Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,000 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Laing', 'KG9', 'sub', 'machine', 'pistol', 'sulod', 'sa', 'balay', 'niini', 'ang', 'nasakmit', 'uban', 'sa', 'usa', 'ka', '.380', 'pistol', 'nga', 'dunay', 'magazine', 'ug', 'daghang', 'mga', 'bala', 'ug', 'usa', 'ka', 'Ingram', 'sub', 'machine', 'pistol', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,001 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ramos', 'way', 'lisenya', 'isip', 'security', 'guard', 'ang', 'armas', ',', 'nibutyag', 'nga', 'gipanag-iya', 'ni', 'Butanas', 'ang', 'mga', 'armas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0] | cebuaner |
6,002 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'kawani', 'niini', 'ingon', 'man', 'kabanaynmibutyag', 'nga', 'sayo', 'nga', 'nilakaw', 'si', 'Butanas', 'kay', 'nagpaubos', 'sa', 'medical', 'check-up', 'hinungdan', 'wa', 'diha', 'sa', 'panimalay', 'uban', 'sa', 'iyang', 'asawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,003 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Yape', 'nibutyag', 'nga', 'nakadawat', 'silag', 'impormasyon', 'kabahin', 'sa', 'dili', 'maayong', 'gawi', 'ni', 'Ramos', 'nga', 'giingong', 'kanunay', 'nga', 'mopakita', 'sa', 'iyang', 'armas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,004 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'ubang', 'mga', 'impormasyon', 'nga', 'ilang', 'nadawat', 'batok', 'sa', 'negosyante', 'ila', 'pang', 'gisusi', 'ug', 'givalidate', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,005 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Yape', 'nga', 'si', 'Butanas', 'way', 'record', 'nga', 'legal', 'ang', 'iyang', 'mga', 'armas', 'ug', 'nagtiner', 'na', 'hinuog', 'way', 'mga', 'lisensiya', ',', 'lakip', 'na', 'ang', 'pagbutang', 'ogi', 'gwardiya', 'nga', 'walay', 'agency', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,006 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Cebu', 'City', 'Councilor', 'Dave', 'Tumulak', 'nga', 'dili', 'dali', 'ang', 'pagpatuman', 'sa', 'istriktong', 'disiplina', 'sa', 'firecracker', '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, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,007 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'kay', 'hangtod', 'karong', 'panahona', 'daghan', 'sa', 'mga', 'barangay', 'ang', 'naninda', 'gihapon', 'og', 'ilegal', 'nga', 'mga', 'pabuto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,008 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hugot', 'ang', 'ilang', 'mando', 'nga', 'diha', 'ra', 'dapit', 'sa', 'South', 'Road', 'Properties', 'ang', 'gitugotan', 'nga', 'makabaligya', 'og', '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] | cebuaner |
6,009 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Tumulak', 'nga', 'angay', 'lang', 'nga', 'bantayan', 'kini', 'tungod', 'kay', 'adunay', 'mga', 'bata', 'nga', 'magpabuto', 'gamit', 'ang', 'watusi', 'diin', 'delikado', 'kini', 'tungod', 'kay', 'possible', 'pa', 'kini', 'nga', 'makahilo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,010 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihangyo', 'sab', 'sa', 'mga', 'opisyal', 'ang', 'mga', 'katawhan', 'ang', 'pagdili', 'ug', 'paglikay', 'sa', 'paggamit', 'og', 'fire', 'crackers', 'tungod', 'para', 'ra', 'sab', 'kini', 'sa', 'safety', 'ug', 'aron', 'sad', 'makatipid', 'pa', 'sa', 'umaabot', 'nga', 'pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,011 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mas', 'labing', 'maayo', 'pa', 'ang', 'pag-andam', 'nalang', 'og', 'handa', 'para', 'sa', 'pasko', 'kaysa', 'mogasto', 'pa', 'kini', 'para', 'pangpalit', 'og', 'fire', 'crackers', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,012 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hingpit', 'nang', 'nakagawas', 'gikan', 'sa', 'tambalanan', 'ang', '10', 'anyos', 'nga', 'batang', 'lalaki', 'nga', 'nakadawat', 'sa', 'anti-dengue', 'vaccine', 'o', 'Dengvaxia', 'apan', 'nahidangat', 'sa', 'tambalanan', 'human', 'nasakit', 'na', 'hinuon', 'og', '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, 0, 0, 0, 0, 0] | cebuaner |
6,013 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'bata', 'nakagawas', 'niadtong', 'Dominggo', 'lang', 'sa', 'hapon', 'apan', 'gipabalik', 'kini', 'sa', 'pribadong', 'tambalanan', 'kagahapon', 'sa', 'buntag', 'agig', 'tipik', 'sa', 'pag-monitor', 'sa', 'kahimtang', 'sa', 'maong', 'bata', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 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 |
6,014 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Jinny', 'Ababon', ',', 'inahan', 'ni', 'Jujen', 'Ababon', 'nga', 'taga', 'Barangay', 'Lawaaan-3', ',', 'dakbayan', 'sa', 'Talisay', 'miingon', 'nga', 'daw', 'nahuwasan', 'na', 'siya', 'sa', 'kabalaka', 'human', 'sila', 'gipahibawo', 'sa', 'mananambal', 'nga', 'hingpit', 'nang', 'naayo', 'ang', 'bata', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,015 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', ',', 'sa', 'samang', 'higayon', ',', 'gitambagan', 'siya', 'nga', 'ipadayong', 'ang', 'pag-monitor', 'sa', 'bata', 'ug', 'dili', 'una', 'kini', 'magpalabig', 'haguon', 'ang', 'panglawas', 'niini', 'tungod', 'kay', 'bag-o', 'pa', 'kining', 'naayo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,016 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'Department', 'of', 'Health', 'mipahibawo', 'na', 'usab', 'kaniya', 'nga', 'ang', 'ahensiya', 'motapal', 'sa', 'nabayran', 'sa', '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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,017 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'amahan', 'sa', 'bata', 'nga', 'si', 'Jhonry', 'Ababon', 'miingon', 'nga', 'grabe', 'usab', 'ang', 'iyang', 'gibating', 'kaguol', 'ug', 'kahadlok', 'sa', 'dihang', 'gi-dengue', 'ang', 'iyang', 'anak', 'human', 'usab', 'siya', 'nakadungog', 'sa', 'balita', 'nga', 'kadtong', 'nabakunahan', 'og', 'dengvaxia', 'vaccine', 'nga', 'wala', 'pa', 'makasuway', 'og', 'dengue', 'mao', 'na', 'hinuom', 'ang', 'magrabehan', 'kon', 'mataptapan', '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, 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, 0, 0] | cebuaner |
6,018 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'nga', 'kon', 'duna', 'pay', 'dautang', 'nahitabo', 'sa', 'iyang', 'anak', 'mopasaka', 'gyud', 'siyag', 'kaso', 'sa', 'nagbakuna', '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] | cebuaner |
6,019 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'nang', 'nakahatag', 'og', 'P1', 'million', 'nga', 'financial', 'assistance', 'ang', 'Kapitloy', 'sa', 'Kananga', ',', 'Leyte', 'niadtong', 'Agosto', ',', 'human', 'kini', 'naigo', 'sa', 'kusog', 'nga', 'linog', 'sa', 'bulan', 'sa', 'Hulyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,020 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pag-adto', 'ni', 'Gobernador', 'Hilario', 'Davide', 'III', 'ug', 'sa', 'mga', 'tinugyanan', 'sa', 'Kapitolyo', 'nga', 'nag-turn', 'over', 'sa', 'cheke', ',', 'ila', 'gikahimamat', 'si', 'Mayor', 'Richard', 'Gomez', 'sa', 'Ormoc', ',', 'kinsa', 'personal', 'nga', 'nangayo', 'ug', 'hinabang', 'sa', 'probinsiya', 'tungod', 'sa', 'kadaot', 'usab', 'nga', 'ilang', 'naangkon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,021 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'mga', 'rekisito', 'sa', 'pag-proseso', 'sa', 'P1', 'million', 'gihapon', 'nga', 'hinabang', 'alang', 'sa', 'Omoc', ',', 'mao', 'ang', 'deklarasyon', 'sa', 'state', 'of', 'calamity', 'sa', 'dapit', 'ingun', 'man', 'ang', 'resolusyon', 'sa', 'Hunta', 'Probinsiyal', 'sa', 'Sugbo', 'nga', 'nagtugot', 'sa', 'paghatag', 'sa', 'maong', 'ayuda', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,022 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Inay', 'nga', 'tutokan', 'ang', 'pagpangayo', 'og', 'refund', 'gikan', 'sa', 'tigama', 'sa', 'kontrobersiyal', 'nga', 'dengue', 'vaccine', 'nga', 'Dengvaxia', ',', 'si', 'Gob.', 'Hilario', 'Davide', 'III', 'nagtuo', 'nga', 'mas', 'importanteng', 'mahatagan', 'og', 'pagtagad', 'ang', 'mga', 'bata', 'nga', 'nabakunahan', '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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,023 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'nunot', 'sa', 'pagpahibalo', 'sa', 'maong', 'kompaniya', 'nga', 'dunay', 'dautang', 'epekto', 'ang', 'bakuna', 'ngadto', 'sa', 'mga', 'bata', 'nga', 'nabakunahan', 'apan', 'wala', 'pa', 'maigo', '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, 0] | cebuaner |
6,024 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'gobernador', ',', 'angayang', 'masiguro', 'kon', 'kinsa', 'kadtong', 'mga', 'bata', 'nga', 'mopakita', 'sa', 'dautang', 'epekto', 'sa', 'Dengvaxia', 'ug', 'kihanglang', 'hatagan', 'dayon', 'kini', 'sa', 'dinaliang', 'medical', 'nga', 'atensiyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,025 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Subay', 'sa', 'gipangayo', 'nga', 'refund', ',', 'ang', 'DOH', 'nagtinguha', 'pag-uli', 'sa', 'kapin', '800', 'mil', 'ka', 'mga', 'bakuna', 'nga', 'wa', 'magamit', 'nga', 'nagkantidad', 'og', 'kapin', 'sa', 'P1', 'billion', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
6,026 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'nasayran', ',', 'gisugyot', 'karon', 'didto', 'sa', 'Kaulohan', 'nga', 'iuli', 'sa', 'Sanofi-Pasteur', 'ang', 'nabayad', 'sa', 'gobiyerno', 'nga', 'P3.5', 'B', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,027 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aron', 'hatagan', 'og', 'kahigayonan', 'nga', 'makaampo', 'ang', 'mga', 'masakiton', ',', 'bisitahon', 'sa', 'imahen', 'ni', 'Sr.', 'Sto.', 'Niño', 'ang', 'duha', 'ka', 'dagkong', 'pampubliko', 'nga', 'tambalanan', 'sa', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,028 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'Dominggo', ',', 'dalhon', 'ang', 'imahen', 'sa', 'Vicente', 'Sotto', 'Memorial', 'Medical', 'Center', '(', 'VSMMC', ')', ',', 'ug', 'molungtad', 'kini', 'hangtod', 'Martes', '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, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,029 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Konsehal', 'Dave', 'Tumulak', ',', 'kinsa', 'Executive', 'Committee', 'Chairman', 'sa', 'Sinulog', '2018', ',', 'nga', 'gihimo', 'kini', 'nila', 'aron', 'mahatagan', 'usab', 'og', 'kahigayonan', 'ang', 'mga', 'masakiton', 'nga', 'anaa', 'sa', 'tambalanan', 'nga', 'makaampo', 'subay', 'na', 'usab', 'sa', 'umaabot', 'nga', 'kapyistahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,030 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikan', 'sa', 'Basilica', 'Minore', 'del', 'Sto.', 'Niño', ',', 'mag-mobile', 'procession', 'kini', 'paingon', 'sa', 'VSMMC', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 6, 6, 6, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,031 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alas', '12:00', 'sa', 'udto', 'sa', 'Martes', ',', 'ibalhin', 'kini', 'sa', 'Cebu', 'City', 'Medical', 'Center', 'hangtod', 'sa', 'Huwebes.', 'Gibutyag', 'usab', 'ni', 'Tumulak', 'nga', 'mobisita', 'una', 'kini', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,032 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'bisitahon', 'niini', 'ang', 'Operation', 'Second', 'Chance', 'sa', 'Enero', '9', ',', 'ug', 'ang', 'Cebu', 'City', 'Jail', 'Female', 'Dormitory', 'samtang', 'Enero', '10', ',', 'sa', 'Bureau', 'of', 'Jail', 'Management', 'and', 'Penology', 'Facility', 'sa', 'Kalunasan', ',', 'Gawas', 'niini', ',', 'makahigayon', 'usab', 'pagduol', 'ang', 'pamilya', 'sa', 'mga', 'piniriso', 'nga', 'makaampo', 'ingon', 'man', 'i-prosesyon', 'usab', 'kini', 'sa', 'mga', 'selda', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 3, 4, 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] | cebuaner |
6,033 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'paman', 'sa', 'kakalungan', 'sa', 'abilidad', 'ni', 'Mary', 'June', 'Solitana', 'tungod', 'sa', 'iyang', 'orthopedic', 'impairment', ',', 'wala', 'kini', 'makapugong', 'kaniya', 'aron', 'makahimo', 'og', 'nagkadaiyang', 'artworks', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,034 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '34-anyos', 'nga', 'lumad', 'sa', 'Barangay', 'Zone', '6', ',', 'Santa', 'Barbara', ',', 'Iloilo', 'gihimong', 'instrumento', 'ang', 'paghimo', 'sa', 'iyang', 'artworks', 'aron', 'mapadayag', 'niya', 'ang', 'iyang', 'gibati', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,035 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gamit', 'ang', 'papel', 'ug', 'lapis', ',', 'nagsugod', 'siya', 'sa', 'pagdibuho', 'niadtong', '12-anyos', 'palang', 'siya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,036 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Manguha', 'ra', 'siya', 'og', 'papel', 'sa', 'iyang', 'mga', 'igsuon', 'aron', 'aduna', 'siyay', 'lingaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,037 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'MJ', ',', 'dili', 'makalakaw', ',', 'ug', 'anaa', 'lang', 'sa', '3', 'ka', 'tiil', 'ang', 'gitas-on', 'tungod', 'sa', 'iyang', 'kakulangan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,038 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gamit', 'ang', 'kamot', 'niini', 'ug', 'iyang', 'baba', ',', 'makahimo', 'na', 'siya', 'og', 'nagkadaiyang', 'artwork', 'sulod', 'lang', 'sa', 'usa', 'ka', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,039 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dihang', 'namatay', 'ang', 'iyang', 'inahan', 'niadtong', '2011', ',', 'didto', 'siya', 'nagsugod', 'pagsuway', 'og', 'painting', 'gamit', 'ang', 'Titus', 'Pen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 7, 8, 0] | cebuaner |
6,040 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'MJ', 'nga', 'nahinabi', 'sa', 'Superbalita', 'Cebu', 'nga', 'usa', 'sa', 'hagit', 'niya', 'mao', 'ang', 'pagbutang', 'og', 'emosyon', 'sa', 'iyang', 'artwork', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,041 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna’y', 'higayon', 'nga', 'maglagot', 'siya', 'o', 'nasubo', 'ug', 'mao', 'kini', 'ang', 'iyang', 'gigamit', 'aron', 'makahimo', 'og', 'nagkadaiyang', 'maanindot', 'nga', 'mga', 'painting', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,042 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'MJ', 'mao', 'ang', 'ika-11', 'nga', 'anak', 'sa', '12', 'ka', 'mga', 'managsuon', ',', 'diin', '10', 'niini', 'ang', 'lalake', 'ug', 'duha', 'sila', 'nga', 'babaye', 'sa', 'iyang', 'kinamanghuran', 'nga', 'igsuon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,043 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagkakaron', ',', 'gitabangan', 'si', 'MJ', 'ni', 'Arnel', 'Murga', 'pinaagi', 'sa', 'Project', 'Akay', 'diin', 'usa', 'kini', 'ka', 'kampanya', 'alang', 'sa', 'mga', 'persons', 'with', 'disabilities', '(', 'PWDs', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,044 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'damgo', 'niini', 'mao', 'ang', 'mahimong', 'ilado', 'nga', 'artist', 'dili', 'lang', 'sa', 'nasod', ',', 'apan', 'lakip', 'sa', 'laing', 'nasod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,045 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'umaabot', 'nga', 'Enero', ',', 'posibleng', 'dalhon', 'ang', 'artworks', 'ni', 'MJ', 'sa', 'Indonesia', 'alang', 'sa', 'usa', 'ka', 'exhibit', 'pinaagi', 'ang', 'ASEAN', 'Foundation', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
6,046 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'BOC', 'mokansilar', 'usab', 'sa', 'lisensiya', 'sa', 'broker', 'nga', 'nagbase', 'sa', 'Sugbo', 'nga', 'maoy', 'nagtabang', 'sa', 'pagdala', 'sa', 'container', 'vans', 'nga', 'wa', 'gideklarar', 'og', 'sakto', 'paingon', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 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, 5, 0] | cebuaner |
6,047 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'consignees', 'ug', 'ang', 'broker', 'ang', 'kasohan', 'usab', 'og', 'paglapas', 'sa', 'Customs', 'Modernization', 'Tariff', 'Act', 'ug', 'Anti-Agricultural', 'Smuggling', 'Act', 'of', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,048 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'ilang', 'nakaplagan', ',', 'sumala', 'sa', 'commissioner', ',', 'ang', '71', 'ka', 'container', 'vans', 'nga', 'gikataho', 'dunay', 'sulod', 'nga', 'smuggled', 'rice', 'kay', 'nagbalor', 'og', 'P83', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,049 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'container', 'van', 'nahiabot', 'sa', 'Sugbo', 'sa', 'managlahi', 'nga', 'mga', 'petsa', 'luwan', 'sa', 'lima', 'ka', 'mga', 'barko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,050 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'unang', 'set', 'sa', 'container', 'vans', 'gihatod', 'niadtong', 'Nobiyembre', '27', ',', '29', 'ug', '30', 'samtang', 'ang', 'ikaduhang', 'set', 'kay', 'nahatod', 'niadtong', 'Disyembre', '3', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,051 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pag-abot', 'sa', 'container', 'vans', 'sa', 'Sugbo', ',', 'gideklarar', 'kini', 'nga', 'dunay', 'sulod', 'nga', '“ceramic', 'tiles.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,052 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'dihang', 'gipaubos', 'sa', 'X-ray', 'inspection', ',', 'nakaplagan', 'nga', 'duna', 'kini', 'sud', 'nga', 'mga', 'sinakong', 'bugas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,053 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niini', ',', 'nakahukom', 'ang', 'mga', 'opisyal', 'sa', 'BOC', 'nga', 'dili', 'una', 'i-release', 'ang', 'mga', 'kargamento', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,054 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pasiunang', 'pagbukas', ',', 'ila', 'dayon', 'nadiskobrihan', 'ang', 'mga', 'sinako', 'sa', 'Sinandomeng', 'rice', 'sa', '14', 'gikan', 'sa', '18', 'ka', 'container', 'vans', ',', 'samtang', 'ang', 'upat', 'ka', 'vans', 'kay', 'dunay', 'sulod', 'nga', 'ceramic', 'tiles', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
6,055 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagkutlo', 'ning', 'balita', ',', 'ang', 'mga', 'opisyal', 'sa', 'BOC', 'ang', 'padayon', 'nag-abli', 'sa', 'ubang', 'mga', 'container', 'van', 'pagtino', 'sa', 'mga', 'sulod', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,056 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'interbyu', 'sa', 'Superbalita', 'Cebu', ',', 'si', 'Espinido', 'niingon', 'nga', 'P5', 'milyunes', 'nga', 'balor', 'sa', 'illegal', 'drugas', 'isakay', 'na', 'unta', 'og', 'barko', 'alang', '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, 3, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,057 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'supply', 'gikan', 'sa', 'grupo', 'sa', 'mga', 'Parojinog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
6,058 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ning', 'mao', 'nga', 'drug', 'personalities', 'nasikop', 'na', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
6,059 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'hinungdan', 'nga', 'gibakwi', 'sa', 'FDA', 'ang', 'license', 'to', 'operate', 'sa', 'Sanofi', 'Pasteur', 'alang', 'sa', 'pagbaligya', 'og', 'Dengvaxia', 'dinhi', '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, 3, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,060 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dinhi', 'sa', 'Sugbo', ',', 'moabot', 'sa', '323,779', 'ang', 'tinguha', 'nga', 'mabakunahan', 'og', 'Dengvaxia', 'dengue', 'vaccines', ',', 'apan', '49.34', 'porsyento', 'lang', 'kun', '159,766', 'ang', 'nabakunahan', 'batok', '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, 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 |
6,061 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bernadas', 'nitug-an', 'nga', 'human', 'niluwat', 'og', 'kamandoan', 'si', 'Health', 'Secretary', 'Francisco', 'Duque', 'pagpa-recall', 'sa', 'mga', 'Dengvaxia', 'vials', ',', 'gisugdan', 'na', 'nila', 'ang', 'pagpabalik', 'niini', 'gikan', 'sa', 'mga', 'rural', 'health', 'units', 'sa', 'mga', 'local', 'government', 'unit', 'sa', 'Sugbo', ',', 'ang', 'uban', 'niini', 'sa', 'sunod', 'tuig', 'na', 'ma-expire', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 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] | cebuaner |
6,062 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apil', 'sa', 'kamandoan', 'ang', 'pribadong', 'mga', 'doktor', 'nga', 'namaligya', 'sa', 'maong', 'bakuna', 'sa', 'ilang', 'mga', '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, 0, 0, 0, 0, 0] | cebuaner |
6,063 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bernadas', 'niangkon', 'nga', 'insakto', 'usab', 'ang', 'gibuhat', 'sa', 'Sanofi', 'nga', 'nag-isyu', 'kini', 'og', 'advisory', 'bahin', 'sa', 'Dengvaxia', 'kay', 'duna', 'kini', 'nakita', 'sa', 'pagtuon', 'apan', ',', 'ang', 'termino', 'ang', 'ilang', 'gikwestyon', 'tungod', 'kay', 'wa', 'kini', 'giklaro', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 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 |
6,064 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '“severe”', 'nga', 'klasipikasyon', 'gigamit', 'sa', 'DOH', 'niadtong', 'dekada', '90', 'nga', 'kon', 'itandi', 'kini', 'sa', 'kasamtangan', 'nga', 'klasipikasyon', 'nga', 'gigamit', 'karon', 'sa', 'buhatan', 'mahimo', 'nga', 'maubos', 'kini', 'sa', 'Grade', '1', 'ug', 'Grade', '2', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,065 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Grade', '1', ',', 'apil', 'sa', 'simtomas', 'kay', 'dunay', 'hilanat', 'ang', 'pasyente', ',', 'dunay', 'rashes', 'sa', 'panit', 'ug', 'magpositibo', 'sa', 'rapid', 'dengue', 'test', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,066 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Mayor', 'Vicente', 'Loot', 'nga', 'pasalamat', 'nila', 'kini', 'sa', 'Balaang', 'Bata', 'sa', 'tanang', 'grasya', 'nga', 'ilang', 'nadawat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,067 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niduaw', 'si', 'Loot', 'sa', 'Kapitolyo', 'ug', 'nihangyo', 'kang', 'Gobernador', 'Hilario', 'Davide', 'III', 'nga', 'tabangan', 'ang', 'ilang', 'lungsod', 'sa', 'pinansiyal', 'nga', 'paagi', 'sa', 'ilang', 'subling', 'pag-apil', 'sa', 'Sinulog', 'Grand', 'Parade', 'sa', '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, 1, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,068 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Loot', 'naghinam-hinam', 'ang', 'ilang', 'lungsod', 'nga', 'makapakita', 'sa', 'ilang', 'katakos', 'sa', 'indigay', 'sa', 'sayaw', 'sa', 'Sinulog', 'ug', 'makabalik', 'sa', 'pagpasundayag', 'sa', 'grand', 'stage', 'atol', 'sa', 'kapistahan', 'ni', 'Sr.', '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, 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, 7, 8, 8, 0] | cebuaner |
6,069 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'pa', 'hinuon', 'niya', 'ibutyag', 'kon', 'unsay', 'theme', 'sa', 'ilang', 'pasundayag', 'apan', 'sigun', 'sa', 'mayor', 'gitumong', 'aron', 'magpasalamat', 'sa', 'tanang', 'grasya', 'nga', 'ilang', 'nadawat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,070 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'sa', 'P500,000', 'ang', 'gihangyo', 'ni', 'Loot', 'sa', 'Kapitolyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,071 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'anaa', 'lang', 'sa', 'P250,000', 'ang', 'giparubahan', 'ni', 'Davide', 'nga', 'iabag', 'sa', 'contingent', 'sa', 'Daanbantayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 5, 0] | cebuaner |
6,072 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Sinulog', 'Foundation', ',', 'Inc.', 'nangayo', 'na', 'usab', 'og', 'financial', 'assistance', 'gikan', 'sa', 'Kapitolyo', 'nga', 'moabot', 'usab', 'sa', 'P3', '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, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,073 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'tabletas', ',', 'usa', 'ka', 'balde', 'nga', 'sealant', ',', 'kutsara', ',', 'tinidor', 'ug', 'ubang', 'ginagmay', 'nga', 'talinis', 'nga', 'butang', 'ang', 'nasakmit', 'diha', 'sa', 'pipila', 'ka', 'selda', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,074 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahibulong', 'ang', '256', 'ka', 'mga', 'babayeng', 'binilanggo', 'nga', 'sayo', 'silang', 'gipagawas', 'sa', 'ilang', 'selda', 'niadtong', 'Disyembre', '9', ',', '2017', 'sanglit', 'dili', 'kini', 'maoy', 'ilang', 'naandan', 'nga', 'routine', 'ug', 'gipatapok', 'sa', 'female', 'dormitory', 'ground', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
6,075 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'dihang', 'gisiksik', 'ang', 'matag', 'prisohan', 'nakakuha', 'ang', 'mga', 'operatiba', 'og', 'usa', 'ka', 'balde', 'nga', 'vulcaseal', ',', 'ballpens', ',', 'blade', 'nga', 'gigamit', 'og', 'ahit', ',', 'disposable', 'lighters', 'ug', 'mga', 'tambal', 'ilabi', 'na', 'niadtong', 'inmate', 'adunay', 'gibate', 'sa', 'panglawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,076 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nipasabot', 'si', 'Mabalod', 'nga', 'ang', 'maong', 'medisina', 'gikan', 'niadtong', 'binilanggo', 'nga', 'adunay', 'resita', 'gikan', 'sa', 'doctor', 'human', 'kini', 'nagpakonsulta', ',', 'samtang', 'ang', 'uban', 'nagtipig', 'og', 'mga', 'over-the-counter', 'drug', 'alang', 'sa', 'mga', 'simple', 'nga', 'sakit', 'nga', 'ilang', 'magamit', 'puhon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,077 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'ni', 'Mabalod', 'nga', 'ila', 'sab', 'nga', 'gi-regulate', 'ang', 'gidaghanon', 'sa', 'tambal', 'nga', 'ipasulod', 'sa', 'jail', 'facility', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,078 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sulod', 'sa', '20', 'ka', 'tuig', ',', 'gikinahanglan', 'ni', 'Yeldez', 'Beran', ',', '46', ',', 'nga', 'mohakot', 'og', 'binaldeng', 'tubig', 'aron', 'magamit', 'matag', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,079 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matag', 'adlaw', ',', 'moabot', 'ngadto', 'sa', 'P30', 'ang', 'magasto', 'ni', 'Beran', 'sanglit', 'sa', '10', 'ka', 'balde', 'sa', 'tubig', 'nga', 'gamiton', 'sa', 'iyang', 'pamilya', 'alang', 'sa', 'paglung-ag', ',', 'pagluto', ',', 'pampaligo', 'sa', 'iyang', 'mga', 'anak', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,080 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ang', 'balay', 'ni', 'Beran', 'sa', '150', 'ka', 'mga', 'pamilya', 'nga', 'makabenepisyo', 'unya', 'sa', 'maong', 'programa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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] | cebuaner |
6,081 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'sa', 'buntag', ',', 'opisyal', 'nga', 'gilusad', 'sa', 'MCWD', 'ang', 'programa', 'niini', 'uban', 'sa', 'mga', 'kaabag', 'niini', 'nga', 'mga', 'non-government', 'organization', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,082 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sobra', 'sa', '700', 'ka', 'mga', 'biktima', 'sa', 'sunog', 'nga', 'miigo', 'sa', 'Brgy.', 'Mantuyong', ',', 'Mandaue', 'sa', 'milabayng', 'tuig', 'ang', 'makakuha', 'unya', 'og', 'supply', 'sa', 'tubig', 'sa', 'di', 'pa', 'mag-Pasko', 'karong', '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, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,083 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'niini', ',', 'aduna', 'usay', 'ibutang', 'nga', 'fire', 'hydrants', 'ug', 'bansayon', 'ang', 'mga', 'residente', 'sa', 'disaster', 'preparedness', 'aron', 'makapangandam', 'kini', 'kalamidad', 'sama', 'sa', 'sunog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,084 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasuta', 'ning', 'mantalaan', 'nga', 'ang', 'WatSupCebu', 'giponduhan', 'og', 'P376', 'milyones', 'sa', 'nasud', 'sa', 'Netherlands', 'sa', 'lima', 'ka', 'tuig', 'niini', 'nga', 'privte-public', 'partnership', 'niini', 'diin', 'tuyo', 'nga', 'makatabang', 'sa', 'kinabuhi', 'sa', '80,000', 'ka', 'mga', 'Sugboanon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
6,085 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitampo', 'niini', 'og', 'P112', 'milyones', 'ang', 'MCWD', 'samtang', 'mao', 'sab', 'kini', 'ang', 'gibuhat', 'sa', 'French', 'NGO', 'nga', 'Eau', 'et', 'Vie', '(', 'E', '&', 'V', ')', 'ug', 'Netherlands', 'Red', 'Cross', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 0] | cebuaner |
6,086 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aron', 'mamahimong', 'sayon', 'lang', 'sab', 'sa', 'mga', 'benepisaryo', ',', 'ang', 'E', '&', 'V', '-', 'Tubig', 'Pag-asa', 'na', 'ang', 'ni-apply', 'alang', 'sa', 'service', 'connection', 'ngadto', 'sa', 'MCWD', 'ug', 'sila', 'na', 'ang', 'moapud-apod', 'niini', 'og', 'konektar', 'sa', 'mga', 'kabalayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,087 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aron', 'gaan', 'lang', 'sab', 'sa', 'bulsa', 'sa', 'mga', 'residente', ',', 'inadlaw', 'ang', 'P20', 'nga', 'koleksyon', 'nga', 'pangayuon', 'sa', 'MCWD', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 0] | cebuaner |
6,088 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', '2015', ',', 'duha', 'ka', 'mga', 'empleyado', 'sa', 'LTFRB', '7', 'ang', 'gitangtang', 'sa', 'trabaho', 'human', 'nagpositibo', 'sa', 'paggamit', 'og', '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, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,089 | 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', 'mahinungdanon', 'kaayo', 'ang', 'drug', 'test', 'kay', 'samtang', 'nanglimpyo', 'sila', 'sa', 'mga', 'badlungon', 'sa', 'sektor', 'sa', 'publikong', 'transportasyon', ',', 'kinahanglang', 'apilon', 'limpyo', 'ang', 'ilang', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,090 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mahibaw-an', 'ang', 'resulta', 'sa', 'drug', 'test', 'human', 'sa', 'duha', 'ka', '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] | cebuaner |
6,091 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'nahitabong', 'pagkasakmit', 'sa', 'mga', 'gadget', 'sa', 'Talisay', 'City', 'Jail', 'sama', 'sa', 'mobile', 'wifi', 'ug', 'Samsung', 'Tablet', 'nagtinguha', 'na', 'usab', 'ang', 'warden', 'nga', 'makabutang', 'silag', 'signal', 'jammer', '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, 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] | cebuaner |
6,092 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Supt.', 'Gil', 'Inopia', 'warden', 'sa', 'Talsiay', 'City', 'Jail', 'miingon', 'nga', 'ang', 'signal', 'jammer', 'maoy', 'sulbad', 'aron', 'mahunong', 'na', 'ang', 'bisan', 'unsang', 'transaksyon', 'sa', 'mga', 'piniriso', 'sa', 'gawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0] | cebuaner |
6,093 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'niini', 'giangkon', 'ni', 'Inopia', 'nga', 'maglisod', 'gayod', 'sila', 'sa', 'pagsumpo', 'sa', 'pagpalusot', 'sa', 'mga', 'butang', 'sama', 'sa', 'cellphone', 'hinungdan', 'nga', 'kanunay', 'silang', 'mohimog', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,094 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinungdan', 'nga', 'padayun', 'ang', 'negusyo', 'sa', 'ilegal', 'nga', 'drugas', 'bisan', 'paman', 'ug', 'naa', 'sulod', 'sa', 'prisuhan', 'ang', 'mga', 'ilegalista', 'tungod', 'sa', 'cellphone', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,095 | 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', 'aduna', 'na', 'sila', 'niini', 'kaniadto', 'apan', 'dili', 'tanang', 'area', 'sa', 'ilang', 'prisohan', 'ang', 'maigo', 'sa', 'signal', 'jammer', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,096 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'gawas', 'pa', 'mahal', 'usab', 'kayo', 'nga', 'lisod', 'sila', 'sa', 'pagpalit', 'ug', 'lisod', 'na', 'usab', 'kining', 'paliton', 'karong', 'panahona', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,097 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagkakaron', 'ang', 'ilang', 'gihimo', 'mao', 'lang', 'gyud', 'ang', 'pagpahigayun', 'ug', 'greyhound', 'operation', 'sa', 'higayon', 'nga', 'sila', 'makadawaty', 'og', 'kasayuran', 'nga', 'adunay', 'mga', 'kontrabando', 'nga', 'nakalusot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,098 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'greyhound', 'sa', 'miaging', 'semana', 'gawas', 'sa', 'mga', 'gadget', 'nakasakmit', 'usab', 'sila', 'og', 'shabu', 'ug', 'mga', 'shabu', 'paraphernalia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,099 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'Yamyam', 'Gaviola', ',', 'maoy', 'gitumbok', 'nga', 'tag-iya', 'sa', 'mga', 'butang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
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