Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,900 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihulagway', 'ni', 'Cabucos', 'nga', 'dako', 'og', 'ikatabang', 'sa', 'mga', 'pasahero', 'sanglit', 'mahibaw-an', 'nila', 'ang', 'taxi', 'driver', 'ug', 'kon', 'aduna', 'silay', 'mga', 'butang', 'nga', 'mahabilin', 'dali', 'ra', 'usab', 'ang', 'pag-ila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,901 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'sa', 'presidente', 'sa', 'MCTOA', 'nga', 'maserbisyohan', 'na', 'usab', 'nila', 'ang', 'mga', 'tawo', 'nga', 'nagpuyo', 'sa', 'subdivisions', 'sanglit', 'mo-book', 'ra', 'sila', 'pinaagi', 'sa', 'MICAB', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 3, 0] | cebuaner |
6,902 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', '60-anyos', 'nga', 'ginang', 'nipasangil', 'nga', 'siya', 'gidagit', 'sa', 'tulo', 'ka', 'mga', 'tawo', 'ug', 'gidala', 'ang', 'iyang', 'sakyanan', 'ug', 'mga', 'alahas', 'sa', 'wa', 'pa', 'siya', 'makaeskapo', 'niadtong', 'Lunes', 'sa', 'alas', '12:30', 'sa', 'udto', 'sa', 'basement', 'sa', 'mall', 'sa', 'Brgy.Talamban', ',', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 5, 0] | cebuaner |
6,903 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nakasakay', 'sa', 'Ceres', 'bus', 'unya', 'gigiyahan', 'sa', 'kondutor', 'nga', 'adto', 'siya', 'Moalboal', 'Police', 'Staion', 'mo-report', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0] | cebuaner |
6,904 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Seniedo', 'nibutyag', 'nga', 'niadtong', 'Lunes', 'sa', 'udto', 'human', 'sa', 'iyang', 'transaction', 'sa', 'mall', 'sa', 'Talamban', ',', 'nibalik', 'siya', 'sa', 'iyang', 'sakyanan', 'nga', 'naka-park', 'sa', 'basement', 'sa', 'usa', 'ka', 'mall', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,905 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'ka', 'mga', 'lalake', 'ningduol', 'kaniya', ',', 'nagdalag', 'armas', 'ug', 'nition', 'kaniya', 'dayong', 'mando', 'nga', 'di', 'siya', 'maglangas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,906 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gigagapos', 'ang', 'iyang', 'duha', 'ka', 'mga', 'kamot', 'ginamit', 'ang', 'plastic', 'cable', 'ingon', 'man', 'gi-blindfold', 'siya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,907 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidala', 'siya', 'sa', 'mga', 'kidnapper', 'luwan', 'sa', 'iyang', 'sakyanan', 'dayon', 'giingong', 'gituyoktuyok', 'siya', 'ug', 'dunay', 'babaye', 'nga', 'nisakay', 'kanila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,908 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Seniedo', 'nialigar', 'nga', 'gidala', 'siya', 'sa', 'kakahuyan', 'sa', 'Brgy.', 'Kanyuco', 'ug', 'gibantayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 0, 0, 0] | cebuaner |
6,909 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nihangyo', 'siya', 'nga', 'tangtangan', 'sa', 'hikot', 'ug', 'nisugot', 'ang', 'mga', 'kidnapper', 'ug', 'nibaod', 'kaniya', 'nga', 'di', 'modagan', 'o', 'mosibat', ',', 'apan', 'nakaeskapo', 'siya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,910 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'tigpamaba', 'sa', 'DOE', 'Visayas', 'Lourdes', 'Arciaga', 'nga', 'aduna’y', 'kasarangan', 'nga', '500', 'megawatts', 'ang', 'net', 'reserve', 'sa', 'kuryente', 'sa', 'tibuok', 'Visayas', 'grid', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0] | cebuaner |
6,911 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Di', 'usab', 'kaayo', 'makaapekto', 'ang', 'gipaabot', 'nga', 'weak', 'La', 'Niña', 'nga', 'mosulod', 'sunod', 'buwan', 'tungod', 'kay', 'gamay', 'ra', 'nga', 'porsyento', 'ang', 'maapektohan', 'niini', 'nga', 'source', 'of', 'power', 'nga', 'solar', 'power', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,912 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Arciaga', 'nga', 'anaa', 'lang', 'sa', '5.23', '%', 'ang', 'share', 'sa', 'solar', 'power', 'sa', 'kinatibuk-ang', 'power', 'supply', 'sa', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,913 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Visayas', 'grid', ',', 'aduna’y', 'average', 'nga', 'kapin', 'sa', '1,800MW', 'ang', 'demand', 'samtang', 'moabot', 'sa', 'kapin', '2,400MW', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,914 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'tuiga', ',', 'mas', 'nidaghan', 'ang', 'supply', 'sa', 'kuryente', 'gikan', 'sa', 'nagkadaiyang', 'power', 'plants', 'itandi', 'sa', 'nakalabay', 'nga', '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] | cebuaner |
6,915 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hingpit', 'na', 'usab', 'nga', 'naayo', 'ang', 'geothermal', 'power', 'plant', 'sa', 'Leyte', 'nga', 'nadaot', 'sa', 'linog', 'niadtong', 'Hulyo', ',', 'nga', 'nakaingon', 'sa', 'rotational', 'brownouts', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,916 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpabilin', 'nga', 'dako', 'og', 'natampo', 'ang', 'coal', 'power', 'sa', 'kinatibuk-ang', 'power', 'supply', 'sa', 'Visayas', 'diin', 'anaa', 'kini', 'sa', '45.78', '%', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,917 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisundan', 'kini', 'sa', 'Geothermal', 'Power', 'sa', '41.19', '%', ',', 'Solar', 'Power', 'sa', '5.23', '%', ',', 'Bio-mass', 'sa', '3.23', '%', ',', 'Diesel', 'sa', '2.14', '%', ',', 'Wind', 'sa', '1.96', '%', 'ug', 'Hydro', 'nga', 'naghatag', 'og', '0.48', '%', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,918 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Posibling', 'mataktak', 'sa', 'iyang', 'trabaho', 'kadtong', 'sakop', 'sa', 'City', 'of', 'Lapu-Lapu', 'Allied', 'Force', '(', 'CLAF', ')', 'nga', 'gihulagway', 'nga', 'arogante', 'ngadto', 'sa', 'iyang', 'gika-engkuwentro', 'nga', 'taxi', 'drayber', 'samtang', 'pasahero', 'mikuha', 'og', 'video', 'og', 'mikatap', 'sa', 'social', 'media', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,919 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Roa', 'sa', 'iyang', 'bahin', 'miangkon', 'nga', 'nadala', 'siya', 'sa', 'iyang', 'emosyon', 'niadtong', 'tungora', 'apan', 'nipasabot', 'sa', 'mga', 'tigbalita', 'atubangan', 'ni', 'Pajo', 'Barangay', 'Captain', 'Junard', 'Chan', 'nga', 'diriyot', 'siyang', 'madisgrasya', 'samtang', 'luwan', 'siya', 'sa', 'iyang', 'motorsiklo', 'sa', 'dihang', 'nagpasutoy', 'lang', 'og', 'padagan', 'ang', 'taxi', 'drayber', ',', 'hinungdan', 'nga', 'iya', 'kining', 'gigukod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,920 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giangkon', 'sa', '40', 'anyos', 'nga', 'kanhi', 'drug', 'surrenderer', 'nga', 'sayop', 'ang', 'iyang', 'gihimo', 'apan', 'siya', 'mihangyo', 'nga', 'sabton', 'siya', 'ilabi', 'na', 'nga', 'aduna', 'na', 'siyay', 'kahadlok', 'sa', 'aksidente', 'sa', 'kadalanan.', 'nga', 'maoy', 'gikamatyan', 'sa', 'iyang', 'ginikanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,921 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naatol', 'nga', 'angkas', 'diha', 'sa', 'iyang', 'motorsiklo', 'ang', 'iyang', 'manghod', 'babaye', 'nga', 'iyang', 'gikuha', 'niadtong', 'higayona', 'aron', 'nga', 'magdungan', 'og', 'pauli', 'sa', 'ilang', 'gipuy-an', 'sa', 'dihang', 'natapsingan', 'sila', 'sa', 'maong', 'taxi', 'unit', 'nga', 'milahos', 'sa', 'iyang', 'pagpadagan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,922 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nangayo', 'og', 'pasaylo', 'si', 'Roa', 'ngadto', 'ni', 'Kapitan', 'Chan', 'ug', 'andam', 'niyang', 'dawaton', 'kon', 'unsa', 'may', 'silot', 'nga', 'ipahamtang', 'kaniya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,923 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'sinkhole', 'ang', 'lungag', 'nga', 'nitumaw', 'sa', 'dan', 'Juana', 'Osmeña', ',', 'dakbayan', 'sa', 'Sugbo', '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, 1, 2, 0, 0, 0, 5, 0, 0] | cebuaner |
6,924 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'MGB', '7', 'Geologist', 'Dr.', 'Dennis', 'Gerald', 'Aleta', 'niingon', 'nga', 'ang', 'tubig', 'sa', 'naguba', 'nga', 'drainage', 'ang', 'nakaingon', 'nga', 'nihulpa', 'ang', 'maong', '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, 3, 4, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,925 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Marian', 'Codilla', ',', 'tigpamaba', 'sa', 'MGB', '7', ',', 'nga', 'naanod', 'ang', 'soil', 'materials', 'sa', 'maong', 'dapit', 'hinungdan', 'nga', 'naingon', 'ato', 'ang', 'kadako', 'ang', 'lungag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,926 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'nilang', 'pasabot', 'nga', 'dugay', 'na', 'usab', 'ang', 'maong', 'drainage', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,927 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Misugyot', 'si', 'Codilla', 'nga', 'kinahanglan', 'trabahuon', 'dayon', 'kini', 'karon', 'aron', 'dili', 'makamugna', 'og', 'aksidente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,928 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipusasan', 'ang', 'usa', 'ka', 'negosyante', 'human', 'kini', 'makuha', 'og', 'binulto', 'sa', 'gidiling', 'drugas', 'niadtong', 'Lunes', 'sa', 'alas', '12:40', 'sa', 'udto', 'sa', 'Sitio', 'Panabang', ',', 'Barangay', 'Apas', ',', 'dakbayan', 'sa', 'Sugbu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 5, 0] | cebuaner |
6,929 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'nga', 'taudtaud', 'nang', 'gimonitor', 'sa', 'iyang', 'kalikuhan', 'si', 'Rodriguez', 'sanglit', 'nalakip', 'kini', 'sa', 'listahan', 'sa', 'PDEA', 'human', 'pipila', 'ka', 'mga', 'residente', 'ang', 'mireklamo', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,930 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'usa', 'ka', 'putos', ',', 'nasakmit', 'usab', 'gikan', 'sa', 'iyang', 'possession', 'lima', 'ka', 'gramos', 'nga', 'gidiling', 'drugas', 'nga', 'mobalor', 'og', 'P25', 'mil', 'pesos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,931 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'suspetsado', 'mipadayag', 'nga', 'gisuyaan', 'lang', 'siya', 'sa', 'iyang', 'mga', 'silingan', 'tungod', 'kay', 'gawas', 'nga', 'aduna', 'siyay', 'barbershop', 'nakapalit', 'usab', 'siyag', 'mga', 'sakyanan', 'nga', 'ginegosyo', 'niini', 'sa', 'Grab', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 3, 0] | cebuaner |
6,932 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'usab', 'niya', 'nga', 'milambo', 'ang', 'iyang', 'negosyo', 'sa', 'lending', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,933 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'traffic', 'enforcers', 'sa', 'dakbayan', 'sa', 'Talisay', 'mipadayag', 'og', 'kahingawa', 'sa', 'ila', 'karong', 'siguridad', 'human', 'sa', 'istrikto', 'karon', 'nilang', 'pagpatuman', 'sa', 'lagda', 'sa', 'trapiko', 'nunot', 'sa', 'kamandoan', 'ni', 'retired', 'Gen.', 'Cecil', 'Ezra', 'Sandalo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 2, 0] | cebuaner |
6,934 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'niini', 'ang', 'mga', 'traffic', 'enforcers', 'gibati', 'og', 'kabalaka', 'tungod', 'sa', 'giingong', 'madawat', 'nila', 'nga', 'hulga', 'human', 'mipatuman', 'og', 'hugot', 'nga', 'lagda', 'sa', 'trapiko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,935 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gani', 'aduna', 'namay', 'mga', 'traffic', 'enforcers', 'kaniadto', 'ang', 'gipatay', 'ug', 'nadunggaban', 'sa', 'ilang', 'madakpan', 'tungod', 'sa', 'kalagot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,936 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ilang', 'gipadayag', 'nga', 'job', 'order', 'lang', 'ang', 'ilang', 'status', 'ug', 'wala’y', 'madawat', 'gikan', 'sa', 'gobiyerno', 'kon', 'magkinaunsa', 'ang', 'ilang', 'kinabuhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,937 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Sandalo', 'kinsa', 'maoy', 'consultant', 'sa', 'peace', 'and', 'order', 'ni', 'Mayor', 'Eduardo', 'Gullas', 'ug', 'gitahasan', 'sa', 'pagpatarong', 'og', 'padagan', 'sa', 'CT-TODA', 'aron', 'masulbad', 'sa', 'problema', 'sa', 'trapiko', 'diha', 'sa', 'Tabunok', 'miingon', 'nga', 'mao', 'gyud', 'kini', 'ang', 'risgo', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,938 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'gitataw', 'ni', 'Sandalo', 'nga', 'kon', 'nahadlok', 'man', 'gani', 'ang', 'pipila', 'ka', 'mga', 'sakop', 'sa', 'CT-TODA', ',', 'mahimo', 'nga', 'mangita', 'na', 'lang', 'silag', 'laing', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,939 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Atty.', 'Rudelyn', 'Navarro', ',', 'city', 'administrator', ',', 'niingon', 'nga', 'ila', 'nang', 'gitan-aw', 'ang', 'ka', 'kamahinungdanon', 'sa', 'CT-TODA', 'hinungdan', 'gusto', 'nilang', 'amendahon', 'ang', 'traffic', 'code', 'aron', 'ma', 'institutionalize', 'ang', 'CT-TODA', 'ingon', 'man', 'himuon', 'gyud', 'nga', 'departamento', 'aron', 'makadawat', 'silag', 'benepisyo', 'sama', 'sa', 'regular', 'nga', 'kawani', 'sa', 'City', 'Hall', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,940 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'maoy', 'tubag', 'sa', 'ilang', 'kabalaka', 'ug', 'kahingawa', 'sa', 'ilang', 'pagpatuman', 'sa', 'lagda', 'sa', 'trapiko.Mao', 'na', 'kini', 'ang', 'uwahing', 'adlaw', 'sa', 'rehistrasyon', 'ug', 'wala', 'nay', 'ihatag', 'nga', 'extention', 'ang', 'Comelec', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0] | cebuaner |
6,941 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Election', 'Officer', 'Ferdinand', 'Gujilde', 'mibutyag', 'nga', 'kutob', 'lang', 'sa', 'alas', '5', 'sa', 'hapon', 'sa', 'maong', 'adlaw', 'ang', 'rehistrasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,942 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', ',', 'layo', 'ang', 'posibilidad', 'nga', 'lugwayan', 'pa', 'ang', 'schedule', 'sa', 'continuing', 'registration', 'tungod', 'usab', 'sa', 'kagamay', 'lang', 'sa', 'nagpa-rehistro', 'sukad', 'gisugdan', 'kini', 'niadtong', 'Nobiyembre', '6', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,943 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'sa', 'Comelec', 'nga', 'dili', 'pa', 'hingpit', 'nga', 'rehistrado', 'ang', 'tanan', 'nga', 'nakaduso', 'sa', 'ilang', 'application', 'for', 'registration', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,944 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'ni', 'Gujilde', 'nga', 'moagi', 'pa', 'kini', 'sa', 'Election', 'Registration', 'Board', 'aron', 'pagsuta', 'kon', 'angayan', 'kining', 'aprubahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,945 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Barangay', 'Kapitan', 'Felicisimo', '“Imok”', 'Rupinta', 'sa', 'Ermita', ',', 'kinsa', 'gibanhigan-patay', 'sa', 'lungsod', 'sa', 'Liloan', ',', 'dili', 'kaalyado', 'sa', '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, 1, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
6,946 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'ni', 'Osmeña', 'nga', 'sulod', 'sa', 'kapin', '15', 'ka', 'tuig', 'nga', 'pagdumala', 'ni', 'Rupinta', 'sa', 'Ermita', ',', 'ubay-ubay', 'ang', 'kanhi', 'mga', 'pusher', 'sa', 'iyang', 'barangay', 'nga', 'nahimong', 'bantugan', 'nga', 'drug', 'lord', 'sa', 'Sugbo', 'ug', 'sa', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 0] | cebuaner |
6,947 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gipanghinganlan', 'silang', 'anhing', 'Crisostomo', 'Llaguno', 'alyas', '“Tata', 'Negro”', ',', 'Rowen', 'Secretaria', 'alyas', '“Yawa”', 'ug', 'si', 'Franz', 'Sabalones', 'kinsa', 'anaa', 'sa', 'kustodiya', 'sa', 'kapulisan', 'nga', 'gihulagway', 'ni', 'Osmeña', 'nga', 'diha', 'nagsugod', 'sa', 'ilang', 'ginadiling', 'kalihukan', 'sa', 'Ermita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 7, 8, 0, 1, 2, 0, 7, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,948 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gasaway', 'sa', 'mayor', 'nga', 'dili', 'una', 'ihatag', 'sa', 'Team', 'Rama', 'ang', 'P300,000', 'reward', 'ngadto', 'sa', 'tipster', 'kon', 'dili', 'kini', 'ma-establisar', 'nga', 'siya', 'maoy', 'mastermind', 'sa', 'krimen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,949 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'ang', 'haya', 'ni', 'Rupinta', 'gibalhin', 'na', 'kagahapon', 'sa', 'gym', 'sa', 'Ermita', 'ug', 'gisugat', 'kini', 'sa', 'mga', 'masulub-ong', 'lumoluyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,950 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'kanila', 'nanghilak', 'ug', 'ningsinggit', 'og', 'hustisya', 'sa', 'kamatayon', 'sa', 'ilang', 'kapitan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,951 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'haya', 'ni', 'Rupinta', 'abli', 'sulod', 'sa', '24', 'oras', 'hangto', 'sa', 'Disyembre', '9', ',', 'adlaw', 'sa', 'iyang', 'paglubong', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,952 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['POLICE', 'Regional', 'Office', '(', 'PRO', ')', ')', '7', 'padayon', 'nga', 'mibarog', 'nga', 'dili', 'fall', 'guy', 'ang', 'ilang', 'gisikop', 'nga', 'usa', 'sa', 'gunman', 'sa', 'pagbanhig', 'patay', 'kang', 'Felicisimo', '“Imok”', 'Rupinta', 'tungod', 'sa', 'lig-ong', 'ebidensya', 'nga', 'nagtumbok', 'kaniya', 'ug', 'ang', 'pagtudlo', 'sa', 'saksi', 'kang', 'Jimmy', 'Largo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,953 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'nang', 'nihimakak', 'si', 'Jimmy', 'Largo', 'nga', 'siya', 'ang', 'nagpatay', 'kang', 'Rupinta', 'tungod', 'kay', 'pinangga', 'siya', 'nga', 'sakop', 'niini', 'sa', 'Barangay', 'Ermita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
6,954 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gitug-an', 'sa', 'usa', 'ka', 'interview', 'sa', 'Superbalita', 'Cebu', 'nga', 'niadtong', 'tungora', 'nga', 'gibanhigan', 'si', 'Rupinta', 'naa', 'ra', 'siya', 'sa', 'Carbon', 'market', 'uban', 'sa', 'igsuon', 'sa', 'kapitan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 1, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,955 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'Sr.', 'Supt.', 'Jonathan', 'Cabal', ',', 'hepe', 'sa', 'Regional', 'Intelligence', 'Division', ',', 'nagkanayon', 'nga', 'lig-on', 'ang', 'ilang', 'ebidensya', 'pagtumbok', 'kang', 'Largo', 'nga', 'siya', 'gyud', 'ang', 'usa', 'sa', 'nagpusil', 'sa', 'biktima', 'ug', 'ang', 'positibong', 'pagtudlo', 'sa', 'saksi', 'kaniya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,956 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'sa', 'saksi', 'ang', 'motorsiklo', 'nga', 'nakuha', 'sa', 'panimay', 'ni', 'Largo', 'lakip', 'na', 'ang', 'gisul-ob', 'niini', 'nga', 'sapot', 'nga', 'nakuha', 'usab', 'sa', 'mga', 'sakop', 'sa', 'Regional', 'Special', 'Operations', 'Group', 'sa', 'gihimong', 'follow', 'up', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0] | cebuaner |
6,957 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Patay', 'ang', 'usa', 'ka', 'babaye', 'human', 'kini', 'gidul-it', 'sa', 'pagpusil', 'alas', '7:15', 'sa', 'gabii', 'sa', 'Looc', ',', 'Barangay', 'Poblacion', ',', 'lungsod', 'sa', 'Argao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
6,958 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'biktima', 'giila', 'nga', 'si', 'Vivian', 'Rosales', 'Herbosa', ',', '45', ',', 'ug', 'biyuda', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
6,959 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Herbosa', 'nagbilar', 'sa', 'haya', 'sa', 'iyaan', 'ug', 'nagduwa', 'og', 'madjong', 'dihang', 'kalit', 'nga', 'gipusil', 'sa', 'suspek', 'nga', 'si', 'Erwin', '“Sagoy”', 'Sabado', ',', '41.', 'ulitawo', 'ug', 'giilang', 'adik', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,960 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'matino', 'an', 'motibo', 'sa', 'pagpamusil', 'ug', 'nisibat', 'human', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,961 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', 'ang', 'tagal', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'sa', 'Talisay', 'niadtong', '38', 'ka', 'mga', 'pamilya', 'nga', 'nagpuyo', 'sa', 'merkado', 'sa', 'Lagtang', 'sukad', 'pa', 'sa', 'miaging', 'administrasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
6,962 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Plano', 'sa', 'dakbayan', 'nga', 'ang', 'maong', 'merkado', 'nga', 'wala', 'magamit', 'himuong', 'technical', 'vocational', 'school', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,963 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', ',', 'ang', 'mga', 'naapektohang', 'pamilya', 'hatagan', 'og', 'P10', 'mil', 'bugti', 'sa', 'ilan', 'pagbalhin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,964 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Atty.', 'Rudelyn', 'Navarro', ',', 'city', 'administrator', ',', 'niingon', 'nga', 'andam', 'na', 'ang', 'tanang', 'gikinahanglan', 'gikan', 'sa', 'kuwarta', 'ngadto', 'sa', 'mga', 'sakyanan', 'nga', 'ilang', 'sakyan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,965 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'ni', 'Navarro', 'nga', 'nasabotan', 'usab', 'nga', 'dili', 'na', 'lang', 'sila', 'patrabahuon', 'isip', 'mga', 'job', 'order', 'employee', 'hinuon', 'mahimo', 'silang', 'makapahimulos', 'o', 'mo-eskuyla', 'ang', 'ilang', 'mga', 'anak', 'nga', 'libre', 'sa', 'vocational', 'school', 'sa', 'maong', '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, 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] | cebuaner |
6,966 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasalamatan', 'ni', 'Navarro', 'ang', 'mga', 'nagpuyo', 'nga', 'nakasabot', 'ra', 'usab', 'sa', 'ilang', 'paghangyo', 'nga', 'gamiton', 'na', 'ang', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,967 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayuran', 'ang', 'maong', 'mga', 'pamilya', 'gikan', 'sa', 'pribadong', 'luna', 'sa', 'Barangay', 'Tanke', 'apan', ',', 'gigamit', 'na', 'sa', 'tag-iya', 'hinungdan', 'nga', 'nipuyo', 'sila', 'sa', 'merkado', 'sa', 'Lagtang', 'nga', 'wala', 'usab', 'pa', 'gamita', 'kaniadto', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,968 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipusil', 'ang', 'duha', 'ka', 'lalaki', 'nga', 'nag-inom', 'sa', 'wa', 'mailhing', 'tawo', 'kagahapon', 'sa', 'dakbayan', 'sa', 'Minglanilla', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,969 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'napusilan', 'naila', 'nga', 'sila', 'si', 'Carlito', 'Rodriguez', 'Cabanig', ',', '32', ',', 'residente', 'sa', 'dapit', 'ug', 'Michael', 'Geozon', '35', ',', 'taga', 'Ward', '3', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 5, 6, 0] | cebuaner |
6,970 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alas', '9:50', 'sa', 'gabii', 'sa', 'Ward', '4', 'Minglanilla', 'ang', 'duha', 'nag-inom', 'dihang', 'gipamusil', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0] | cebuaner |
6,971 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'si', 'Sandalo', 'maoy', 'consultant', 'sa', 'peace', 'and', 'order', 'apan', 'gawas', 'sa', 'isyu', 'sa', 'kapulisan', ',', 'si', 'Sandalo', 'gihatagan', 'usab', 'og', 'gahum', 'sa', 'pagsulbad', 'sa', 'situwasyon', 'sa', 'trapik', 'sa', 'Talsiay', 'ilabi', 'na', 'sa', 'Tabunok', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0] | cebuaner |
6,972 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Gullas', 'nga', 'si', 'Sandalo', 'adunay', 'igong', 'personalidad', 'nga', 'mopadagan', 'ug', 'mangulo', 'sa', 'CT-TODA', 'tungod', 'kay', 'kanhi', 'heneral', 'sa', 'kapulisan', 'ug', 'aduna', 'usay', 'command', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,973 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinungdan', 'nga', 'iya', 'kining', 'gihangyo', 'nga', 'tabangan', 'siya', 'sa', 'suliran', 'sa', 'trapiko', 'nga', 'maoy', 'isyu', 'karon', 'sa', 'iyang', 'administrasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,974 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Gullas', 'nga', 'dili', 'siya', 'manghilabot', 'ni', 'Sandalo', 'bisan', 'pa', 'sa', 'pagkuha', 'o', 'tagtaktak', 'ug', 'mga', 'traffic', 'enforcers', 'iya', 'kining', 'higatagan', 'og', 'kompletong', 'gahum', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,975 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'Sandalo', 'mitataw', 'nga', 'consultant', 'gihapon', 'siya', 'ug', 'dili', 'siya', 'ilhon', 'nga', 'bag-ong', 'pangulo', 'sa', 'CT-TODA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,976 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giduso', 'niya', 'niya', 'nga', 'kinsa', 'kadtong', 'iyang', 'mapili', 'ug', 'magpabilin', 'sa', 'puwesto', 'hangtod', 'nga', 'mahuman', 'ang', 'termino', 'sa', 'mayor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,977 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Labot', 'sa', 'suliran', 'sa', 'Tabunok', ',', 'si', 'Dela', 'Peña', 'nitumbok', 'sa', 'upat', 'ka', 'hinungdan', ':', 'gamay', 'ang', 'karsada', ',', 'nagkadaghan', 'ang', 'sakyanan', ',', 'mga', 'illegal', 'nga', 'manindahay', 'ug', 'pagpataka', 'og', 'parking', 'sa', 'mga', 'traysikol', 'ug', 'ubang', 'sakyanan', 'nga', 'ang', 'karsada', 'gihimong', 'terminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 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] | cebuaner |
6,978 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dungan', 'sa', 'pagbalhin', 'sa', 'husay', 'sa', 'mga', 'kasong', 'nga', 'iyang', 'gi-atubang', 'gikan', 'sa', 'Tagbilaran', 'City', 'RTC', 'paingon', 'sa', 'Cebu', 'City', 'RTC', ',', 'gimando', 'usab', 'ang', 'pagbalhin', 'sa', 'detention', 'ni', 'Boniel', 'paingon', 'sa', 'provincial', 'jail', 'dinhi', '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, 5, 6, 6, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,979 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daan', 'nia', 'a', 'Cebu', 'City', 'RTC', 'Branch', '57', 'ang', 'husay', 'sa', 'kasong', 'parricide', 'nga', 'iyang', 'gi-atubang', ',', 'subay', 'usab', 'sa', 'giingong', 'pagpatay', 'niya', 'sa', 'iyang', 'asawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,980 | "Inaasahan na ni Vice President Jejomar Binay na may mga taong... https://t.co/SDytgbWiLh." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,981 | "Mar Roxas TANG INA TUWID NA DAAN DAW .. EH SYA NGA DI STRAIGHT." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,982 | "Salamat sa walang sawang suporta ng mga taga makati! Ang Pagbabalik Binay In Makati #OnlyBinayInMakatiSanKaPa https://t.co/iwAOdtZPRE." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,983 | "@rapplerdotcom putangina mo binay TAKBO PA." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,984 | "Binay with selective amnesia, forgetting about the past six years he spent preparing to be president. #PiliPinasDebates2016." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,985 | "It doesn't matter whoever won between Duterte & Miriam as President. As long as they finally changed the country Noynoy failed to do so.." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,986 | "Nognog? Pero nognog din ang nag malasakit? Wtf? Tangina mo Binay nagpapaawa kapa! Hahahahaha #Nognog ??." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,987 | "#OnlyB1nay ?? #FB https://t.co/QEQnsK67Gm." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,988 | "What Abi Binay said on running for Makati mayor #Halalan2016 https://t.co/ayxM39JKNx https://t.co/rHhl4LfMaa." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,989 | "Srsly. How can Binay do away with no tax for those earning PhP 30k and below without any compromises? Nkklk talaga!." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,990 | "Sen Grace Poe, puro ka puso. Kaya lugmok bansa natin sa kahirapan at corruption. Puro puso. Walang utak.." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,991 | "RT @KarlNative: Sa laki ng ginastos ni Binay tapos sa laki din ng talo niya sa Mayo, siya pa din tameme sa ending ng kwento. Yun na! https:…." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,992 | "ENDO na para kay PNoy. No extension para sa kanyang Mar Roxas! #DuterteTillTheEnd." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,993 | "@xpeanutgalleryx Pet theory: Contrasted w/ PNoy for past 6 years, Binay hasn't failed much - then again he hasn't done much either.." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,994 | "RT @_nicklegaspi_: Sino ba si Binay?
Yuan: Nognog, Pandak, Laki sa Hirap.." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,995 | "BREAKING: VCM Inside Novotel Cubao owned by Mar Roxas https://t.co/dHPwvHwfwX." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,996 | "di daw pagsisihan na binoto nila si MDS kahit talo baka dun na kayo magsisi kung si Roxas, Binay or Poe ang mananalo, kayo na matatalino :D." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,997 | "RT @chevalierkun: So "anak ni Mar" pala itong si DJP.
Won't be surprised if "Nese Iye Ne Eng Lehet" will be Mar Roxas' campaign jingle.
#N…." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
6,998 | "Ang kakapal ng mga mukha niyo PNoy at Roxas, matapos niyong batikusin si Poe at Binay sila naman ang yayain niyo? Anu toh Civil War?." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | hate speech | filipino_hatespeech_tiktok |
6,999 | "#OnlyBinayMagAangatSaTacloban." Is this Filipino text hate speech or non-hate speech? Output your answer as either "hate speech" or "non-hate speech". | non-hate speech | filipino_hatespeech_tiktok |
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