Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,400 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pundo', 'kuhaon', 'sa', 'Annual', 'Budget', 'ug', 'dili', 'sa', 'Special', 'Education', 'Fund', 'sama', 'sa', 'Cola', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 8, 0, 0, 0, 7, 8, 8, 0, 0, 7, 0] | cebuaner |
6,401 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpasalamat', 'ang', 'chairman', 'sa', 'committee', 'on', 'education', 'nga', 'nahunong', 'nila', 'niadtong', '2016', 'ug', '15', 'ra', 'gikan', 'sa', '120', 'ka', 'mga', 'eskwelahan', 'ang', 'nakakobra', 'sa', 'Cola', 'balor', 'og', 'tulo', 'ka', 'buwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0] | cebuaner |
6,402 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'ni', 'Young', 'nga', 'mahimo', 'nga', 'ikunhod', 'ang', 'P10,000', 'sa', 'ilang', 'Cola', ',', 'apan', 'siya', 'niinsister', 'nga', 'di', 'kini', 'mahimo', 'sanglit', 'nasayod', 'siya', 'sa', 'kahimtang', 'sa', 'mga', 'magtutudlo', 'nga', 'nagkinahanglan', 'og', 'kwarta', 'hilabi', 'na', 'sa', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,403 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipadayag', 'niya', 'nga', 'wa', 'silay', 'mahimo', 'sanglit', 'benepisyaryo', 'ra', 'sila', 'sa', 'programa', 'ug', 'angay', 'hunahunaon', 'sa', 'mga', 'teacher', 'ang', 'positibo', 'nga', 'aspeto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,404 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kataposang', 'paghatag', 'og', 'pinansiyal', 'nga', 'hinabang', 'sa', 'mga', 'senior', 'citizen', 'ipahigayon', 'karong', 'Disyembre', '17', ',', '2017', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,405 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ilang', 'madawat', 'ang', 'P2,000', 'alang', 'sa', 'Nobiyembre', 'ug', 'Disyembre', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,406 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'venue', 'sama', 'ra', 'sa', 'nangaging', 'pag-apod-apod', ',', 'gikan', 'sa', 'alas', '8', 'sa', 'buntag', 'kutob', 'sa', 'alas', '12', 'sa', 'udto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,407 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naa', 'sa', '67,300', 'ang', 'numero', 'sa', 'mga', 'Senior', 'Citizen', 'nga', 'kuwalipikado', 'nga', 'makadawat', 'sa', 'pinansiyal', 'nga', 'hinabang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,408 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aron', 'sab', 'makadeterminar', 'nga', 'kuwalipikado', 'kini', ',', 'kinahanglan', 'rehistradong', 'botante', 'sa', 'Commission', 'on', 'Elections', '(', 'Comelec', ')', 'gikan', 'sa', '2010', 'paubos', 'ug', 'katong', 'nirehistro', 'og', '2011', 'pataas', ',', 'kinahanglan', 'pani', 'og', 'Cebu', 'City', 'amendment', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0] | cebuaner |
6,409 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SI', 'Cebu', 'Archbishop', 'Jose', 'Palma', 'nanghinaot', 'nga', 'mangita', 'og', 'mga', 'paagi', 'ang', 'awtoridad', 'aron', 'mahimo', 'nga', 'epektibo', 'ang', 'kampanya', 'batok', 'sa', 'gidili', '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, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,410 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'mga', 'operasyon', 'batok', 'sa', 'mga', 'drug', 'personality', ',', 'matod', 'sa', 'arsobispo', ',', 'angay', 'nga', 'tutokan', 'sa', 'gobiyerno', 'ug', 'seryusohon', 'ang', 'drug', 'rehabilitation', 'program', 'sa', 'mga', 'surrenderee', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,411 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Palma', 'subling', 'nipadayag', 'nga', 'nipaluyo', 'sila', 'sa', 'intensyon', 'sa', 'pagsumpo', 'sa', 'gidili', 'nga', 'drugas', 'sa', 'gobiyerno', 'apan', 'duna', 'silay', 'kwestyon', 'sa', 'mga', 'pamaagi', 'niini', 'labi', 'na', 'kon', 'dunay', 'mamatay', 'sa', 'mga', 'operasyon', 'sa', 'kapulisan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,412 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Calunsag', 'gipanid-an', 'sa', 'mga', 'ahente', 'sa', 'PDEA-7', ',', 'gipalitan', 'og', 'usa', 'ka', 'bultong', 'shabu', 'nga', 'mobalor', 'og', 'P32,000', 'gamit', 'ang', 'boodle', 'money', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,413 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nakuhaan', 'og', 'lima', 'ka', 'bultong', 'shabu', 'nga', 'motimbang', 'og', '20', 'gramos', 'nga', 'mobalor', 'og', 'P128,000', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,414 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Monteadora', 'gipanid-an', 'sud', 'na', 'sa', 'pipila', 'ka', 'mga', 'semana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,415 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'makompirmar', 'nihimo', 'sa', 'operasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0] | cebuaner |
6,416 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'ahente', 'sa', 'PDEA-7', 'mipalit', 'og', 'usa', 'ka', 'bultong', 'shabu', 'kang', 'Monteadora', 'nga', 'mobalor', 'og', 'P32,000', 'gamit', 'ang', 'boodle', 'money', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,417 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'barangay', 'Pajo', ',', 'Pusok', 'ug', 'Ibo', 'sa', 'dakbayan', 'sa', 'Lapu-Lapu', 'maoy', 'gihimong', 'pilot', 'barangays', 'nga', 'magsilbing', '‘Discipline', 'Zone’', 'diin', 'paghugtan', 'ang', 'pagpatuman', 'sa', 'national', 'law', 'ug', 'local', 'nga', 'ordinansa', 'alang', 'sa', 'malinawong', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 6, 0, 5, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,418 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mayor', 'Paz', 'Radaza', 'nagkanayon', 'nga', 'sa', 'mga', 'dapit', 'nga', 'gideklarar', 'nga', '‘Discipline', 'Zone’', 'tutokan', 'niini', 'ang', 'pagpatuman', 'sa', 'curfew', ',', 'lagda', 'sa', 'trapiko', ',', 'towing', 'niadtong', 'magpataka', 'og', 'parking', ',', 'di', 'magpataka', 'og', 'labaw', 'sa', 'basura', 'lakip', 'na', 'sa', 'pagpangolekta', 'niini', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,419 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'maong', 'paagi', 'maanam', 'og', 'disiplina', 'ang', 'mga', 'lumolopyo', 'o', 'konstituente', 'nga', 'motuman', 'sa', 'mga', 'balaodnon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,420 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'ubang', 'barangay', 'nga', 'wa', 'hinganli', 'wa', 'nagpasabot', 'nga', 'di', 'motuman', 'sa', 'lagda', 'sanglit', 'buot', 'sa', 'mayor', 'nga', 'ang', 'tibuok', 'dakbayan', 'mamahimong', '‘discipline', 'zone’', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,421 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihangop', 'sa', 'opisyal', 'ang', 'maong', 'programa', 'aron', 'nga', 'makab-ot', 'ang', 'kahusay', 'ug', 'kalinaw', 'dili', 'lang', 'sa', 'usa', 'ka', 'particular', 'nga', 'lugar', 'apan', 'lukop', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,422 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'susamang', 'kalambuan', ',', 'dakong', 'uyon', 'ang', 'mayor', 'nga', 'ibalik', 'na', 'sa', 'kapulisan', 'ang', 'kampaniya', 'batok', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,423 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihangyo', 'ni', 'Councilor', 'David', '“Dave”', 'Tumulak', 'ang', 'mga', 'vendors', 'nga', 'magparehistro', 'na', 'sila', 'daan', 'atol', 'sa', 'umaabot', 'nga', 'Sinulog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,424 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikinahanglan', 'sab', 'nga', 'molukat', 'og', 'permit', 'ang', 'mga', 'manindahay', 'ug', 'hinan-ay', 'ang', 'angay', 'nilang', 'itinda', 'aron', 'mapamatuod', 'sa', 'mga', 'sector', 'group', 'nga', 'gilakipan', 'sa', 'PNP', ',', 'mga', 'tanod', ',', 'ug', 'mga', 'volunteer', 'groups', 'nga', 'maoy', 'mosusi', 'nga', 'sila', 'niagi', 'sa', 'saktong', 'proseso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,425 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MOPAHIGAYON', 'og', 'public', 'bidding', 'ang', 'Cebu', 'City', 'Government', 'aron', 'di', 'maputol', 'ang', 'paghakot', 'sa', 'basura', 'gikan', 'sa', 'transfer', 'station', 'paingon', 'sa', 'landfill', 'inig', 'sugod', 'sa', 'Bag-ong', 'Tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
6,426 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ronald', 'Malacora', ',', 'chairman', 'sa', 'bids', 'and', 'awards', 'committee', '(', 'BAC', ')', ',', 'nagkanayon', 'nga', 'gigahinan', 'og', 'P150', 'milyones', 'sa', 'dakbayan', 'alang', 'sa', 'mosunod', 'nga', 'unom', 'ka', 'buwan', 'gikan', 'sa', 'Enero', 'hangtod', 'sa', 'Hunyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,427 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitug-an', 'ni', 'Malacora', 'nga', 'karong', 'Disyembre', '12', 'ipahigayon', 'ang', 'pre-bid', 'conference', 'sa', 'City', 'Hall', 'ug', 'sa', 'samang', 'higayon', 'ang', 'ilang', 'pagbaligya', 'sa', 'bid', 'documents', 'ngadto', 'sa', 'mga', 'service', 'provider', 'nga', 'gustong', 'moapil', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,428 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'bidding', 'proper', 'ipahigayon', 'sa', 'BAC', 'karong', 'Disyembre', '26', 'aron', 'ilang', 'masayran', 'kon', 'si', 'kinsa', 'ang', 'modaog', 'ug', 'kon', 'mopasar', 'sa', 'post', 'qualification', 'hayan', 'sila', 'ang', 'ideklara', 'nga', 'mananaog', 'ug', 'sila', 'ang', 'mahatagan', 'sa', '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, 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] | cebuaner |
6,429 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sila', 'ang', 'nakadaog', 'sa', 'gipahigyaon', 'nga', 'bidding', 'niadtong', 'buwan', 'sa', 'Agusto', 'pinaagi', 'sa', 'ilang', 'winning', 'bid', 'nga', 'P1,296', 'matag', 'tonelada', '(', '1,000', 'kg', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,430 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'laing', 'mga', 'bidder', 'nga', 'nisalmot', 'mao', 'ang', 'Pasajero', 'Motor', 'Corporation', '(', 'Pamocor', ')', ',', 'kinsa', 'maoy', 'private', 'hauler', 'niadtong', 'Enero', 'hangtod', 'Agusto', 'ning', 'tuiga', 'ug', 'sila', 'sab', 'ang', 'private', 'hauler', 'gikan', 'sa', 'barangay', 'paingon', 'ngadto', 'sa', 'transfer', 'station', '(', 'P56', 'milyones', ')', 'ug', 'ang', 'Quirante', 'Construction', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
6,431 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'firecracker', 'zone', 'maoy', 'gitugotan', 'nga', 'lugar', 'alang', 'niadtong', 'mga', 'konstituente', 'nga', 'magpabuto', 'og', 'firecrackers', 'o', 'pyrotechnics', 'atol', 'sa', 'pagsaulog', 'sa', 'Pasko', 'ug', 'pagsugat', 'sa', 'Bag-ong', 'Tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 8, 0] | cebuaner |
6,432 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikinahanglan', 'nga', 'ang', 'maong', 'area', 'layo', 'sa', 'mga', 'kabalayan', 'aron', 'nga', 'malikay', 'sa', 'posibleng', 'sunog', 'gumikan', 'sa', 'pagtugpa', 'sa', 'pabuto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,433 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibawo', 'sa', 'opisyal', 'nga', 'way', 'limitasyon', 'kon', 'pila', 'ka', 'area', 'ang', 'i-identify', 'nga', 'magsilbing', 'firecracker', 'zone', 'sa', 'usa', 'ka', 'barangay', ',', 'diin', 'moabag', 'sab', 'ang', 'kapulisan', 'sa', 'pagpatuman', '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, 0] | cebuaner |
6,434 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'pa', 'sa', 'mayor', 'nga', 'sa', 'higayon', 'adunay', 'disgrasya', 'o', 'panghitabo', 'gumikan', 'sa', 'paggamit', 'og', 'pabuto', 'nga', 'wa', 'motuman', 'sa', 'firecracker', 'zone', 'manubag', 'ang', 'barangay', 'opisyal', 'ngadto', 'kaniya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,435 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'pagdasig', 'sa', 'arsobispo', 'human', 'gipusil-patay', 'si', 'Padre', 'Marcelito', '“Tito”', 'Paez', ',', '72', ',', 'sa', 'lungsod', 'sa', 'Jaen', ',', 'Nueva', 'Ecija', 'niadtong', 'Lunes', 'sa', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
6,436 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'arsobispo', 'namahayag', 'nga', 'dunay', 'mga', 'pari', 'ug', 'bisan', 'gani', 'mga', 'obispo', 'ang', 'vocal', 'nga', 'nanaway', 'batok', 'sa', 'pipila', 'ka', 'kahiwian', 'sa', 'mga', 'programa', 'sa', 'gobiyerno', 'ug', 'uban', 'pa', 'nga', 'nilapas', 'sa', 'tawhanong', 'katungod', 'sa', 'ubang', 'mga', 'tawo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
6,437 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'misa', 'sa', 'ordinasyon', ',', 'si', 'Palma', 'nipahinumdum', 'sa', 'bag-ong', 'mga', 'deyakono', 'nga', 'wa', 'sila', 'gitawag', 'nga', 'mahimong', '“lords”', 'ug', '“masters”', 'apan', 'mangalagad', 'sa', 'lainlaing', 'mga', 'pamaagi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0] | cebuaner |
6,438 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nidugang', 'nga', 'dako', 'nga', 'pribilihiyo', 'nila', 'nga', 'gitawag', 'sa', 'pagkapari', 'ning', 'Liturgical', 'Year', 'nga', 'gipahinungod', 'alang', 'sa', 'ministry', 'sa', 'kaparian', 'ug', 'relihiyuso', 'nga', 'mga', 'tawo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,439 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Palma', 'nidugang', 'nga', 'human', 'sa', 'unom', 'ka', 'buwan', 'mahimo', 'na', 'sila', 'nga', 'mga', 'pari', 'kon', 'sunod', 'tuig', 'ordinahan', 'sa', 'pagkapari', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,440 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Isip', 'deyakono', 'mahimo', 'sila', 'nga', 'assistant', 'sa', 'mga', 'pari', 'sa', 'misa', ',', 'makapangalawat', ',', 'makasangyaw', 'o', 'makawali', ',', 'makabunyag', 'ug', 'makasaulog', 'og', 'kasal', 'apan', 'di', 'misa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 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 |
6,441 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'taliwala', 'sa', 'deklarasyon', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'nga', 'mga', 'teroristang', 'grupo', 'ang', 'CPP', 'ug', 'NPA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 0, 3, 0] | cebuaner |
6,442 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'retired', 'judge', 'Meinrado', 'Paredes', ',', 'hangtod', 'nga', 'nahasubay', 'pa', 'sa', 'balaod', 'ang', 'ilang', 'gipanghimo', ',', 'angay', 'hatagan', 'ang', 'maong', 'mga', 'organisasyon', 'og', 'igong', 'respeto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,443 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nideklarar', 'si', 'Duterte', 'sa', 'CPP', 'ug', 'NPA', 'isip', 'mga', 'terrorist', 'organizations', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 3, 0, 0, 0, 0, 0] | cebuaner |
6,444 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'CPP-NPA', 'dunay', 'suporta', 'gikan', 'sa', 'nagkadaiyang', 'militanteng', 'mga', 'pundok', 'o', 'front', 'groups', ',', 'nga', 'nakigbisog', 'usab', 'alang', 'sa', 'mga', 'marginalized', 'sector', 'ug', 'sa', 'mga', 'nilupigan', 'sama', 'sa', 'Bayan', ',', 'Anak', 'Pawis', ',', 'Karapatan', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 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, 3, 0, 3, 4, 0, 3, 0, 0, 0, 0] | cebuaner |
6,445 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Paredes', 'sa', 'Free', 'Legal', 'Assistance', 'Group', '(', 'FLAG', ')', 'nagtuo', 'hinuon', 'nga', 'bisan', 'sa', 'suporta', 'nga', 'gihatag', 'sa', 'maong', 'mga', 'grupo', 'sa', 'CPP-NPA', ',', 'angayang', 'di', 'sila', 'direktang', 'pagatwagon', 'nga', 'mga', 'terorista', 'usab', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,446 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'ni', 'Paredes', 'nga', 'dunay', 'katungod', 'ang', 'mga', 'militanteng', 'pundok', 'nga', 'mopadayag', 'sa', 'ilang', 'kaugalingon', 'sa', 'bisan', 'unsang', 'paagi', ',', 'basta', 'di', 'lang', 'sila', 'makalapas', 'sa', 'balaod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 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 |
6,447 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigun', 'ni', 'Paredes', ',', 'duna’y', 'desisyon', 'kaniadto', 'ang', 'Korte', 'Suprema', 'nga', 'nag-ila', 'sa', 'grupong', 'Bayan', 'isip', 'partylist', 'aron', 'makaapil', 'sila', 'sa', 'parliamentary', 'struggle', 'sa', 'legal', 'nga', 'paagi', 'ug', 'dili', 'makig-gubat', 'o', 'mosukol', 'sa', 'gobiyerno', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,448 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naila', 'sab', 'siya', 'sa', 'mga', 'ngan', 'nga', 'Jamie', ',', 'Japie', 'ug', 'Daddy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0, 1, 0] | cebuaner |
6,449 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Uwahing', 'nakit-an', 'si', 'Phinney', 'pasado', 'ala', '1', 'sa', 'kaadlawon', 'sa', 'nag-roving', 'nga', 'gwardiya', 'sa', 'NBI.', 'Siya', 'rang', 'usa', 'ang', 'sulod', 'sa', 'detention', 'cell', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,450 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pasado', 'alas', '2', 'sa', 'kaadlawon', ',', 'nakadungog', 'og', 'kasikas', 'ang', 'babayeng', 'piniriso', 'sa', 'tupad', 'nga', 'detention', 'cell', ',', 'gikan', 'sa', 'dapit', 'diin', 'gipriso', 'ang', 'langyaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,451 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Phinney', 'nasikop', 'sa', 'NBI', 'niadtong', 'Lunes', 'sa', 'gabii', 'sa', 'giabangang', 'balay', 'sa', 'Camella', 'Homes', ',', 'Brgy.', 'Lawaan', ',', 'Talisay', '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, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0] | cebuaner |
6,452 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nag-atubang', 'og', 'kasong', 'kalapasan', 'sa', 'Sec.', '5', ',', 'Article', 'III', 'sa', 'RA', '7610', 'kun', 'Anti-Child', 'Abuse', 'Law', 'ang', 'maong', 'langyaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 7, 8, 8, 0, 0, 0, 0] | cebuaner |
6,453 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasuta', 'nga', 'asphyxia', 'due', 'to', 'hanging', 'maoy', 'hinungdan', 'sa', 'kamatayon', '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] | cebuaner |
6,454 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'imbestigasyon', 'sa', 'NBI', ',', 'dunay', 'lima', 'ka', 'mga', 'bata', 'nga', 'giingong', 'gi-abuso', 'ug', 'gipahimudsan', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,455 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Adto', 'niya', 'buhaton', 'ang', 'pag-molestiya', 'kanila', 'sa', 'laing', 'balay', 'nga', 'iyang', 'giabangan', 'sa', 'Aureo', 'street', ',', 'Brgy.', 'Maghaway', ',', 'sa', 'siyudad', 'gihapon', 'sa', 'Talisay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,456 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'wa', 'pa', 'gani', 'maimplementar', ',', 'nakalarga', 'na', 'siya', 'sa', 'gawas', 'sa', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,457 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Agusto', 'ning', 'tuiga', ',', 'napasakaan', 'og', 'pormal', 'nga', 'reklamo', 'sa', 'Talisay', 'City', 'Prosecutor’s', 'Office', 'si', 'Phinney', ',', 'apan', 'sa', 'ngalan', 'nga', '“Jaime', 'Peterson”', ',', 'nga', 'usa', 'sa', 'mga', 'alyas', 'nga', 'iyang', 'gigamit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,458 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SI', 'Presidente', 'Rodrigo', 'Duterte', 'nimando', 'sa', 'Philippine', 'National', 'Police', 'sa', 'pagbalik', 'sa', 'mga', 'operasyon', 'batok', 'sa', 'illegal', 'drugs', ',', 'butyag', 'sa', 'iyang', 'tigpamaba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,459 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Roque', 'niingon', 'nga', 'si', 'Duterte', 'nipirma', 'sa', 'memorandum', 'kagahapon', ',', 'Martes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,460 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gihimo', 'ang', 'pahibawo', 'atol', 'sa', 'joint', 'command', 'conference', 'sa', 'Armadong', 'Kusog', 'ug', 'kapulisan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0] | cebuaner |
6,461 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nidangop', 'sa', 'PNP', 'Anti', 'Cyber', 'Crime', 'Group-7', 'ang', 'usa', 'ka', '25', 'anyos', 'nga', 'dalaga', 'uban', 'ang', 'iyang', 'inahan', 'nga', 'taga', 'syudad', 'sa', 'Talisay', 'human', 'gipakatap', 'pinaagi', 'sa', 'Facebook', 'sa', 'iyang', 'kanhi', 'uyab', 'nga', 'naa', 'karon', 'sa', 'gawas', 'sa', 'nasud', 'ang', 'iyang', 'hubo', 'nga', 'hulagway', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,462 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'suspek', 'lumad', 'nga', 'taga', 'Davao', 'Oriental', 'apan', 'kasamtangan', 'nga', 'nagtrabaho', 'sa', 'Saudi', 'Arabia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
6,463 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'ngan', 'sa', 'biktima', 'gitago', 'lang', 'sa', 'Superbalita', 'Cebu', 'alang', 'sa', 'iyang', 'proteksyon', ',', 'unang', 'miduol', 'sa', 'estasyon', 'DYHP', 'RMN', 'Cebu', 'aron', 'magpatabang', 'unsay', 'iyang', 'buhaton', 'sa', 'gihimo', 'sa', 'iyang', 'kanhi', 'hinigugma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,464 | 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', 'nga', 'sa', 'dihang', 'maayo', 'pa', 'ang', 'ilang', 'relasyon', 'ug', 'tungod', 'sa', 'iyang', 'paghigugma', 'sa', 'lalake', ',', 'iyang', 'gitug-an', 'ang', 'password', 'sa', 'iyang', 'Facebook', 'account', 'ug', 'mahimo', 'usab', 'siya', 'nga', 'makaabli', 'sa', 'account', 'sa', 'iyang', 'uyab', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,465 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'niadtong', 'nanglabay', 'nga', 'mga', 'buwan', ',', 'niaslom', 'ang', 'ilang', 'relasyon', 'nunot', 'sa', 'batasan', 'sa', 'lalake', 'nga', 'wala', 'niya', 'mauyoni', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,466 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakahukom', 'siya', 'nga', 'makig', 'buwag', 'na', 'tungod', 'kay', 'dili', 'na', 'niya', 'madawat', 'ang', 'mga', 'pagduda', 'sa', 'iyang', 'hinigugma', 'nga', 'duna', 'siyay', 'laing', 'lalake', 'nga', 'gikarelasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,467 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakurat', 'siya', 'nga', 'niadtong', 'milabayng', 'buwan', 'nakadawat', 'siya', 'og', 'mensahe', 'pinaagi', 'sa', 'Facebook', 'nga', 'naa', 'ang', 'ilang', 'hulagway', 'nga', 'wala', 'nay', 'sapot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,468 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mokabat', 'sa', 'P240,000', 'cash', 'ug', 'mga', 'alahas', 'ang', 'nadala', 'sa', 'mga', 'kawatan', 'nga', 'nilungkab', 'sa', 'remitance', 'branch', 'sa', 'LBC', 'sa', 'Lunes', 'sa', 'kadlawon', 'sa', 'Brgy.', 'Poblacion', ',', 'lungsod', 'sa', 'Dalaguete', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
6,469 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'tulo', 'ka', 'mga', 'kawatan', 'wa', 'magtabon', 'sa', 'ilang', 'mga', 'nawong', 'apan', 'nagkawo', 'pagsulod', 'sa', 'buhatan', 'diin', 'nakuha', 'sa', 'CCTV', 'camera', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,470 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alas', '2:21', 'sa', 'kadlawon', 'dihang', 'naabot', 'ang', 'mga', 'kawatan', 'sakay', 'sa', 'motorsiklo', 'ug', 'giablihan', 'ang', 'roll-up', 'door', 'dayon', 'gibuak', 'ang', 'glass', 'door', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,471 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'kantidad', 'maoy', 'gihatag', 'sa', 'Siyudad', 'para', 'sa', 'Cost', 'of', 'Living', 'Allowance', '(', 'Cola', ')', 'ug', 'hardship', 'allowance', 'sa', 'libuan', 'ka', 'mga', 'magtutudlo', 'sa', 'DepEd', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
6,472 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'patakaran', 'sa', 'COA', 'nga', 'di', 'mahimong', 'mo-release', 'og', 'laing', 'kantidad', 'ang', 'usa', 'ka', 'ahensiya', 'sa', 'gobiyerno', 'sa', 'samang', 'benepisyaryo', 'ug', 'katuyoan', 'kon', 'adunay', 'natanggong', 'nga', 'problema', 'sama', 'sa', 'disallowance', 'nga', 'dili', 'pa', 'masulbad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,473 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'City', 'Treasurer', 'Tessie', 'Camarillo', 'nagkanayon', 'nga', 'ila', 'pang', 'susihon', 'ang', 'mga', 'line', 'items', 'sa', 'financial', 'assistance', 'kay', 'siya', 'nibutyag', 'nga', 'ang', 'maong', 'ND', 'niadto', 'pang', '2015', 'apan', 'naapil', 'pa', 'sa', 'Annual', 'Budget', 'para', 'sa', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,474 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gipasabot', 'nga', 'kon', 'ang', 'P56', 'milyones', 'ilawom', 'gihapon', 'sa', 'Cola', 'ug', 'hardship', 'allowance', 'di', 'ma-release', 'ang', 'kantidad', 'hangtod', 'di', 'masulbad', 'ang', 'ND', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,475 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'sa', 'maong', 'punto', ',', 'ang', 'tag', 'P10,000', 'gilatid', 'isip', 'ayuda', 'sa', 'usa', 'ka', 'ordinansa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,476 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gisalig', 'ang', 'maong', 'problema', 'ngadto', 'sa', 'mga', 'opisyal', 'sa', 'Siyudad', 'nga', 'mahatagan', 'ra', 'kini', 'og', 'solusyon', 'para', 'madayon', 'og', 'hatag', 'ang', 'P56', 'milyones', 'nga', 'pundo', 'para', 'sa', 'financial', 'assistance', 'nga', 'nalatid', 'sa', 'ordinansa', 'nga', 'gipangamahanan', 'ni', 'kanhi', 'Konsehal', 'Lea', 'Japson', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 0] | cebuaner |
6,477 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'wa', 'pa', 'ang', 'notisya', ',', 'ang', 'COA', 'niisyu', 'og', 'notice', 'of', 'suspension', 'niadtong', 'Pebrero', '23', 'sa', 'miaging', 'tuig', 'labot', 'sa', 'P78.9', 'milyones', 'nga', 'Cola', 'ug', 'P1.4', 'milyones', 'nga', 'hardship', 'allowance', 'para', 'sa', 'mga', 'magtutudlo', 'nga', 'gikuha', 'gikan', 'sa', 'Special', 'Education', 'Fund', '(', 'SEF', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0] | cebuaner |
6,478 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'posibleng', 'manubag', 'mao', 'silang', 'kanhi', 'mayor', 'Michael', 'Rama', 'ug', 'iyang', 'mga', 'department', 'head', 'kinsa', 'apil', 'sa', 'distribution', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,479 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Mayo', '11', 'sa', 'miaging', 'tuig', ',', 'ang', 'Local', 'School', 'Board', 'nitubag', 'sa', 'notisya', 'sa', 'COA', 'ug', 'nidason', 'nga', 'ilawom', 'sa', 'RA', '4670', 'kuon', 'Magna', 'Carta', 'for', 'Public', 'School', 'Teachers', ',', 'gitugotan', 'niini', 'ang', 'paghatag', 'og', 'Cola', 'uban', 'sa', 'section', '19', 'sa', 'samang', 'balaod', 'para', 'mga', 'magtutudlo', 'nga', 'nag-atubang', 'og', 'kalisod', 'pag-adto', 'sa', 'ilang', 'eskwelahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,480 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'COA', 'subay', 'sa', 'ilang', 'notisya', 'niingon', 'nga', 'way', 'merito', 'ang', 'gibasehan', 'sa', 'Siyudad', 'kay', 'ubos', 'sa', 'section', '12', 'sa', 'RA', '6758', ',', 'ang', 'paghatag', 'og', 'dugang', 'insentibo', 'sa', 'magtutudlo', 'nalakip', 'na', 'sa', 'standardized', 'salary', 'rate', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,481 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitudlo', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'ang', 'pag-umangkon', 'ni', 'Mayor', 'Tomas', 'Osmeña', 'aron', 'maoy', 'mohulip', 'sa', 'bakanteng', 'posisyon', 'sa', 'City', 'Council', 'nga', 'gibiyaan', 'ni', 'Hanz', 'Abella', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2, 0] | cebuaner |
6,482 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Abella', 'gitudlo', 'ni', 'Duterte', 'isip', 'commissioner', 'sa', 'National', 'Labor', 'Relations', 'Commission', '(', 'NLRC', ')', '7th', 'division', 'ug', 'niluwat', 'sa', 'pagkakonsehal', 'niadtong', 'Septiyembre', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,483 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Junjun', 'nagpapili', 'niadtong', '2016', 'pagkakonsehal', 'sa', 'south', 'district', 'ilawom', 'sa', 'UNA-Team', 'Rama', 'apan', 'nahimutang', 'siya', 'sa', 'ika-walo', 'ug', 'pito', 'ra', 'ang', 'masulod', 'sa', 'konseho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,484 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigun', 'sa', 'PHO', ',', 'ang', 'monitoring', 'posibling', 'molungtad', 'pa', 'og', '10', 'ngadto', 'sa', '20', 'ka', 'tuig', ',', 'depende', 'kon', 'kanus-a', 'usab', 'masinati', 'sa', 'nabakunahan', 'ang', 'giingong', 'dautang', 'epekto', 'sa', 'bakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,485 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Plano', 'nila', 'nga', 'maghimo', 'og', 'protocol', 'alang', 'sa', 'mas', 'systematic', 'nga', 'monitoring', 'ug', 'surveillance', 'sa', 'mga', 'bata', 'sa', 'probinsiya', 'nga', 'nabakunahan', 'sa', 'kontrobersiyal', 'nga', 'Dengvaxia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,486 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Subay', 'niini', ',', 'gipadayag', 'ni', 'Catan', 'nga', 'i-tap', 'nila', 'ang', 'DepEd', 'ingun', 'man', 'ang', 'mga', 'BHWs', 'lukop', 'lalawigan', 'alang', 'sa', 'pagsusi', 'kon', 'duna', 'nay', 'mga', 'bata', 'nga', 'nipakita', 'sa', 'dautang', 'epekto', 'sa', 'bakuna', ',', 'ilabi', 'na', 'kadtong', 'mga', 'wala', 'pa', 'makasinati', 'ug', '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, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,487 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'gihapon', 'makasiguro', 'ang', 'PHO', 'kon', 'unsa', 'ang', 'mahitabo', 'ngadto', 'sa', 'mga', 'bata', 'nga', 'wala', 'pa', 'maigo', 'sa', 'sakit', 'nga', 'dengue', 'apan', 'nakadawat', 'na', 'sa', 'bakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0] | cebuaner |
6,488 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Catan', ',', 'importante', 'nga', 'masubay', 'gikan', 'karon', 'ang', 'tanang', 'mahitabo', 'sa', 'panlawas', 'sa', 'mga', 'nabakunahan', ',', 'ilabi', 'na', 'kon', 'kini', 'madangat', 'sa', 'tambalanan', 'nunot', 'sa', 'bisan', 'unsang', 'sakit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,489 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Susihon', 'usab', 'karon', 'sa', 'PHO', 'kon', 'ang', 'mga', 'recent', 'dengue', 'deaths', 'nga', 'natala', 'sa', 'probinsiya', ',', 'may', 'kalabutan', 'na', 'ba', 'sa', 'dautang', 'epekto', 'sa', 'bakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0] | cebuaner |
6,490 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mae', 'Cheryl', 'Tepait', ',', 'coordinator', 'sa', 'Expanded', 'Program', 'on', 'Immunization', 'sa', 'city', 'health', 'nagkanayon', 'nga', 'sukad', 'nga', 'gihatag', 'ang', 'Dengvaxia', 'niadtong', 'Hunyo', '9', 'ngadto', 'na', 'sa', 'Hulyo', '31', ',', '2017', 'alang', 'sa', 'first', 'dose', 'way', 'nadangat', 'nga', 'reklamo', 'sa', 'ilang', 'buhatan', 'kalabot', 'sa', 'giingong', 'simtomas', 'sa', 'side', 'effects', 'sa', 'maong', 'bakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 4, 4, 4, 0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,491 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '30', 'ka', 'mga', 'barangay', 'sa', 'siyudad', 'ang', 'mipahibawo', 'na', 'pinaagi', 'sa', 'rekorida', 'ngadto', 'sa', 'ilang', 'mga', 'konstituente', 'nga', 'gihunong', 'una', 'ang', 'paghatag', 'sa', 'maong', 'bakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,492 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikatakda', 'unta', 'nga', 'sa', 'Disyembre', '9', 'magsugod', 'ang', 'city', 'health', 'sa', 'paghatag', 'sa', 'second', 'dose', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0] | cebuaner |
6,493 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-awhag', 'sab', 'ni', 'Tepait', 'kadtong', 'mga', 'ginikanan', 'kansang', 'anak', 'nakadawat', 'sa', 'maong', 'vaccine', 'nga', 'sa', 'higayon', 'nga', 'adunay', 'gibate', 'o', 'hilantanang', 'pasyente', ',', 'mopahibalo', 'gilayon', 'ngadto', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'health', 'center', 'aron', 'nga', 'ma-monitor', 'kini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,494 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kapin', 'sa', '1,000', 'ka', 'mga', 'nurse', 'ang', 'gikinahanglan', 'karon', 'sa', 'Provincial', 'Health', 'Office', 'aron', 'matubag', 'ang', 'panginahanglan', 'sa', 'mga', 'district', 'ug', 'provincial', 'hospitals', '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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,495 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'niini', ',', 'moabot', 'sa', '250', 'ka', 'mga', 'doctor', 'ang', 'gikinahanglan', 'usab', 'sa', 'maong', 'mga', 'tambalanan.', 'Ang', 'giaprubahan', 'nga', 'dakong', 'budget', 'alang', 'sa', 'hospital', 'operations', 'gitumbok', 'nga', 'makatabang', 'pagkab-ot', 'sa', 'maong', 'panginahanglan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,496 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Catan', ',', 'sa', 'maong', 'budget', 'na', 'unya', 'kuhaon', 'ang', 'pondo', 'alang', 'sa', 'pagkuha', 'sa', 'dugang', 'nurse', 'ug', 'doctor', 'nga', 'maka-serbisyo', 'sa', 'mga', 'tambalanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,497 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'sa', '2,500', 'nurses', 'ang', 'gikinahanglan', 'alang', 'sa', 'tanang', 'tambalanan', 'ubos', 'sa', 'PHO', 'apan', '1,000', 'lang', 'ang', 'anaa', 'sa', 'pagka-karon.', 'Sigun', 'ni', 'Catan', ',', 'usa', 'ka', 'nurse', 'ang', 'ideal', 'nga', 'mobantay', 'sa', 'matag', '10', 'ka', 'pasyente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,498 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'sa', '250', 'ka', 'mga', 'doctor', 'nga', 'gikinahanglan', ',', 'wala', 'pa', 'makaabot', 'Og', '100', 'ang', 'mga', 'doctor', 'nga', 'kasamtangang', 'nag-alagad', 'karon', 'sa', 'mga', 'tambalanan', 'sa', 'probinsiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 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 |
6,499 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'matuman', 'na', 'sa', 'PHO', 'ang', 'mga', 'kalambuan', 'nga', 'gitinguha', 'niini', 'sa', 'mga', 'tambalanan', ',', 'lakip', 'sa', 'himuon', 'nilang', 'basehanan', 'sa', 'maayo', 'nila', 'ang', 'serbisyo', 'mao', 'ang', 'pagmenus', 'na', 'unya', 'sa', 'maternal', 'death', 'rate', 'sa', 'probinsiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 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] | cebuaner |
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