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Mabunay', 'nga', 'kon', 'ang', 'budget', 'sa', 'miaging', 'tuiga', 'P6', 'milyones', 'karong', 'tuiga', 'basin', 'molabaw', 'pa', 'apan', 'dili', 'usab', 'moubos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,429 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'tungod', 'kay', 'mimahal', 'na', 'usab', 'ang', 'gastuhonon', 'ilabi', 'na', 'sa', 'mga', 'props', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,430 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Sinulog', '2018', 'ang', 'mosalmot', 'mao', 'gihapon', 'ang', 'Talisay', 'City', 'Central', 'School', 'nga', 'maoy', 'kanunay', 'nilang', 'contingent', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0] | cebuaner |
5,431 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mabunay', 'wala', 'na', 'mohatag', 'og', 'dugang', 'detalye', 'kon', 'unsa', 'ang', 'ilang', 'mga', 'concept', ',', 'apan', 'miingon', 'nga', 'ila', 'gihapong', 'paningkamotan', 'nga', 'modaug', 'gihapon', 'sila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
5,432 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Mabunay', 'nga', 'lahi', 'ang', 'Sinulog', 'sa', '2018', 'tungod', 'kay', 'sagol', 'na', 'ang', 'giontingent', 'sa', 'mga', 'dagko', 'ug', 'gagmay', 'lang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,433 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sabongero', 'gipatay', 'human', 'giingon', 'nga', 'nagpakatap', 'og', 'peke', 'nga', 'mga', 'kwarta', 'sa', 'tigbakayan', 'niadtong', 'Martes', 'sa', 'hapon', 'sa', 'Sitio', 'Abante', ',', 'Brgy.', 'General', 'Climaco', ',', 'dakbayan', 'sa', 'Toledo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
5,434 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'naangol', 'sab', 'ang', 'laing', 'sabongero', 'nga', 'si', 'James', 'Ronald', 'Malacaste', ',', '24', ',', 'nagpuyo', 'sa', '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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,435 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Eduyan', 'didto', 'sa', 'maong', 'lugar', 'kay', 'namusta', 'sa', 'tigbayan', 'sanglit', 'dunay', 'gisaulog', 'nga', 'fiesta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
5,436 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'giingong', 'nipakatap', 'kini', 'og', 'peke', 'nga', 'kwarta', 'hinungdan', 'nga', 'kalit', 'siyang', 'gipusil', 'tumong', 'sa', 'ulo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,437 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Malacaste', 'nga', 'iyang', 'tupad', 'naigo', 'sab', 'sa', 'tuong', 'dughan', 'human', 'milapos', 'ang', 'bala', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
5,438 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'pagpamusil', ',', 'paspas', 'nga', 'nakasibat', 'ang', 'gunman', 'samtang', 'si', 'Eduyan', 'dead', 'on', 'arrival', 'sa', 'tambalanan', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,439 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kapulisan', 'nakarekuber', 'sa', 'crime', 'scene', 'og', 'P300', 'gikan', 'kang', 'Eduyan', 'nga', 'tag', 'P20', 'ug', 'ang', 'duha', 'ka', 'pekeng', 'tag', 'P500', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,440 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'pa', 'niya', 'nga', 'ang', 'mga', 'residente', 'sa', 'lugar', 'nagpabiling', 'tak-om', 'kay', 'giingon', 'nga', 'wa', 'makaila', 'sa', 'gunman', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,441 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'sa', 'Disyembre', '22', 'ngadto', 'sa', '23', ',', 'masinati', 'sa', 'Sugbo', 'labi', 'na', 'sa', 'habagatang', 'bahin', 'sa', 'lalawigan', 'ang', 'hinay', 'ngadto', 'sa', 'kasarangan', 'nga', 'mga', 'pag-ulan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,442 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Disyembre', '24', 'hangtod', 'sa', 'adlaw', 'sa', 'Pasko', ',', 'masinati', 'na', 'ang', 'maayong', 'kahimtang', 'sa', 'panahon', ',', 'dugang', 'ni', 'Tabada', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0] | cebuaner |
5,443 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', ',', 'nakasud', 'na', 'ang', 'low', 'pressure', 'area', '(', 'LPA', ')', 'sa', 'PAR', 'ug', 'nahimong', 'tropical', 'depression', 'ug', 'ginganlan', 'kini', 'og', 'Vinta', ',', 'ang', 'ika', '22', 'nga', 'bagyo', 'sa', 'nasod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,444 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagkutlo', 'ning', 'balita', ',', 'si', 'Vinta', 'katapusan', 'nakit-an', 'nga', 'naa', 'sa', 'gilay-on', 'nga', '790', 'kilometros', 'sidlakan', 'sa', 'Hinatuan', ',', 'Surigao', 'del', 'Sur', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0] | cebuaner |
5,445 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagdala', 'kini', 'sa', 'hangin', 'nga', 'may', 'gikusgon', 'nga', '45', 'ka', 'kilometros', 'matag', 'takna', 'ug', 'pag-unos', 'nga', 'moabot', 'ngadto', 'sa', '60', 'ka', 'kilometros', 'matag', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,446 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Tabada', 'nag-irog', 'ang', 'bagyong', 'Vinta', 'sa', '20', 'ka', 'kilometros', 'matag', 'oras', 'sa', 'westward', 'nga', 'direksyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,447 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'pagbanabana', 'sa', 'Pagasa', ',', 'si', 'Vinta', 'gipaabot', 'motugpa', 'sa', 'yuta', 'sa', 'Davao', 'Oriental', 'o', 'Surigao', 'del', 'Sur', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 0, 0, 7, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 6, 0] | cebuaner |
5,448 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Martes', 'lang', 'sa', 'udto', 'dihang', 'hingpit', 'na', 'nga', 'nagsugod', 'ang', 'Angkas', 'Padala', 'nga', 'usa', 'na', 'ka', 'serbisyo', 'sa', 'Angkas', 'nga', 'modala', 'og', 'package', '/', 'parcel', 'pinaagi', 'sa', 'pag-book', 'online', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 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] | cebuaner |
5,449 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kining', 'pamaagi', 'usa', 'ka', 'Transport', 'Network', 'Vehicle', 'Service', '(', 'TNVS', ')', 'ubos', 'sa', 'Transportation', 'Network', 'Company', 'niini', 'nga', 'Angkas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 3, 0] | cebuaner |
5,450 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihunong', 'sa', 'Angkas', 'ang', 'serbisyo', 'niini', 'nga', 'susama', 'sa', 'habal-habal', 'tungod', 'kay', 'giisip', 'kini', 'nga', 'illegal', 'sa', 'LTFRB', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0] | cebuaner |
5,451 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'LTFRB', '7', 'regional', 'director', 'Ahmed', 'Cuizon', 'nibutyag', 'sa', 'Superbalita', 'Cebu', 'kagahapon', 'nga', 'gimonitor', 'na', 'kini', 'nila', 'atol', 'sa', 'pagsugod', 'sa', 'maong', 'serbisyo', '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, 3, 4, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,452 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Martes', 'dihang', 'gisuwayan', 'ning', 'mantalaan', 'nga', 'mosakay', 'sa', 'usa', 'sa', 'Angkas', 'Padala', 'biker', 'ug', 'nakasakay', '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] | cebuaner |
5,453 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'nipahibawo', 'ang', 'driver', 'daan', 'nga', 'di', 'kini', 'pwede', 'base', 'na', 'usab', 'sa', 'ilang', 'pamaagi', 'sa', 'Angkas', 'Padala', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,454 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karon', 'naghuwat', 'pa', 'sila', 'sa', 'proposal', 'sa', 'Angkas', 'alang', 'sa', 'Siyudad', 'ug', 'kon', 'unsaon', 'nila', 'nga', 'mamahimong', 'makabalik', 'sa', 'syudad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,455 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hingpit', 'na', 'nga', 'sirhan', 'sa', 'Kagamhanan', 'sa', 'dakbayan', 'sa', 'Sugbo', 'ang', '12', 'ka', 'mga', 'bodega', 'lakip', 'na', 'ang', 'tindahan', 'sa', 'Vic', 'Enterprises', 'karong', 'adlawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,456 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'kon', 'aduna’y', 'business', 'permit', 'ang', 'Vic', 'Enterprises', ',', 'apan', 'nakita', 'nga', 'aduna', 'siyay', 'kalapasan', 'sa', 'regulatory', 'requirements', 'hinungdan', 'nga', 'bawian', 'kini', 'og', 'permit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,457 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakita', 'nga', 'nakalapas', 'ang', 'ilang', 'establisemento', 'sa', 'Barangay', 'Mabolo', 'sa', 'nagkadaiyang', 'traffic', 'regulations', 'lakip', 'na', 'usab', 'ang', 'environmental', 'regulations', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,458 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihatagan', 'ra', 'usab', 'sila', 'og', '24', 'oras', 'nga', 'motuman', 'sa', 'maong', 'closure', 'order', 'sukad', 'sa', 'pagtunol', 'niini', 'sa', 'maong', 'tindahan', 'kagahapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,459 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagdumili', 'una', 'pagahatag', 'sa', 'ilang', 'pamahayag', 'ang', 'Vic', 'Enterprises', 'dihang', 'gitawagan', 'kini', 'sa', 'SunStar', '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, 3, 4, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
5,460 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ikawalo', 'na', 'diay', 'kini', 'nga', 'higayon', 'nga', 'nadugay', 'ang', 'sweldo', 'sa', 'mga', 'kawani', 'sa', 'barangay', 'tungod', 'kay', 'dugay', 'ang', 'pagsumiter', 'sa', 'Certificate', 'of', 'Punog', 'Barangay', 'nga', 'usa', 'sa', 'rekisito', 'aron', 'mapagawasan', 'og', 'sweldo', 'sukad', '2015', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,461 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Poblacion', 'Pardo', 'Konsehal', 'Litang', 'Abarquez', 'nga', 'di', 'mosugot', 'ang', 'konseho', 'nga', 'motudlo', 'og', 'laing', 'treasurer', 'kon', 'di', 'masulbad', 'ang', 'problema', 'nga', 'gibilin', 'ni', 'Idul', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,462 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', 'nga', 'mao', 'usab', 'kini', 'ang', 'tambag', 'ni', 'State', 'Auditor', 'Daisy', 'Bercede', 'kanila', 'atol', 'sa', 'pagbisita', 'nila', 'sa', 'COA', 'niadtong', 'Disyembre', '4', 'aron', 'mangayo', 'sa', 'detalyado', 'nga', 'report', 'sa', 'unsettled', 'ug', 'undeposited', 'amount', 'sa', 'barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,463 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingon', 'nga', 'niabot', 'sa', 'P172,190', 'ang', 'wa', 'madeposito', 'sukad', 'Enero', 'hangtod', 'Agusto', 'karong', 'tuiga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,464 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'masikop', 'si', 'Masangkay', 'apan', 'gipangsakmit', 'sa', 'kapulisan', 'ang', 'mga', 'LPG', 'tanks', 'nga', 'moabot', 'og', '253', 'ka', 'mga', 'tangke', ',', 'fuel', 'pumps', ',', 'cylinder', 'tanks', ',', 'tank', 'trailers', 'ug', 'ubang', 'mobile', 'containers', ',', 'mga', 'sakyanan', 'nga', 'gikargahan', 'sa', 'mga', 'LPG', 'tank', ',', ',', 'timbangan', 'ug', 'uban', 'pang', 'mga', 'kagamitan', 'sa', 'sulod', 'sa', 'refilling', 'station', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,465 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibana-bana', 'sa', 'CIDG-7', 'nga', 'mokabat', 'ngadto', 'sa', 'P15', 'million', 'ang', 'balor', 'sa', 'ilang', 'nasakmit', 'lakip', 'na', 'ang', 'mga', 'sakyanan', 'ug', 'liquefied', 'petroleum', 'gas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,466 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Sr.', 'Insp.', 'Edwin', 'Lacostales', 'nga', 'nakadawat', 'sila', 'og', 'report', 'gikan', 'sa', 'DOE', '7', 'ug', 'mihimo', 'dayon', 'sila', 'og', 'surveillance', 'ug', 'validation', 'ug', 'nasuta', 'nga', 'wa', 'silay', 'permit', 'nga', 'mamaligya', 'sa', 'LPG', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,467 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sila', 'ang', 'tig', 'supply', 'sa', 'mga', 'retailer', 'sa', 'butane', 'canister', 'nga', 'gipamaligya', 'sa', 'tibuok', 'Central', 'Visayas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,468 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'sa', 'LPG', 'station', 'si', 'Masangkay', 'ug', 'mga', 'pahinante', 'lang', 'ang', 'anaa', 'apan', 'bisan', 'pa', 'niini', ',', 'ang', 'CIDG7', 'ug', 'DOE-7', 'niimbargo', 'sa', 'mga', 'dagkong', 'tangke', 'nga', 'puno', 'sa', 'LPG', 'tanks', 'lakip', 'na', 'ang', 'mga', 'sakyanan', 'nga', 'gisakyan', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,469 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Lacostales', 'nga', 'kasagaran', 'sa', 'ilang', 'kustomer', 'manawag', 'lang', 'kon', 'pila', 'ang', 'paliton', 'niini', 'ug', 'kuhaon', 'ra', 'sa', 'maong', 'refilling', 'station', 'sa', 'barato', 'nga', 'presyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,470 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'mga', 'barangay', 'tanod', 'sa', 'Mambaling', 'nga', 'di', 'magpaila', 'nitug-an', 'sa', 'mga', 'tigbalita', 'nga', 'dugay', 'na', 'nga', 'nag', 'operate', 'ang', 'refilling', 'station', 'apan', 'wa', 'lang', 'nila', 'agda', 'sa', 'pagtuo', 'nga', 'legal', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,471 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'pahinante', 'nga', 'giabangan', 'nga', 'mo-deliver', 'sa', 'mga', 'LPG', 'tanks', 'nakurat', 'nga', 'dunay', 'mga', 'police', 'nga', 'ningbabag', 'kanila', 'nga', 'makagawas', 'sanglit', 'sa', 'ilang', 'nahibaw-an', 'legal', 'ang', 'negosyo', 'sa', 'ilang', 'amo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,472 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'human', 'nga', 'hangtod', 'karon', 'ang', 'kagamhanan', 'sa', 'Talisay', 'way', 'gitumbok', 'nga', 'designated', 'nga', 'fire', 'cracker', 'zone', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,473 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Villamater', 'miingon', 'nga', 'ning', 'pipila', 'nalang', 'ka', 'adlaw', 'sa', 'di', 'pa', 'ang', 'Pasko', 'ug', 'Bag-ong', 'Tuig', 'siya', 'nagtuo', 'nga', 'wa', 'nay', 'tugtan', 'ang', 'siyudad', 'nga', 'makabaligya', 'og', 'mga', 'pabuto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
5,474 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ning', 'upat', 'nalang', 'ka', 'adlaw', 'sa', 'di', 'pa', 'ang', 'Pasko', 'siya', 'nagtuo', 'nga', 'wa', 'nay', 'tugtan', 'nga', 'makabaligya', 'sanglit', 'wa', 'man', 'usay', 'nikuhag', 'business', 'permit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,475 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gumikan', 'niini', 'matod', 'ni', 'Villamater', 'nga', 'kutob', 'sa', 'mga', 'mamaligyag', 'pabuto', 'ila', 'kining', 'sakmiton', 'tungod', 'kay', 'nagpasabot', 'kini', 'nga', 'ilegal', 'ug', 'kuyaw', 'usab', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,476 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Villamater', 'nga', 'pabor', 'siya', 'nga', 'way', 'mga', 'pabuto', 'ang', 'ibaligya', 'sa', 'siyudad', 'sa', 'Talisay', 'tungod', 'kay', 'siya', 'mismo', 'di', 'usab', 'uyon', 'sa', 'mga', 'pabuto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,477 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'adunay', 'mga', 'pamaagi', 'nga', 'ihulip', 'niini', 'nga', 'mao', 'ang', 'pagsaba-saba', 'sama', 'nalang', 'sa', 'turotot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,478 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'Talisay', 'Fire', 'Department', 'nibutyag', 'usab', 'nga', 'uyon', 'silang', 'waa', 'gihapoy', 'esignated', 'nga', 'fire', 'cracker', 'zone', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,479 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'kon', 'pangayuan', 'silag', 'opinion', 'ilang', 'isugyot', 'nga', 'adto', 'sa', 'Talisay', 'Fish', 'Port', 'tungod', 'kay', 'layo', 'sa', 'mga', 'tawo', 'ug', 'pinuy-anan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,480 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kahinumdoman', 'ang', 'atubangan', 'nga', 'bahin', 'sa', 'Talisay', 'City', 'Hall', 'maoy', 'gitugtan', 'nga', 'baligyaanan', 'sa', 'milabayng', 'katuigan', ',', 'apan', 'ning', 'higayona', 'dii', 'na', 'mahimo', 'tungod', 'kay', 'gikoral', 'na', 'sa', 'tag-iya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,481 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisugdan', 'na', 'ang', 'unang', 'hugna', 'sa', 'pagpatukod', 'og', 'bag-ong', 'tambalanan', 'sa', 'dakbayan', 'sa', 'Bogo', 'sa', 'amihanang', '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, 5, 0, 0, 5, 0] | cebuaner |
5,482 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Catan', 'nga', 'ang', 'kinatibuk-ang', 'P230', 'milyones', 'ang', 'nadawat', 'sa', 'PHO', 'gikan', 'sa', 'Senado', 'alang', 'sa', 'pagpabarog', 'og', 'bag-ong', 'building', 'sa', 'provincial', 'hospital', 'sa', 'dakbayan', 'sa', 'Bogo', ',', 'Severo', 'Verallo', 'Hospital', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 3, 4, 4, 0] | cebuaner |
5,483 | 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', 'duna', 'pay', 'P30', 'milyones', 'nga', 'nahabilin', 'gikan', 'sa', 'Department', 'of', 'Health', 'ug', 'sa', 'sunod', 'tuig', 'ipadayon', 'ang', 'P130', 'milyones', 'nga', 'giapil', 'sa', 'General', 'Appropriations', 'Act', '(', 'GAA', ')', 'sa', '2018', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,484 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Catan', 'nidugang', 'kon', 'way', 'kausaban', 'sa', 'skedyul', ',', 'mahatag', 'ang', 'bag-ong', 'ospital', 'sa', 'di', 'pa', 'mahuman', 'ang', '2019', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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 |
5,485 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Uban', 'ni', 'Espino', 'ang', 'Regional', 'Chief', 'sa', 'Civil', 'Security', 'Unit', 'nga', 'si', 'P', '/', 'Supt.', 'Alan', 'Servida', 'aron', 'masuta', 'unsa', 'ka', 'luwas', 'ang', 'lugar', 'nga', 'gipanindahan', 'sa', 'mga', 'pabuto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,486 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nakamatikod', 'kini', 'nga', 'sa', 'mga', 'fireworks', 'nga', 'gi-', 'display', 'way', 'nakabutang', 'nga', 'Philippine', 'Standard', 'mark', 'sa', 'mga', 'produkto', 'nga', 'magpamatuod', 'nga', 'di', 'imported', 'nga', 'produkto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,487 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Espino', 'nga', 'wa', 'usab', 'mabutang', 'sa', 'label', 'sa', 'fireworks', 'kinsa', 'ang', 'naggama', 'sa', 'produkto', 'aron', 'maoy', 'manubag', 'kun', 'ugaling', 'mahitabo', 'ang', 'disgrasya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,488 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nabantayan', 'usab', 'nga', 'kadudahan', 'ang', 'mga', 'fireworks', 'nga', 'gipangplastar', 'sa', 'SRP', 'diin', 'dunay', 'mga', 'marka', 'nga', 'inintsik', 'sa', 'luyong', 'bahin', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,489 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakurat', 'usab', 'si', 'Espino', 'nga', 'dunay', 'mga', 'imported', 'nga', 'mga', 'pabuto', 'ang', 'gipang-display', 'nga', 'hugot', 'nga', 'gidili', 'sa', 'balaod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,490 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasakmit', 'dayon', 'kini', 'sa', 'hepe', 'sa', 'PRO', '7', 'ang', 'mga', 'ilegal', 'nga', 'pabuto', 'ug', 'gihatagan', 'lang', 'una', 'og', 'warning', 'sa', 'pagkakaron', 'ug', 'dii', 'pasakahan', 'og', '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, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,491 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mimando', 'dayon', 'si', 'Espino', 'nga', 'pasirad-an', 'lang', 'una', 'ang', 'mga', 'tindahan', 'sa', 'pabuto', 'samtang', 'di', 'pa', 'sila', 'makapakita', 'og', 'ebidensya', 'nga', 'gigama', 'lang', 'dinhi', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,492 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'produkto', 'nga', 'fireworks', 'display', 'nga', 'matod', 'pa', 'hinimo', 'sa', 'Bulacan', 'wa', 'mabutangi', 'kun', 'unsa', 'nga', 'kompanya', 'ang', 'nag-manufacture', 'nga', 'usa', 'sa', 'mga', 'rekisitos', 'sa', 'balaod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,493 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'agri-eco', 'park', 'pormal', 'na', 'nga', 'gilusad', 'ug', 'giablihan', 'sa', 'publiko', 'kagahapon', 'sa', 'hapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,494 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Rolando', 'Tero', ',', 'city', 'agriculturist', ',', 'nga', 'gibanabana', 'nga', 'anaa', 'sa', 'P3', 'milyones', 'ang', 'gigahin', 'sa', 'iyang', 'buhatan', 'aron', 'mausab', 'ang', '3,630', 'square', 'meters', 'nga', 'luna', 'nga', 'nabakanti', 'niadto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,495 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Tero', 'nga', 'sa', 'maong', 'dapit', 'bansayon', 'matag', 'semana', 'ang', 'mga', 'mag-uuma', 'gikan', 'sa', '27', 'ka', 'barangay', 'sa', 'Mandaue', 'matag', 'semana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
5,496 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hingpit', 'nga', 'nakuha', 'ang', 'karaang', 'bomba', 'pagka', 'alas', '6', 'na', 'sa', 'buntag', 'ug', 'ila', 'dayon', 'nga', 'gibyahe', 'paingon', 'sa', 'siyudad', 'sa', 'Toledo', 'aron', 'unta', 'didto', 'pabuthon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0] | cebuaner |
5,497 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'alas', '8:30', 'sa', 'buntag', 'luwas', 'nga', 'naabot', 'ang', 'mga', 'awtoridad', 'sa', 'lugar', 'nga', 'didto', 'pabuthon', 'ang', 'bomba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,498 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'usab', 'ka', 'team', 'sa', 'SWAT', 'sa', 'siyudad', 'sa', 'Sugbo', 'ang', 'mikuyog', 'sa', 'mga', 'sakop', 'sa', 'Central', 'Command', 'aron', 'mo-escort', 'sa', 'gikargahan', 'sa', 'karaang', 'bomba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,499 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'mga', 'gimando', 'sa', 'DENR', 'mao', 'ang', 'pagpahibawo', 'sa', 'mga', 'lumolopyo', 'duol', 'sa', 'Carmen', 'Copper', 'nga', 'mo-dispose', 'sila', 'sa', 'karaang', 'bomba', 'aron', 'di', 'malisang', 'ang', 'mga', 'tawo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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