Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,900 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipatawag', 'sa', 'counselor', 'ang', 'mga', 'gikinakanan', 'sa', 'duha', 'kinsa', 'maoy', 'nangayo', 'og', 'pakitabang', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,901 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Lunes', ',', 'gipangutana', 'sa', 'mga', 'panghitabo', 'ang', 'duha', 'ka', 'mga', 'biktima', 'sa', 'WCPD', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0] | cebuaner |
5,902 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'maong', 'adlaw', ',', 'nakadawat', 'og', 'text', 'si', 'Nicky', 'gikan', 'ni', 'Cue', 'nga', 'makighimamat', 'siya', 'pag-usab', 'tungod', 'kay', 'dako', 'siya', 'og', 'gusto', 'niini', ',', 'sigun', 'ni', 'Villaro', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,903 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tuod', 'man', ',', 'inubanan', 'sa', 'kapulisan', ',', 'nisugot', 'ang', 'biktima', 'nga', 'makighimamat', 'kini', 'ug', 'didto', 'na-entrrap', 'sud', 'sa', 'motel', 'sa', 'dakbayan', 'ug', 'nasikop', 'si', 'Cue', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 0] | cebuaner |
5,904 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sila', 'si', 'Princess', 'ug', 'Jordan', 'padayon', 'pa', 'nga', 'gipangita', '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, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,905 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'tuig', 'human', 'gipirmahan', 'ang', 'kasabutan', 'tali', 'sa', 'Dakbayan', 'ug', 'Probinsiya', 'sa', 'Sugbo', 'kabahin', 'sa', '93-1', 'nga', 'luna', ',', 'nangutana', 'karon', 'ang', 'konsehal', 'ubos', 'sa', 'Team', 'Rama', 'kon', 'unsa', 'na', 'ang', 'kalambuan', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,906 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Konsehal', 'Joel', 'Garganera', 'nagkanayon', 'nga', 'suportado', 'sila', 'sa', '93-1', ',', 'apan', 'nakapangutana', 'siya', 'kon', 'unsa', 'na', 'ang', 'kalambuan', 'niini', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,907 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Garganera', 'nga', 'basin', 'og', 'buhion', 'ra', 'ni', 'Osmeña', 'ang', 'isyu', 'sa', '93-1', 'pipila', 'ka', 'buwan', 'sa', 'di', 'pa', 'ang', 'piniliay', 'ug', 'himuon', 'kini', 'nga', 'pamaagi', 'aron', 'makahakot', 'og', 'boto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,908 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'usab', 'ang', 'pangutana', 'ni', 'Konsehal', 'Raymond', 'Garcia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,909 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'sa', 'konsehal', 'nga', 'niadtong', 'kampanya', 'sa', 'piniliay', 'pa', 'kini', 'nga', 'gipasalig', 'sa', 'mayor', 'diin', 'kaniadto', 'niingon', 'nga', 'sulod', 'sa', 'unom', 'ka', 'buwan', 'lang', 'kini', 'nga', 'sulbaron', 'apan', 'kutob', 'ra', 'diay', 'sa', 'MOA', 'signing', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,910 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'usab', 'niini', 'nga', 'usa', 'ning', 'pag-andam', 'sa', 'dokumento', 'sa', 'appraisal', 'alang', 'sa', 'mga', 'luna', 'sa', 'Dakbayan', 'ug', 'Probinsiya', 'nga', 'nakadugay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,911 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gusto', 'sa', 'duha', 'ka', 'local', 'government', 'unit', '(', 'LGU', ')', 'nga', 'di', 'ma', 'disallow', 'sa', 'COA', 'ang', 'ilang', 'himuong', 'pagbaylo', 'sa', 'luna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,912 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niini', 'nga', 'basin', 'sa', 'Enero', 'sila', 'makakuha', 'sa', 'approval', 'gikan', 'sa', 'COA', 'apan', 'kon', 'madugay', 'pa', 'gyud', 'kini', ',', 'mangita', 'sila', 'og', 'laing', 'legal', 'nga', 'pamaagi', 'aron', 'mas', 'madali', 'ang', 'pagbaylo', 'na', 'sa', 'luna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,913 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'nagkanayon', 'nga', 'di', 'siya', 'pabor', 'sa', 'anunsyo', 'sa', 'LTFRB', 'nga', 'ideklarar', 'nga', 'ilegal', 'ang', 'Angkas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
5,914 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mayor', 'nihatag', 'og', 'suporta', 'sa', 'mga', 'driver', 'sa', 'maong', 'app', 'kay', 'nakita', 'niya', 'nga', 'kini', 'sa', 'nakahatag', 'og', 'kaayuhan', 'sa', 'katawhan', 'labi', 'na', 'kadtong', 'way', 'kaugalingong', 'sakyanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,915 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'sab', 'niya', 'sa', 'iyang', 'Facebook', 'post', 'nga', 'ang', 'iyang', 'pagkontra', 'sa', 'LTFRB', 'di', 'ra', 'para', 'sa', 'Angkas', 'lang', ',', 'apan', 'alang', 'sa', 'mga', 'trabahante', ',', 'empleyado', ',', 'ug', 'mga', 'estudyante', 'nga', 'mas', 'mahatagan', 'og', 'maayong', 'serbisyo', 'sa', 'Angkas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 0, 0, 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, 3, 0] | cebuaner |
5,916 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'pa', 'sa', 'mayor', 'nga', 'mas', 'layo', 'sa', 'aksidente', 'ang', 'mga', 'driver', 'sa', 'Angkas', 'tungod', 'kay', 'sila', 'gibansay', 'ug', 'way', 'mga', 'pagpangabuso', 'sa', 'pagpliti', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,917 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'oras', 'ug', 'kahuot', 'sa', 'trapiko', 'ang', 'usa', 'sa', 'mga', 'problema', 'sa', 'publiko', 'apan', 'ang', 'Angkas', 'ang', 'nakitang', 'solusyon', 'ni', 'Osmeña', 'nga', 'mas', 'epektibo', 'batok', 'sa', 'trapiko', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,918 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitandi', 'ni', 'Osmeña', 'ang', 'normal', 'nga', 'mga', 'habalhabal', 'driver', 'nga', 'nag-iyahay', 'og', 'babag', 'sa', 'mga', 'interseksyon', 'sa', 'kalsada', 'kay', 'tungod', 'maghuwat', 'og', 'pasahero', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,919 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'sa', 'Angkas', ',', 'kinahanglan', 'ka', 'lamang', 'nga', 'motawag', 'ug', 'sila', 'moanha', 'na', 'diritso', 'ug', 'di', 'na', 'motambay', 'sa', 'mga', 'eskina', 'nga', 'makapahuot', 'sa', 'karsada', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,920 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'niingon', 'nga', 'ang', 'Angkas', 'naghatag', 'og', 'maayong', 'serbisyo', 'sa', 'mga', 'empleyado', 'ug', 'estudyante', 'nga', 'ganahang', 'di', 'ma', 'late', 'sa', 'ilahang', 'gitrabahoan', 'o', 'gieskuylahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,921 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'sab', 'sa', 'mayor', 'ngadto', 'sa', 'hearing', 'nga', 'ang', 'balaod', 'moserbisyo', 'sa', 'katawhan', ',', 'di', 'ang', 'katawhan', 'ang', 'moserbisyo', 'sa', 'balay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,922 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'matod', 'pa', 'sab', 'sa', 'LTFRB', ',', 'ang', 'Angkas', 'nagsundog', 'sa', 'Uber', 'ug', 'Grab', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 3, 0] | cebuaner |
5,923 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'nagplano', 'sa', 'paggamit', 'sa', 'Angkas', 'isip', 'modelo', 'sa', 'serbisyong', 'pampubliko', 'gamit', 'ang', 'motorsiklo', 'taliwa', 'sa', 'iyang', 'nakitang', 'panginahanglan', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,924 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pamahayag', 'ni', 'Osmeña', 'subay', 'kini', 'sa', 'desisyon', 'sa', 'DOJ', 'human', 'gibasura', 'ang', 'kaso', 'nga', 'gipasaka', 'niini', 'batok', 'sa', 'duha', 'ka', 'branch', 'sa', 'BDO', 'Unibank', 'sa', 'kakuwang', 'sa', 'ebidensiya', 'ug', 'probable', 'cause', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,925 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakighinabi', 'ni', 'Osmeña', 'sa', 'SunStar', 'Cebu', 'nagkanayon', 'kini', 'nga', 'dili', 'pa', 'siya', 'makahatag', 'og', 'komento', 'tungod', 'kay', 'wala', 'pa', 'siya', 'hingpit', 'nga', 'makabasa', 'sa', 'kopya', 'sa', 'desisyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,926 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'ang', 'oposisyon', 'nga', 'konsehal', 'ni', 'Osmeña', 'nagkanayon', 'nga', 'hunongon', 'na', 'sa', 'mayor', 'ang', 'isyu', 'ug', 'mo-move', 'on', 'na', 'kini', 'tungod', 'kay', 'dili', 'kini', 'maayo', 'sa', 'business', 'environment', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,927 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'pa', 'man', 'og', 'aduna’y', 'laing', 'legal', 'nga', 'option', 'si', 'Osmeña', 'apan', ',', 'matod', 'ni', 'Councilor', 'Raymond', 'Garcia', ',', 'nga', 'respetaran', 'sa', 'mayor', 'ang', 'desisyon', 'sa', 'DOJ', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
5,928 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nihulagway', 'usab', 'ang', 'duha', 'ka', 'mga', 'oposisy', 'nga', 'konsehal', 'nga', 'adunay', 'gidapigan', 'ang', 'mayor', 'ug', 'angay', 'lang', 'nga', 'mosunod', 'usab', 'kini', 'sa', 'balaod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,929 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'gipasabot', 'sa', 'mga', 'konsehal', 'ang', 'mando', 'niini', 'sa', 'SM', 'Seaside', 'City', 'nga', 'ipatangtang', 'ang', 'cube', 'structure', 'niini', 'isip', 'pagtuman', 'sa', 'setback', 'requirement', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,930 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', ',', 'dugang', 'ni', 'Garganera', 'nga', 'tan-awon', 'usab', 'unta', 'si', 'Osmeña', 'ang', 'luna', 'sa', 'Filinvest', 'Land', 'Inc.', '(', 'FLI', ')', ',', 'nga', 'anaa', 'atbang', 'sa', 'luna', 'sa', 'SM', 'kon', 'nisunod', 'ba', 'usab', 'kini', 'sa', 'requirements', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 1, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,931 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tulo', 'ka', 'mga', 'area', 'sa', 'Barangay', 'Babag', 'dakbayan', 'sa', 'Lapu-Lapu', 'ang', 'gitudlo', 'nga', 'firecracker', 'zone', ',', 'ang', 'dapit', 'diin', 'gitugotan', 'nga', 'makapabuto', 'og', 'firecrackers', 'ug', '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, 5, 6, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,932 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'karong', 'Disyembre', '16', ',', 'Sabado', 'sugdan', 'na', 'ang', 'pagpamaligya', 'og', 'pabuto', 'diin', 'ang', 'display', 'area', 'niini', 'mahimutang', 'daplin', 'sa', 'M.L.', 'Quezon', 'Highway', 'habig', 'sa', 'maong', 'barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
5,933 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'naasoyng', 'dapit', 'hawan', 'ug', 'layo', 'sa', '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] | cebuaner |
5,934 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'ni', 'Augusto', 'nga', 'sa', 'dili', 'pa', 'ang', 'adlaw', 'sa', 'pagpamaligya', 'ang', 'fire', 'department', 'mohimo', 'og', 'pakisusi', 'sa', 'dapit', 'aron', 'pagbadlong', 'niadtong', 'mga', 'maninda', 'og', 'barbeque', 'nga', 'dili', 'mopuwesto', 'sa', 'designated', 'display', 'area', 'sa', 'mga', 'pabuto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 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 |
5,935 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Babag', 'maoy', 'sentro', 'sa', 'firecracker', 'industry', 'sa', 'Lapu-Lapu', 'diin', 'ang', 'nag-unang', 'panginabuhi', 'sa', 'mga', 'taga-barangay', 'mao', 'ang', 'pagpanggama', 'og', 'mga', 'pabuto', 'ug', 'pyrotechnics', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,936 | 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', 'mga', 'okasyon', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,937 | 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', 'ug', 'ubang', 'matang', 'sa', 'disgrasya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,938 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'regional', 'director', 'sa', 'BFAR', ',', 'Allan', 'Poquita', ',', 'maoy', 'nangulo', 'sa', 'maong', 'kalihukan', 'nga', 'gihimo', 'sa', 'Talisay', 'Fish', 'Port', 'nga', 'gitambungan', 'usab', 'sa', 'mga', 'opisyal', 'sa', 'dakbayan', 'sa', 'Talisay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,939 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Poquita', 'nga', 'kini', 'gitumong', 'aron', 'dili', 'na', 'kinahanglan', 'pa', 'nga', 'mag-gamit', 'og', 'kahoy', 'aron', 'himuon', 'og', 'banka', 'sanglit', 'ginama', 'man', 'kini', 'sa', 'fiber', 'glass', ',', 'diin', 'ang', 'kantidad', 'sa', '40', 'ka', 'motor', 'banca', 'apil', 'na', 'ang', 'mga', 'makina', 'mokabat', 'sa', 'P1.4', '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, 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, 0, 0, 0, 0, 0, 0] | cebuaner |
5,940 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Poquita', 'nga', 'usa', 'sa', 'naka-ayo', 'sa', 'fiber', 'glass', 'nga', 'pum', 'boat', 'lisod', 'nga', 'malunod', 'kon', 'adunay', 'mga', 'katalagman', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,941 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'kaihukan', 'sa', 'wapa', 'gipanghatag', 'ang', 'mga', 'pumpboat', 'ang', 'mga', 'nakabenipisyo', 'gitudloan', 'una', 'unsaon', 'sa', 'pag-atiman', 'sa', 'motor', 'banca', ',', 'lakip', 'sa', 'gihatag', 'mao', 'ang', 'makina', 'ug', 'pukot', 'nga', 'ilang', 'magamit', 'sa', 'panagat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,942 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'presidente', 'sa', 'Talisay', 'Fisher', 'Folks', 'Federation', 'nga', 'si', 'Dennis', 'Reyes', 'niingon', 'nga', 'dako', 'ang', 'ilang', 'pasalamat', 'sa', 'BFAR', 'nga', 'mitabang', 'kini', 'sa', 'ilang', 'panginabuhian', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,943 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hangyo', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'giaprobahan', 'sa', 'Kongreso', 'kagahapon', 'sud', 'sa', 'kapin', 'sa', 'duha', 'ka', 'semana', 'sa', 'dili', 'pa', 'mohupas', 'ang', 'unang', 'paglugway', 'sa', 'Martial', 'law', 'sa', 'Mindanao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 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, 5, 0] | cebuaner |
5,944 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'habig', 'ang', 'Senado', ',', 'sa', 'botasyon', '14', 'ang', 'pabor', 'ug', 'upat', 'lang', 'ang', 'supak.', 'Sa', 'House', 'of', 'Representatives', ',', '226-23', 'ang', 'resulta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0] | cebuaner |
5,945 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Lorenzana', 'nanalipod', 'sa', 'usa', 'ka', 'tuig', 'nga', 'paglugway', 'sa', 'martial', 'human', 'ang', 'rebelyon', 'wala', 'pa', 'hingpit', 'napapas', 'bisan', 'pagdeklarar', 'ni', 'Duterte', 'nga', '‘liberated’', 'na', 'ang', 'Marawi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 0, 0, 0, 5, 0] | cebuaner |
5,946 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'suwat-hangyo', 'sa', 'Senado', 'ug', 'Kongreso', ',', 'si', 'Duterte', 'nitumbok', 'sa', 'hulga', 'gikan', 'sa', 'ISIS-inspired', 'groups', 'ug', '“communist', 'terrorists.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,947 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'maoy', 'nakita', 'ni', 'FCInsp.', 'Josephus', 'Alburo', 'nga', 'dakong', 'hagit', 'kon', 'motubag', 'ang', 'iyang', 'buhatan', 'og', 'alarma', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,948 | 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', ',', 'nihimo', 'og', 'simulation', 'drill', 'ang', 'ang', 'BFP', 'sa', 'pagtubag', 'og', 'sunog', 'nga', 'miigo', 'sa', 'Zone', 'Ahos', ',', 'Brgy.', 'Paknaan', 'sa', 'dakbayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0] | cebuaner |
5,949 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'maong', 'drill', ',', 'nakadawat', 'og', 'tawag', 'sa', 'alarma', 'ang', 'BFP', 'sa', 'dakong', 'residential', 'area', 'sa', 'Paknaan', ',', 'alas', '10:09', 'sa', 'buntag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,950 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', 'nga', 'sayop', 'ang', 'panghunahuna', 'sa', 'mga', 'motorista', 'og', 'drayber', 'nga', 'di', 'mohatag', 'sa', 'dan', 'sa', 'mga', 'emergency', 'vehicles', 'kon', 'di', 'ang', 'ilang', 'mga', 'balay', 'ang', 'girespondehan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,951 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paambit', 'niya', 'sa', 'mga', 'tigbalita', 'nga', 'ang', 'kalihukan', 'nga', 'gihimo', 'uban', 'sa', 'suporta', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'ug', 'ni', 'Mayor', 'Luigi', 'Quisumbing', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0] | cebuaner |
5,952 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tuyo', 'niini', 'aron', 'mas', 'mapalambo', 'ang', 'serbisyo', 'sa', 'bombero', 'ug', 'fire', 'volunteers', 'panahon', 'sa', 'emerhensiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,953 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Napalgan', 'ang', 'ilegal', 'nga', 'pagpamaligya', 'og', 'stingrays', 'o', 'pagi', 'nga', 'naa', 'sa', '4.8', 'kilos', 'sa', 'Miyerkules', 'sa', 'buntag', 'sa', 'El', 'Filibusterismo', 'St.', ',', 'Ermita', ',', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
5,954 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'ang', 'mga', 'naninda', 'nga', 'sila', 'Maricel', 'Garcia', ',', 'nagpuyo', 'sa', 'maong', 'lugar', 'nga', 'adunay', 'baligya', 'nga', '1.6', 'kilos', 'nga', 'Stingrays', 'ug', 'si', 'Marvin', 'Manatas', ',', 'taga', 'Escano', ',', 'Carbon', 'nga', 'dunay', '3.2', 'kilos', 'nga', 'Stingrays', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,955 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Dr.', 'Alice', 'Utang', 'nga', 'gikan', 'ang', 'mga', 'supply', 'nga', 'Stingrays', 'sa', 'Zamboanga', 'ug', 'gibawal', 'gyud', 'kini', 'ang', 'pagpamaligya', 'og', 'pagi', 'nga', 'endangered', 'species', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,956 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanunay', 'ang', 'pag-inspeksyon', 'sa', 'manindahay', 'dihang', 'dapita', 'ug', 'sukad', 'sa', 'buntag', ',', 'udto', 'ug', 'hangtod', 'sa', 'kaadlawon', 'ang', 'dyuti', 'ug', 'pagbantay', 'sa', 'mga', 'manindahay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,957 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', 'nga', 'angay', 'lang', 'nga', 'tabangan', 'ang', 'kapulisan', 'mahitungod', 'sa', 'legal', 'problems', 'tungod', 'kay', 'usahay', 'ang', 'mga', 'pulis', 'magpanuko', 'sa', 'paghimo', 'sa', 'ilang', 'trabaho', 'hilabi', 'na', 'kon', 'dagkong', 'mga', 'tawo', 'o', 'mga', 'adunahan', 'ang', 'ilang', 'mabangga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,958 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'ni', 'Garganera', 'nga', 'makatabang', 'ang', 'P50,000', ',', 'apan', 'gihimug-atan', 'niini', 'nga', 'di', 'na', 'kinahanglan', 'pa', 'dunay', 'rekisitos', 'aron', 'ma-release', 'ang', 'maong', 'kantidad', 'sama', 'niining', 'makapatay', 'og', 'kriminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,959 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Garcia', ',', 'sa', 'iyang', 'bahin', ',', 'nagkanayon', 'nga', 'kon', 'ang', 'intensiyon', 'sa', 'mayor', 'mao', 'ang', 'pagtabang', 'sa', 'kapulisan', 'sa', 'legal', 'problem', ',', 'inay', 'mohatag', 'og', 'P50,000', 'nganong', 'dili', 'nalang', 'moumol', 'og', 'special', 'team', 'sa', 'mga', 'abogado', 'ang', 'Siyudad', 'aron', 'pagtubag', 'niining', 'mga', 'insidente', 'nga', 'makiha', 'ang', 'mga', 'pulis', 'kay', 'nakapusil', 'samtang', 'on-duty', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,960 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihulagway', 'ni', 'Garcia', 'ang', 'maong', 'kantidad', 'nga', 'di', 'paigo', 'aron', 'pagbayad', 'sa', 'serbisyo', 'sa', 'abogado', 'nga', 'kuhaon', 'hilabi', 'na', 'kon', 'criminal', 'case', 'kay', 'mas', 'lisod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
5,961 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'kawani', 'sa', 'Cebu', 'Provincial', 'Detention', 'and', 'Rehabilitation', 'Center', '(', 'CPDRC', ')', 'di', 'tugotan', 'nga', 'mo-leave', 'o', 'mo-absent', 'sa', 'Disyembre', '25', 'ug', '26', 'atol', 'sa', 'pagpahigayon', 'sa', 'family', 'day', 'alang', 'sa', 'mga', 'piniriso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,962 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tag-tulo', 'ka', 'mga', 'sakop', 'sa', 'pamilya', 'ang', 'mahimong', 'makaduaw', 'sa', 'usa', 'ka', 'inmate', 'atol', 'sa', 'family', 'day', 'sa', 'CPDRC', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,963 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Anaa', 'na', 'karon', 'sa', 'kapin', '3,000', 'ang', 'gidaghanon', 'sa', 'mga', 'piniriso', 'sa', 'CPDRC', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,964 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'matag', 'usa', 'nila', 'dunay', 'tulo', 'ka', 'mga', 'bisita', ',', 'hayan', 'moabot', 'sa', 'kapin', '9,000', 'ka', 'mga', 'tawo', 'ang', 'magdasok', 'unya', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,965 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niini', ',', 'gitan-aw', 'sa', 'HR', 'sa', 'Kapitolyo', 'ang', 'pagsiguro', 'nga', 'dunay', 'igong', 'personnel', ',', 'nga', 'makatabang', 'sa', 'pagpatunhay', 'sa', 'kahapsay', 'ug', 'kalinaw', 'sa', 'pasilidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,966 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitambagan', 'na', 'ni', 'Provincial', 'Human', 'Resource', 'Office', 'Bhobby', 'Nacorda', 'ang', 'tagdumala', 'sa', 'CPDRC', 'nga', 'way', 'tugotan', 'nga', 'mag-leave', 'o', 'kaha', 'mo-absent', 'sa', 'mga', 'kawani', 'sa', 'prisohan', 'sa', 'maong', 'mga', '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, 1, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,967 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'molapas', 'niini', 'mapahamtangan', 'og', 'disciplinary', 'action', 'ug', 'deduction', 'sa', 'ilang', 'sweldo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,968 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'laing', 'bahin', ',', 'gipahibawo', 'ni', 'Nacorda', 'nga', 'dunay', 'dugang', '24', 'ka', 'mga', 'watchmen', 'ang', 'ila', 'na', 'unyang', 'ipakatap', 'sa', 'CPDRC', 'gihapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 0, 0] | cebuaner |
5,969 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Magdugang', 'kini', 'sa', 'unang', '15', 'nga', 'kasamtangang', 'nagtrabaho', 'na', 'sa', 'pasilidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,970 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Enero', '4', ',', '2017', ',', 'si', 'Osmeña', 'nipasaka', 'sa', 'mga', 'kasong', 'kalapasan', 'sa', 'Article', '318', 'sa', 'Revised', 'Penal', 'Code', 'kun', 'pagpanglimbong', ',', 'ingon', 'man', 'paglapas', 'sa', 'Articles', '171', 'ug', '171', 'kun', 'pagpalsipikar', 'og', 'publiko', 'ug', 'pribadong', 'dokumento', 'ug', 'paglapas', 'sa', 'City', 'Tax', 'Ordinance', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,971 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'kaso', ',', 'gihimug-atan', 'ni', 'Osmeña', 'nga', 'lisod', 'katuohan', 'nga', 'ang', 'sanga', 'sa', 'BDO', 'sa', 'dan', 'Magallanes', ',', 'Siyudad', 'sa', 'Sugbo', 'mokita', 'lang', 'og', 'P400,057.22', 'sa', 'operasyon', 'niini', 'sa', '2016', 'kun', 'tag', 'P33,330', 'matag', '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, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,972 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gimandoan', 'si', 'acting', 'City', 'Treasurer', 'Tessie', 'Camarillo', 'sa', 'pag-imbestigar', 'niini', 'niadtong', 'Nobiyembre', 'sa', 'miaging', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,973 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'imbestigasyon', 'ni', 'Camarillo', ',', 'iyang', 'nakita', 'nga', 'sa', '2015', ',', 'ang', 'gross', 'sales', 'sa', 'mao', 'nga', 'bangko', 'moabot', 'og', 'P4,449,824.94', 'nga', 'gipilo', 'og', 'kanapulo', 'ang', 'gidak-on', 'itandi', 'sa', 'ilang', 'sales', 'sa', '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, 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] | cebuaner |
5,974 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pinasikad', 'niini', ',', 'matod', 'ni', 'Camarillo', 'ngadto', 'sa', 'iyang', 'report', 'ni', 'Osmeña', ',', 'ang', 'bangko', 'ni', '“underdeclared”', 'sa', 'ilang', 'kita', 'kay', 'imposible', 'sa', 'usa', 'ka', 'dakong', 'bangko', 'sama', 'sa', 'BDO', 'nga', 'mokita', 'og', 'ingon', 'niani', 'ka', 'ubos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 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] | cebuaner |
5,975 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'hukom', ',', 'si', 'Gingoyon', 'niingon', 'nga', 'wa', 'makapakita', 'si', 'Osmeña', 'og', 'mga', 'dokumento', 'isip', 'ebidensya', 'nga', 'nanglimbong', 'ang', 'mga', 'opisyal', 'sa', 'bangko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,976 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'gibasehan', 'sa', 'reklamo', 'mao', 'ra', 'ang', 'application', 'form', 'alang', 'sa', 'business', 'permit', 'nga', 'giingong', 'gipalsipikar', 'tungod', 'sa', 'kagamay', 'ra', 'sa', 'gross', 'sales', 'nga', 'nakabutang', 'sa', 'ilang', 'application', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,977 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'hukom', ',', 'nga', 'ang', 'pagduda', 'di', 'igong', 'basehanan', 'sa', 'pagkombikto', 'sa', 'mga', 'akusado', 'kay', 'gikinahanglan', 'nga', 'kini', 'pamatud-an', 'base', 'sa', 'lig-ong', 'mga', 'ebidensya', 'nga', 'wa', 'mapakita', 'ning', 'higayuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,978 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'usab', 'mapamatud-i', 'sa', 'kaso', 'nga', 'dunay', 'panagkunsabo', 'sa', 'mga', 'opisyal', 'sa', 'banko', 'sa', 'paglimbong', 'sa', 'Siyudad', ',', 'dugang', 'sa', 'hukom', 'hinungdan', 'nga', 'gibasura', 'kini', 'ni', 'Gingoyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,979 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kapulisan', 'sa', 'dakbayan', 'sa', 'Talisay', 'nipasaka', 'na', 'og', 'kasong', 'attempted', 'murder', 'sa', 'giingong', 'utok', 'sa', 'pagpangrakrak', 'sa', 'Talisay', 'City', 'Jail', 'makaupat', 'ka', 'mga', 'higayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 5, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
5,980 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipatay', 'sa', 'miaging', 'semana', 'si', 'Arjianne', 'Bacus', ',', 'taga', 'sitio', 'Crusher', ',', 'Brgy.', 'Lawaan', '3', ',', 'dakbayan', 'sa', 'Talisay', 'human', 'makapyansa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
5,981 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Villamater', 'nga', 'bisan', 'kon', 'gipatay', 'si', 'Bacus', 'sa', 'duha', 'ka', 'mga', 'tawo', 'nga', 'nagmotorsiklo', ',', 'nakaisyu', 'na', 'kini', 'og', 'affidavit', 'uban', 'sa', 'lain', 'pang', 'duha', 'ka', 'mga', 'kauban', 'kon', 'si', 'kinsa', 'ang', 'nagsugo', 'kanila', 'sa', 'pagpangrakrak', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,982 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Gaviola', 'maoy', 'ilang', 'gitudlo', 'nga', 'naghatag', 'o', 'nagkontrata', 'kanila', 'nga', 'himuon', 'ang', 'pagpamusil', 'sa', 'Talisay', 'City', 'Jail', 'bugti', 'sa', 'P10,000', '.'] 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0] | cebuaner |
5,983 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ila', 'usab', 'kining', 'gilakip', 'sa', 'pagkiha', 'og', 'attempted', 'murder', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,984 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Villamater', 'nga', 'si', 'Gaviola', 'usa', 'ka', 'drug', 'personality', 'sa', 'Talisay', 'apan', 'wa', 'pa', 'madakpi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0] | cebuaner |
5,985 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Yamyam', 'nakuhaan', 'og', 'shabu', 'ug', 'mga', 'gadget', 'atol', 'sa', 'ilang', 'greyhound', 'operation', 'niadtong', 'miaging', '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, 0, 0, 0, 0, 0, 0] | cebuaner |
5,986 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'magtuo', 'si', 'Villamater', 'nga', 'si', 'Gaviola', 'ang', 'mastermind', 'tungod', 'kay', 'posibleng', 'aduna', 'pay', 'labaw', 'nga', 'nagsugo', 'ug', 'siya', 'ra', 'ang', 'gihatagan', 'og', 'kuwarta', 'ug', 'nangitag', 'tawo', 'nga', 'motrabaho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,987 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Idili', 'na', 'sa', 'umaabot', 'Pista', 'Senyor', 'ang', 'pagpalabi', 'og', 'pagpamutang', 'og', 'banderitas', 'ingon', 'man', 'mga', 'poster', 'ug', 'tarpaulin', 'aron', 'masiguro', 'nga', 'di', 'kini', 'makaali', 'sa', 'panan-aw', 'sa', 'mga', 'motambong', 'ingon', 'man', 'alang', 'usab', 'sa', 'seguridad', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,988 | 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', 'ang', 'kahapsay', 'ug', 'kalinaw', 'mao', 'ang', 'ilang', 'tumong', 'ug', 'nagdasig', 'sa', 'katawhan', 'nga', 'limitahan', 'ang', 'mga', 'banderitas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,989 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'palabing', 'pagbutang', 'og', 'banderitas', 'mamahimong', 'sagabal', 'sa', 'mga', 'security', 'personnel', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,990 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gilimitahan', 'sab', 'ang', 'pagplastada', 'sa', 'mga', 'posters', 'subay', 'sa', 'mga', 'lugar', 'kon', 'diin', 'asa', 'ang', 'rota', 'sa', 'Sinulog', 'ug', 'prosesyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,991 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'nagpositibo', 'ang', '52', 'ka', 'mga', 'kawani', 'sa', 'Cebu', 'City', 'Hall', 'ug', '171', 'ka', 'empleyado', 'gikan', 'sa', '23', 'ka', 'mga', 'barangay', 'ug', 'gikan', 'sa', 'tanang', 'tanod', 'ug', 'loaders', 'sa', 'dakbayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,992 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'kon', 'regular', 'employee', ',', 'huwaton', 'ang', 'confirmatory', 'test', 'ingon', 'man', 'moagi', 'og', 'proseso', 'kon', 'taktakon', 'kini', 'sa', 'serbisyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,993 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'labing', 'uwahi', 'nga', 'drug', 'test', 'sa', 'Cosap', ',', 'wa', 'nagpositibo', 'sa', '244', 'ka', 'mga', 'day', 'care', 'teachers', 'nga', 'ilang', 'gi-test', 'niadtong', 'Lunes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,994 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'usa', 'ang', 'nagpositibo', 'sa', 'Bureau', 'of', 'Jail', 'Management', 'and', 'Penology', '(', 'BJMP', ')', 'gikan', 'sa', '129', 'ka', 'mga', 'personnel', 'nga', 'gi-test', '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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,995 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sukad', 'Enero', 'ning', 'tuiga', 'hangtod', 'ning', 'kasamtangan', ',', 'niabot', 'na', 'sa', '2,302', 'ka', 'mga', 'kawani', 'ang', 'na', 'drug', 'test', 'sa', 'City', 'Hall', ';', '515', 'sa', 'nagkadaiyang', 'public', 'schools', ';', '1,612', 'sa', 'mga', 'empleyado', 'sa', '23', 'ka', 'mga', 'barangay', ';', 'ug', '1,932', 'sa', 'mga', 'tanod', 'ug', 'loaders', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,996 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'usab', 'ni', 'Lao', 'nga', 'anaa', 'sa', 'unom', 'lang', 'ang', 'nagpositibo', 'nga', 'regular', 'employees', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,997 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Marlon', 'Butanas', ',', 'hingkod', ',', 'wa', 'igkita', 'atol', 'sa', 'ronda', 'sa', 'iyang', 'balay', 'ug', 'establisemento', 'nga', 'lutoanan', 'og', 'pan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,998 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nasikop', 'ang', 'nagsilbing', 'private', 'security', 'guard', 'nga', 'si', 'Ricardo', 'Ramos', ',', 'kinsa', 'nakuhaan', 'og', '.22', 'caliber', 'rifle', 'ug', '.45', 'caliber', 'pistol', 'nga', 'dunay', 'daghang', 'mga', 'bala', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,999 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'joint', 'team', 'nakarekuber', 'sa', 'buhatan', 'sa', 'mga', 'pan', 'sa', 'usa', 'ka', 'KG-9', 'sub', 'machine', 'pistol', 'nga', 'dunay', 'magazine', 'nga', 'puno', 'sa', 'mga', 'bala', 'ug', 'kalibre', '.45', 'nga', 'pistola', 'ug', 'daghang', 'mga', 'bala', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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