Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,200 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'joint', 'team', 'nga', 'gipanguluhan', 'ni', 'Yape', 'nakakuhag', 'duha', 'ka', 'kalibre', '.45', 'nga', 'pistola', 'sa', 'lawak', 'sa', 'balay', 'ni', 'Manit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,201 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'dunay', 'magazine', 'ug', 'mga', 'bala', ',', 'lakip', 'na', 'ang', 'usa', 'ka', 'black', 'widow', '9', 'mm', 'nga', 'may', 'upat', 'ka', 'mga', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,202 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipadayag', 'niini', 'nga', 'daghan', 'pang', 'mga', 'susama', 'kang', 'Manit', 'nga', 'nagsilbing', 'mga', 'stockholder', 'sa', 'paltik', 'nga', 'armas', 'nga', 'nagpadayon', 'sa', 'ilang', 'negosyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,203 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Yape', 'nga', 'ang', 'ilang', 'operasyon', 'tubag', 'sa', 'mando', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'nga', 'panakpon', 'ang', 'mga', 'tawo', 'nga', 'nagnegusyo', 'og', 'illegal', 'nga', 'armas', 'nga', 'sagad', 'maadto', 'sa', 'dautang', 'mga', 'elemento', ',', 'ingon', 'man', 'adto', 'ipamaligya', 'sa', 'Mindanao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 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, 5, 0] | cebuaner |
6,204 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Arsobispo', 'Jose', 'Palma', 'nibutyag', 'nga', 'ibalhin', 'ang', 'lawas', 'ni', 'Camomot', 'sa', 'museum', 'nga', 'duol', 'sa', 'gilubngan', '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, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,205 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'lawas', 'ni', 'Camomot', 'unang', 'giugkat', 'niadtong', '2009', 'gikan', 'sa', 'sam-ang', 'publiko', 'sa', 'Carcar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 0] | cebuaner |
6,206 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'namahayag', 'nga', 'apeke', 'ang', 'lugar', 'nga', 'nahimutangan', 'sa', 'lubnganan', 'sa', 'anhing', 'arsobispo', 'ug', 'maghuot', 'ang', 'mga', 'tawo', 'nga', 'moduaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,207 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'matugaw', 'usab', 'ang', 'kahilom', 'ug', 'kasolemnidad', 'sa', 'dapit', 'labi', 'na', 'kon', 'mag-ampo', 'ang', 'mga', 'madre', 'kon', 'daghang', 'mga', 'tawo', 'ang', 'mobisita', 'sa', 'lubnganan', 'ni', 'Camomot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,208 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Congregation', 'for', 'the', 'Causes', 'of', 'Saints', 'sa', 'Vatican', 'nitugot', 'na', 'sa', 'diocesan', 'process', 'sa', 'artsidyosesis', 'alang', 'sa', 'kawsa', 'sa', 'pagkahimo', 'nga', 'santos', 'ni', 'Camomot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,209 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Palma', 'nisaysay', 'nga', 'naa', 'na', 'sa', 'proseso', 'sila', 'si', 'Silloriquez', 'ug', 'Padre', 'Mhar', 'Vincent', 'Balili', ',', 'vice', 'postulator', 'sa', 'paghimo', 'sa', '“positiu.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 7] | cebuaner |
6,210 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'arsobispo', 'nipasabot', 'ang', '“positiu”', 'kay', 'ang', 'opisyal', 'nga', 'libro', 'sa', 'kinabuhi', 'ni', 'Camomot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,211 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '“positiu”', 'iduso', 'ngadto', 'sa', 'mga', 'Theologian', 'sa', 'Vatican', 'ug', 'sa', 'Santo', 'Papa', 'ug', 'kon', 'kini', 'motugot', ',', 'mosaka', 'sa', 'sunod', 'nga', 'proseso', ',', 'ang', 'pagkahimo', 'nga', '“venerable”', 'ni', 'Camomot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 7, 0, 0, 0, 0, 0, 0, 5, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 1, 0] | cebuaner |
6,212 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niini', ',', 'mosunod', 'ang', 'mas', 'mahinungdanon', 'nga', 'proseso', 'diin', 'maghipos', 'ug', 'manguha', 'sila', 'og', 'mga', 'estorya', 'o', 'testimonya', 'sa', 'mga', 'tawo', 'nga', 'naayo', 'o', 'namilagrohan', 'pinaagi', 'sa', 'panginlaba', 'ni', 'Camomot', 'ug', 'kon', 'kini', 'makapasar', 'nga', 'mga', 'milagro', ',', 'ang', 'anhing', 'arsobispo', 'ideklarar', 'na', 'sa', 'pagka', 'beato', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,213 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Shabu', ',', 'mga', 'paraphernalia', 'sa', 'paggamit', 'sa', 'gidiing', 'drugas', ',', 'mobile-wifi', 'ug', 'Samsung', 'Tab', ',', 'ug', 'mga', 'simpack', 'mao', 'ang', 'nakuha', 'sa', 'mga', 'tinugyanan', 'sa', 'Talisay', 'City', 'Jail', 'atol', 'sa', 'ilang', 'gihimong', 'greyhound', 'operation', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,214 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Inopia', 'nga', 'hapit', 'kada', 'adlaw', 'sila', 'mohimog', 'greyhound', 'operation', 'sa', 'ilang', 'pasilidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,215 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'sa', 'miaging', 'adlaw', 'nakadawat', 'silag', 'taho', 'nga', 'adunay', 'mga', 'ilegal', 'sa', 'ilang', 'isolation', 'cell', 'sa', 'mga', 'lalaki', 'nga', 'adunay', 'unom', 'ka', 'mga', 'piniriso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,216 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'hinungdan', 'nga', 'ilang', 'gilusad', 'ang', 'greyhound', 'operation', 'nga', 'miresulta', 'sa', 'pagkasakmit', 'sa', 'usa', 'ka', 'bultong', 'shabu', ',', 'mga', 'plastic', 'nga', 'adunay', 'lama', 'sa', 'shabu', 'mga', 'shabu', 'paraphernalia', ',', 'usa', 'ka', 'unit', 'sa', 'Samsung', 'Tab', ',', 'mobile', 'wifi', ',', 'charger', ',', 'ug', 'mga', 'simpack', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,217 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Inopia', 'nga', 'ang', 'maong', 'mga', 'kontrabando', 'didto', 'nakuha', 'sa', 'posisyon', 'ni', 'Yayam', 'Aliman', 'Gaviola', ',', 'kinsa', 'nag-atubang', 'og', 'kasong', 'ilegal', 'nga', 'drugas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,218 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', ',', 'si', 'Gaviola', 'mihimakak', 'sa', 'pasangil', 'nga', 'iya', 'ang', 'maong', 'mga', 'drugas', 'nga', 'nakuha', 'ug', 'nitumbok', 'niadtong', 'gipatay', 'nga', 'suspek', 'sa', 'pagpangrakrak', 'nga', 'si', 'Arjianne', 'Bacus', 'nga', 'maoy', 'tag-iya', 'sa', 'mga', 'kontrabando', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,219 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iya', 'hinuong', 'giangkon', 'ang', 'gadgets', 'aron', 'malingaw', 'siya', 'sa', 'sulod', 'ug', 'nadala', 'kini', 'nila', 'sa', 'dihang', 'aduna', 'siyay', 'husay', 'gikan', 'sa', 'iyang', 'mga', 'paryenti', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,220 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Inopia', 'niingon', 'nga', 'duha', 'ka', 'mga', 'kaso', 'ang', 'atubangon', 'ni', 'Gaviola', 'una', 'mao', 'ang', 'ilegal', 'nga', 'drugas', 'ug', 'ika-duha', 'mao', 'ang', 'pagpasulod', 'sa', 'gadgets', 'diha', 'sa', 'prisohan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,221 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'niini', 'gibutyag', 'ni', 'Kongresista', 'Gerald', 'Anthony', 'Gullas', 'nga', 'nasulod', 'na', 'kini', 'sa', 'ilang', '2018', 'nga', 'budget', 'ug', 'gibutang', 'sa', 'DPWH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
6,222 | 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', 'kon', 'mahuman', 'ang', 'maong', 'proyekto', 'mao', 'unya', 'kini', 'ang', 'usa', 'sa', 'makatabang', 'nga', 'mapaluag', 'sa', 'dagan', 'sa', 'trapiko', 'sa', 'habagatang', 'bahin', 'sa', 'lalawigan', '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, 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, 5, 0] | cebuaner |
6,223 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'ang', 'kongresista', 'nga', 'usa', 'kini', 'sa', 'suliran', 'diha', 'sa', 'habagatang', 'bahin', 'tungod', 'kay', 'mabara', 'gayod', 'ang', 'trapiko', 'sa', 'San', 'Fernando', 'tungod', 'kay', 'gagmay', 'pa', 'ang', 'karsada', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,224 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'usa', 'sa', 'ilang', 'nakitang', 'suliran', 'mao', 'ang', 'tag-iya', 'sa', 'mga', 'pribadong', 'luna', 'nga', 'maapektohan', 'sa', 'pagpalapad', 'sa', 'karsada', 'tungod', 'kay', 'aduna', 'man', 'gayoy', 'mga', 'tag-iya', 'nga', 'magmagahi', 'ug', 'dihang', 'dapita', 'malangan', 'ang', 'ilang', 'proyekto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,225 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'uban', 'mosukol', 'ug', 'maabot', 'gayod', 'ngadto', 'sa', 'korte', 'nga', 'mosangko', 'usab', 'sa', 'pagkalangan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,226 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'ang', 'pagpalit', 'sa', 'maong', 'mga', 'luna', 'nalakip', 'na', 'sa', 'budget', 'nga', 'muabot', 'sa', 'P290', 'milyones', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,227 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Gullas', 'nga', 'kon', 'mahuman', 'kini', 'sa', '2018', 'ang', 'pagpalapad', 'sa', 'karsada', 'lahos', 'na', 'gayod', 'sa', 'lungsod', 'sa', 'Carcar', 'ug', 'lungsod', 'sa', 'Sibongab', 'iya', 'na', 'usab', 'nga', 'ilakip', 'sa', '2019', 'budget', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,228 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Doble', 'ang', 'madawat', 'unya', 'sa', 'mga', 'regular', 'nga', 'kawani', 'sa', 'Mandaue', 'City', 'Hall', 'karon', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 7, 0] | cebuaner |
6,229 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'human', 'giaprobahan', 'sa', 'konseho', 'sa', 'dakbayan', 'ang', 'paghatag', 'sa', 'P5,000', 'Productivity', 'Enhancement', 'Incentive', '(', 'PEI', ')', 'ug', 'ang', 'Year-End', 'Cash', 'Incentive', 'nga', 'bugti', 'sa', 'usa', 'ka', 'buwan', 'nga', 'suholan.', 'Lakip', 'sa', 'makadawat', 'niini', 'ang', 'mga', 'piniling', 'opisyal', 'sa', 'dakbayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,230 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'sa', 'DO', '143-2017', ',', 'ang', 'pagpanghatag', 'ug', 'PEI', 'base', 'sa', 'section', '6', 'sa', 'EO', 'no.', '2011', 'nga', 'nagkanayon', 'nga', 'sukad', 'Disyembre', '2016', ',', 'ang', 'PEI', 'ihatag', 'sa', 'tanang', 'mga', 'kwalipikadong', 'kawani', 'sa', 'gobiyerno', 'sa', 'di', 'mosayo', 'sa', 'Disyembre', '15', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 8, 0, 0, 0, 0, 7, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,231 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihatagan', 'og', 'ultimatum', 'ni', 'Mayor', 'Tomas', 'Osmeña', 'ang', 'SM', 'Prime', 'Holdings', 'Inc.', '(', 'SMPHI', ')', 'pagtangtang', 'sa', 'ilang', 'cube', 'nga', 'estruktura', 'atubangan', 'sa', 'SM', 'Seaside', '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, 1, 2, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0] | cebuaner |
6,232 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'nihatag', 'og', 'higayon', 'sa', 'SMPHI', 'hangtod', 'katapusan', 'sa', 'selebrasyon', 'sa', 'Sinulog', 'Grand', 'Parade', '2018', 'diin', 'gitumong', 'sa', 'adlaw', 'sa', 'Balaang', 'Bata', 'nga', 'si', 'Snr.', 'Sto.', 'Niño', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 1, 0] | cebuaner |
6,233 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibahad', 'sa', 'mayor', 'nga', 'kon', 'dili', 'makuha', 'ang', 'cube', 'nga', 'nahimutang', 'sa', 'setback', 'kon', 'lugar', 'nga', 'dili', 'angayan', 'tukoran', 'og', 'estruktura', 'iyang', 'sirad-an', 'ang', 'mga', 'sinehan', 'sulod', 'sa', 'SM', 'Seaside', 'ug', 'ilang', 'department', 'store', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0] | cebuaner |
6,234 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Opisyal', 'sa', 'SMPHI', 'nibutyag', 'sa', 'Superbalita', 'Cebu', 'nga', '“no', 'comment”', 'una', 'sila', 'sa', 'gianunsyo', 'sa', 'mayor', 'diin', 'ang', 'landscape', 'kilid', 'sa', 'ilang', 'mall', 'ila', 'nang', 'giguba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,235 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'gipadayag', 'ni', 'Osmeña', 'nga', 'walay', 'labot', 'ang', 'mga', 'tenant', 'o', 'nag-abang', 'sulod', 'sa', 'SM', 'Seaside', 'City', 'uban', 'sa', 'pagbutyag', 'nga', 'dili', 'na', 'siya', 'motugot', 'nga', 'adunay', 'bag-ong', 'patigayon', 'sulod', 'sa', 'maong', 'mall', 'nga', 'mokuha', 'og', 'business', 'permit', '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, 0, 1, 0, 0, 0, 0, 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, 0, 3, 4, 0] | cebuaner |
6,236 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ipadakop', 'ug', 'ipapriso', 'ni', 'Mayor', 'Tomas', 'Osmeña', 'ang', 'mga', 'tawo', 'kinsa', 'mag-inom', 'atol', 'sa', 'Sinulog', 'Grand', 'Parade', 'sa', 'Enero', 'sunod', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0] | cebuaner |
6,237 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'gikatakdang', 'moluwat', 'og', 'executive', 'order', 'aron', 'pag-ban', 'sa', 'inom', 'atol', 'sa', 'maong', 'adlaw', 'diin', 'did-an', 'ang', 'mga', 'tawo', 'pag-inom', 'bisan', 'sulod', 'sa', 'mga', 'restaurant-bar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,238 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'posibleng', 'sa', 'South', 'Road', 'Properties', '(', 'SRP', ')', 'iyang', 'itugot', 'nga', 'dunay', 'mabaligya', 'nga', 'ilimnon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,239 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mayor', 'gusto', 'nga', 'mopahamtang', 'og', 'disiplina', 'sa', 'mga', 'tawo', 'panahon', 'sa', 'Sinulog', 'sanglit', 'siya', 'nabalaka', 'sa', 'mga', 'terorista', 'nga', 'mohimo', 'og', 'kasamok', 'atol', 'sa', 'maong', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,240 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gipadayag', 'nga', 'andam', 'ug', 'tagana', 'kanunay', 'ang', 'mga', 'terorista', 'sa', 'pagbuhat', 'og', 'kasamok', 'bisan', 'unsa', 'nga', '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] | cebuaner |
6,241 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Di', 'nila', 'igsapayan', 'bisan', 'kon', 'kini', 'nagkahulagan', 'sa', '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] | cebuaner |
6,242 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gihulagway', 'nga', 'ang', 'nahitabo', 'sa', 'mga', 'terorista', 'sa', 'siyudad', 'sa', 'Marawi', 'diin', 'gatusan', 'sa', 'ilang', 'mga', 'kauban', 'ang', 'namatay', 'ug', 'gikasakit', 'sa', 'ilang', 'mga', 'kauban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,243 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Di', 'layo', 'sa', 'kamatuoran', 'nga', 'sila', 'mohimo', 'og', 'kasamok', 'aron', 'makabawos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,244 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'sa', 'mayor', 'nga', 'wa', 'siya', 'niingon', 'nga', 'moanhi', 'sa', 'Sugbo', 'ang', 'mga', 'terorista', 'apan', 'maayong', 'managana', 'ug', 'mangandam', 'sa', 'tanang', 'higayon', 'sanglit', 'di', 'ma-screen', 'ang', 'tanang', '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, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,245 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'usa', 'ka', 'maestra', 'sa', 'dakbayan', 'sa', 'Danao', 'usab', 'ang', 'nagpositibo', 'usab', 'sa', 'paggamit', 'og', 'shabu', 'sa', 'buwag', 'nga', 'drug', 'test', 'sa', 'Cpadao', '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, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0] | cebuaner |
6,246 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ivy', 'Remedios', 'Durano-Meca', ',', 'pangu', 'sa', 'Cpadao', ',', 'nibutyag', 'nga', '843', 'tanan', 'ka', 'mga', 'empleyado', 'sa', 'usa', 'ka', 'siyudad', 'ug', 'tulo', 'ka', 'mga', 'lungsod', 'sa', 'lalawigan', 'sa', 'Sugbo', 'ang', 'gi-drug', 'test', 'gikan', 'sa', 'Disyembre', '1', 'hangtod', 'sa', 'Disyembre', '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, 1, 2, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,247 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'naglakip', 'sa', 'Danao', 'ug', 'mga', 'lungsod', 'sa', 'Alegria', ',', 'Consolacion', 'ug', 'Aloguinsan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0] | cebuaner |
6,248 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Meca', 'niklaro', 'ang', 'nagpositibo', 'nga', 'mga', 'kawani', 'kay', 'ipaubos', 'pa', 'sa', 'confirmatory', 'test', 'sa', 'Department', 'of', 'Health', 'sa', 'kaulohan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0] | cebuaner |
6,249 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nakompirmar', 'na', 'sa', 'Cpadao', 'nga', 'nagpositibo', 'sa', 'paggamit', 'og', 'shabu', 'ang', 'maestra', 'sa', 'high', 'school', 'sa', 'publikong', 'tunghaan', 'sa', 'Danao', 'base', 'sa', 'nadawat', 'niini', 'nga', 'resulta', 'sa', 'confirmatory', 'test', 'gikan', 'sa', 'DOH', 'duha', 'ka', 'semana', 'na', 'ang', 'nakalabay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,250 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', ',', 'tinuon', 'ang', 'degree', 'sa', 'addiction', 'sa', 'maong', 'maestra', 'aron', 'mahibaw-an', 'kon', 'unsa', 'nga', 'level', 'ang', 'programa', 'sa', 'drug', 'rehabilitation', '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] | cebuaner |
6,251 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'nasikop', 'giila', 'nga', 'si', 'Jovan', 'Magdadaro', ',', 'live', 'in', 'partner', 'sa', 'anak', 'sa', 'magtiayong', 'Liesyl', 'ug', 'Edwin', 'Bordadora', 'sa', 'Sityo', 'Katibis', ',', 'nga', 'giingong', 'dunay', 'dakong', 'kasuko', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,252 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'nasikop', 'ang', 'higala', 'niini', 'nga', 'kauban', 'ni', 'Magdadaro', 'sa', 'pagpanigbas', 'nga', 'si', 'John', 'Lou', 'Ardena', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 2, 0] | cebuaner |
6,253 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'duha', 'giing', 'mga', 'drug', 'user', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,254 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'matod', 'pa', 'makauyon', 'ang', 'magtiayon', 'sa', 'live-in', 'sa', 'ilang', 'anak', 'nga', 'maoy', 'hinungdan', 'sa', 'kasuko', 'sa', 'suspek', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,255 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Gi', 'amin', 'sa', 'suspek', 'nga', 'ilang', 'gipatay', 'ang', 'mag-asawa', 'kay', 'gikasuk-an', 'daw', 'siya', 'kay', 'wala', 'daw', 'kauyon', 'sa', 'ilang', 'pag', 'live', 'sa', 'anak', 'ani', 'mao', 'to', 'gitabangan', 'nila', 'ug', 'tigbas', ',', '”', 'matod', 'ni', 'Devaras', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0] | cebuaner |
6,256 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Magdadaro', 'pa', 'ang', 'unang', 'nipahibawo', 'nianang', 'gabii', 'nga', 'iyang', 'nakaplagan', 'si', 'Liesyl', 'nga', 'patay', 'na', 'unahan', 'sa', 'pinuy-anan', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,257 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagduda', 'sa', 'sinugdan', 'ang', 'mga', 'pulis', 'nga', 'si', 'Edwin', 'ang', 'killer', ',', 'apan', 'nianang', 'buntag', 'niadtong', 'Huwebes', 'nakaplagan', 'kini', 'sa', 'mga', 'pulis', 'nga', 'patay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,258 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ilang', 'gisalikway', 'nga', 'tulis', 'ang', 'motibo', 'kay', 'wa', 'gikuha', 'ang', 'mga', 'butang', 'sa', 'magtiayon', ',', 'apil', 'na', 'ang', 'kwarta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,259 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasusama', 'usab', 'kini', 'og', 'political', 'rally', 'tungod', 'sa', 'mga', 'placard', 'nga', 'nagsaway', 'kang', 'Mayor', 'Tomas', 'Osmeña', ',', 'numero', 'uno', 'nga', 'kritiko', 'ni', 'Rupinta', 'ug', 'pagsinggit', 'sa', 'iyang', 'mga', 'supporter', 'sa', 'ilang', 'pag-agi', 'sa', 'Cebu', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0] | cebuaner |
6,260 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Emosyonal', 'ang', 'mga', 'katawhan', 'ug', 'nakita', 'gyud', 'kung', 'unsa', 'kabug-at', 'ug', 'kasakit', 'ang', 'ilang', 'gibati', 'sa', 'pagkawala', 'sa', 'ilang', 'pinalanggang', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,261 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dako', 'usab', 'og', 'pasalamat', 'ang', 'pamilya', 'ni', 'Rupinta', 'sa', 'suporta', 'sa', 'partido', 'ug', 'sa', 'katawhan', '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, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,262 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'resista', 'dunay', 'bug-at', 'kaayong', 'mga', 'pulong', 'batok', 'kang', 'Osmeña', 'samtang', 'ang', 'uban', 'nanaghilak', 'sa', 'ilang', 'pagpanamilit', '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, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,263 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Hustisya', '!', 'Tomas', ',', 'berdugo', 'ka', '!', ',', '”', 'singgit', 'sa', 'mga', 'supporter', 'ni', 'Rupinta', 'samtang', 'ang', 'ubang', 'nanglabay', 'og', 'mga', 'water', 'bottle', 'sa', 'ilang', 'pag-agi', 'sa', 'entrance', 'sa', 'Cebu', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0] | cebuaner |
6,264 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'pay', 'namalikas', 'kang', 'Osmeña', 'ug', 'kang', 'Winifredo', 'Miro', 'samtang', 'nagwara-wara', 'sa', 'ilang', 'placards', 'nga', 'naa', 'ang', 'mga', 'ngan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,265 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dihang', 'gibuksan', 'ang', 'lungon', 'ni', 'Rupinta', 'sa', 'dayong', 'pagtunod', 'kaniya', 'sa', 'iyang', 'lubnganan', 'sa', 'Queen', 'City', 'Memorial', 'Garden', ',', 'ang', 'pamilya', 'ug', 'kahigalaan', 'nisinggit', 'og', '“Ayo-ayo', ',', 'Kap', '!', '”', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,266 | 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', 'tawo', 'nga', 'nagmotorsiklo', 'ang', 'nipusil', 'kang', 'Bacus', 'nga', 'nag-inusara', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
6,267 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bacus', 'maoy', 'usa', 'sa', 'duha', 'ka', 'akusado', 'sa', 'pagpangrakrak', 'sa', 'Talisay', 'City', 'Jail', 'sa', 'miaging', 'buwan', 'apan', 'nakapiyansa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 6, 6, 0, 0, 0, 0, 0, 0] | cebuaner |
6,268 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Department', 'of', 'Health', '(', 'DOH', ')', 'modemanda', 'sa', 'Sanofi', 'sa', 'pag-uli', 'sa', 'P3.5-billion', 'nga', 'gibayad', 'alang', 'sa', 'Dengvaxia', 'vaccines', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 4, 4, 4, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
6,269 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'niini', ',', 'abagahon', 'usab', 'ang', 'nagasto', 'sa', 'nangaapektuhang', 'mga', 'biktima', 'sa', 'pagpaospital', ',', 'matod', 'ni', 'Health', 'Secretary', 'Francisco', 'Duque', 'III', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 2, 0] | cebuaner |
6,270 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'DOH', 'nisuspenso', 'sa', 'dengue', 'vaccination', 'program', 'niadtong', 'Disyembre', '1', 'human', 'ang', 'Sanofi', 'Pasteur', 'nipahibawo', 'nga', 'ang', 'Dengvaxia', 'vaccine', 'mopadako', 'sa', 'risgo', 'sa', 'grabeng', 'dengue', 'sa', 'mga', 'tawo', 'nga', 'wa', 'pa', 'matakboyi', 'og', 'dengue', 'sa', 'wa', 'pa', 'bakunahi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,271 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kapin', 'sa', '700,000', 'ka', 'mga', 'bata', 'ang', 'nakadawat', 'na', 'sa', 'mao', 'nga', 'bakuna', 'sukad', 'sa', 'miaging', '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] | cebuaner |
6,272 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Duque', 'niingon', 'nga', 'ang', 'DOH', 'nihimo', 'og', 'task', 'force', 'nga', 'mag-review', 'sa', 'dengue', 'vaccination', 'program', 'sa', 'kagamhanan', 'nga', 'nagsugod', 'sa', 'Marso', '2016', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,273 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'giawhag', 'ang', 'publiko', 'nga', 'magmabinantayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,274 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'nay', 'nidaog', 'nga', 'bidder', 'nga', 'mohimo', 'og', 'license', 'plates', 'alang', 'sa', 'sakyanan', 'ug', 'motorsiklo', '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] | cebuaner |
6,275 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibawo', 'kini', 'sa', 'LTO', 'human', 'gisaway', 'ni', 'House', 'Speaker', 'Pantaleon', 'Alvarez', 'si', 'LTO', 'Chief', 'Edgar', 'Galvante', 'nga', 'giingong', 'nagmika', 'sa', 'iyang', 'trabaho', 'sa', 'kapakyas', 'pag-aksyon', 'sa', 'mga', 'suliran', 'sa', 'supply', 'sa', 'license', 'plates', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,276 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kapin', 'sa', 'sayis', 'milyones', 'ka', 'mga', 'plaka', 'sa', 'mga', 'sakyanan', 'ug', 'motorsiklo', 'ang', 'na-backlog', 'sa', 'LTO', 'sa', 'tibuok', 'nasod', 'gikan', 'sa', 'Hulyo', '2016', 'hangtod', 'sa', 'Nobiyembre', '2017', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,277 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', '136,070', 'ka', 'mga', 'plaka', 'gikan', 'sa', 'Enero', '2017', 'hangtod', '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] | cebuaner |
6,278 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Pulga', 'sa', 'Superbalita', 'Cebu', 'nga', 'ang', 'maong', 'mga', 'sakyanan', 'duna', 'nay', 'naka-assign', 'nga', 'mga', 'plaka', 'apan', 'ang', 'orihinal', 'nga', 'license', 'plates', 'kay', 'i-manufacture', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,279 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gilaoman', 'nga', 'karong', 'Marso', '2018', 'masugdan', 'og', 'hatod', 'ang', 'pasiuna', 'nga', 'mga', 'plaka', 'gikan', 'sa', 'manufacturer', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,280 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', '2013', ',', 'gikwestyon', 'sa', 'Commission', 'on', 'Audit', 'ug', 'mga', 'korte', 'ang', 'pagpalit', 'sa', 'LTO', 'og', 'mga', 'plaka', 'alang', 'sa', 'mga', 'motor', 'vehicle', 'ug', 'motorcycle', 'ug', 'ang', 'mga', 'kaso', 'nag-ung-ong', 'hangtod', 'karon.', 'Ang', 'pagpalit', 'og', 'mga', 'plaka', 'alang', 'sa', 'mga', 'sakyanan', 'nga', 'narehistro', 'gikan', 'sa', '2014', 'hangtod', '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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,281 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dihang', 'niasumir', 'ang', 'bag-ong', 'administrasyon', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'niadtong', 'Hulyo', '2016', ',', 'way', 'gigahin', 'nga', 'pundo', 'alang', 'sa', 'pagpalitog', 'mga', 'plaka', 'alang', 'sa', '2016', ',', '2017', 'ug', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,282 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'Galvante', 'nihangyo', 'og', 'dugang', 'pundo', 'nga', 'P400', 'milyones', 'nga', 'naapil', 'sa', 'pundo', 'sa', 'LTO', 'sa', '2017', 'aron', 'ipadayon', 'ang', 'pagpalit', 'og', 'mga', 'plaka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,283 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Nobiyembre', 'sa', 'miaging', 'tuig', ',', 'si', 'DoTr', 'Secretary', 'Arthur', 'Tugade', 'nitugot', 'sa', 'hangyo', 'ni', 'Galvante', 'ug', 'nipadaplin', 'og', 'P1', 'bilyones', 'gikan', 'sa', 'pundo', 'sa', 'iyang', 'buhatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,284 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'nga', 'ang', 'OSEC', 'usa', 'ka', 'matang', 'sa', 'pang-abuso', 'ngadto', 'sa', 'kabataan', 'nga', 'nagkataas', 'ang', 'kaso', 'dinhi', 'sa', 'nasud', 'gumikan', 'sa', 'pag-usbong', 'sa', 'paggamit', 'og', 'internet', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,285 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pinaagi', 'sa', 'pagpirma', 'og', 'memorandum', 'of', 'agreement', '(', 'MOA', ')', 'ang', 'kagamhanan', 'sa', 'lungsod', 'sa', 'Cordova', 'misuporta', 'sa', 'maong', 'kampanya', 'diin', 'gatusan', 'ka', 'mga', 'tinun-an', ',', 'civil', 'society', 'leaders', 'ug', 'mga', 'opisyal', 'sa', 'munisipyo', 'ang', 'nagsilbing', 'saksi', 'sa', 'maong', 'kalihokan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,286 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'ikalimod', 'nga', 'ang', 'Cordova', 'gi-ila', 'nga', 'kanhi', '“hotspot”', 'sa', 'maong', 'matang', 'sa', 'human', 'trafficking', 'ug', 'tungod', 'niini', ',', 'gihingusgan', 'ni', 'Mayor', 'Sitoy-Cho', 'ang', 'kampanya', 'sa', 'katuyoan', 'nga', 'mahunong', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,287 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Subsob', 'nga', 'pagpanglimpyo', 'sa', 'palibot', 'maoy', 'gihingusdan', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'sa', 'Lapu-Lapu', 'aron', 'batokan', 'ang', 'pagkuyanap', 'lamok', 'nga', 'nagdala', 'sa', 'dengue', 'virus', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
6,288 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'talaan', 'sa', 'City', 'Health', 'Office', ',', 'kapin', 'sa', '35,000', 'ka', 'mga', 'bata', 'ang', 'nahatagan', 'sa', 'Dengvaxia', 'vaccine', 'sa', 'unang', 'dose', 'niadtong', 'Hunyo', 'ug', 'Hulyo', 'ning', 'tuiga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,289 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nakapabalaka', 'sa', 'mga', 'ginikanan', 'nga', 'inay', 'moprotehir', 'sa', 'bakona', 'batok', 'sa', 'dengue', 'virus', 'mahiagom', 'na', 'hinuon', 'sa', 'mas', 'grabe', 'nga', 'sakit', 'sa', 'dengue', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,290 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giaprobahan', 'na', 'ang', 'P1.4', 'bilyones', 'nga', 'pundo', 'sa', 'Provincial', 'Health', 'Office', '(', 'PHO', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0] | cebuaner |
6,291 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Dr.', 'Rene', 'Catan', ',', 'pangu', 'sa', 'PHO', ',', 'nibutyag', 'sa', 'unang', 'quarter', 'sa', 'sunod', 'tuig', 'mahatod', 'na', 'ang', 'duha', 'ka', 'sea', 'ambulances', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,292 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Dr.', 'Catan', ',', 'mapasayon', 'na', 'ang', 'paghatod', 'ug', 'pagkuha', 'sa', 'mga', 'pasyente', 'sa', 'kaislahan', 'sa', 'lalawigan', 'sa', 'Sugbo', 'pinaagi', 'sa', 'duha', 'ka', 'sea', 'ambulances', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,293 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Durano', 'nga', 'kinahanglang', '50', '/', '50', 'ang', 'aksyon', 'sa', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'sa', 'kampanya', 'batok', 'sa', 'gidiling', 'drugas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,294 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikutlo', 'sa', 'pamahayag', 'ni', 'Palma', 'nga', 'gawas', 'sa', 'subsob', 'nga', 'pagpanakop', ',', 'himuon', 'ang', 'seryuso', 'nga', 'rehabilitasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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] | cebuaner |
6,295 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'hingpit', 'nga', 'rehabilitasyon', ',', 'dugang', 'ni', 'Durano', ',', 'moubos', 'usab', 'ang', 'demanda', 'gidiling', 'drugas', 'samtang', 'gihimo', 'ang', 'hugot', 'nga', 'operasyon', 'sa', 'pagwagtang', 'sa', 'tinubdan', 'sa', 'drugas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,296 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ug', 'mas', 'malampuson', 'ang', 'kampanya', 'sa', 'awtoridad', 'kon', 'dunay', 'halapad', 'nga', 'suporta', 'gikan', 'sa', 'komunidad', 'busa', 'giduso', 'paghimo', 'sa', 'Community', 'Base', 'Treatment', 'Program', '(', 'CBTP', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 7, 8, 8, 8, 8, 8, 8, 0] | cebuaner |
6,297 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Agg', 'mga', 'graduwalo', 'sa', 'CBTP', 'naghatag', 'og', 'paglaom', 'nga', 'makatrabaho', 'og', 'balik', 'ug', 'manginabuhing', 'normal', ',', 'kini', 'sukdanan', 'sa', 'maayong', 'resulta', 'sa', 'hiniusang', 'paningkamot', 'sa', 'nakalainlaing', 'organisasyon', 'nga', 'nipatuman', 'sa', 'CBTP', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,298 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'ni', 'Durano', 'nga', 'ang', 'CPADAO', 'hugot', 'nga', 'nakig-alayon', 'sa', 'Department', 'of', 'Health', '7', '(', 'DOH', ')', 'alang', 'sa', 'pag-monitor', 'sa', 'mga', 'migraduwar', 'aron', 'mareporma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,299 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dunay', '2,798', 'ang', 'migradwar', 'sa', 'CBTP', 'sa', 'Sugbo', 'base', 'sa', 'framework', 'sa', 'DOH', '7', 'gikan', 'sa', 'dakbayan', 'sa', 'Naga', 'ug', 'Bogo', 'ug', 'mga', 'lungsod', 'sa', 'Argao', ',', 'Dalaguete', ',', 'San', 'Remegio', ',', 'Carmen', ',', 'Sogod', ',', 'ug', 'Boljoon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 5, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 0, 5, 0] | cebuaner |
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