Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,100 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'paksyon', 'sa', 'PDP-Laban', ',', 'nga', 'gipangulohan', 'ni', 'kanhi', 'Presidente', 'Rodrigo', 'Duterte', ',', 'miuyon', 'sa', 'pag-isyu', 'sa', 'usa', 'ka', 'resolusyon', 'sa', 'partido', 'aron', 'ipahayag', 'ang', 'hingpit', 'nga', 'suporta', 'alang', 'sa', 'pagbag-o', 'sa', 'Charter', ',', 'giingon', 'ni', 'PDP-Laban', 'Secretary', 'General', 'Melvin', 'Matibag', 'kaniadtong', 'Martes', ',', 'Marso', '21', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 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, 3, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0] | cebuaner |
5,101 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'Lunes', ',', 'Marso', '20', ',', 'gihatagan', 'sa', 'House', 'Committee', 'on', 'Ethics', 'and', 'Privileges', 'si', 'Negros', 'Oriental', 'Rep.', 'Arnolfo', 'Teves', 'Jr.', 'sa', '24', 'oras', 'aron', 'personal', 'nga', 'moatubang', 'sa', 'panel', 'aron', 'ipasabot', 'ang', 'iyang', 'pagkawala', 'nga', 'walay', 'opisyal', 'nga', 'pagtugot', 'gikan', 'sa', 'Balay', 'sa', 'mga', 'Representante.', 'matod', 'ni', 'COOP-NATCO', 'Party-list', 'Rep.', 'Felimon', 'Espares', 'sa', 'mga', 'tigbalita.', 'dugang', 'pa', 'niya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 5, 6, 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, 3, 4, 4, 4, 0, 0, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,102 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Road', 'to', '3rd', 'floor', 'Ambot', 'oy', '!', '!', 'Di', 'nko', 'ma', 'gets', 'uban', 'artist', 'ngano', 'pugson', 'ug', 'di', 'kaya', '!', 'Ngano', 'pugson', 'ug', 'dili', 'sure..'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,103 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sukad', 'sa', '2017', ',', 'si', 'kanhi', 'Senador', 'Leila', 'de', 'Lima', 'napriso', 'tungod', 'sa', 'mga', 'kaso', 'sa', 'droga', ',', 'bisan', 'kung', 'daghang', 'mga', 'yawe', 'nga', 'saksi', 'batok', 'kaniya', 'ang', 'nagbakwi', 'sa', 'ilang', 'testimonya.', 'matud', 'pa', 'ni', 'Justice', 'Secretary', 'Boying', 'Remulla', 'sa', 'mga', 'tigbalita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0] | cebuaner |
5,104 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['"', 'Bisan', 'unsa', 'pa', 'ang', 'mahitabo', ',', 'ayaw', 'jud', 'pagmata', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,105 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['kakapoyin', 'pero', 'hindi', 'uundang', 'at', 'susurrender'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0] | cebuaner |
5,106 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['chamba', 'nalang', 'gyud', 'maka', 'kita', 'ta', 'og', 'partner', ',', 'nga', 'dili', 'maibog', 'sa', 'lain'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,107 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Grabeng', 'hinilotay', 'uy.', 'Ma-bun-og', 'man', 'pod', 'ang', 'bukog', 'ani'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,108 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lisud', 'kaayo', 'e', 'pretend', 'na', 'Okey', 'ka', 'labi', 'nag', 'di', 'mao', 'ang', 'ticket', 'imong', 'nadala', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,109 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugay', 'na', 'nakong', 'gipraktis', 'ang', 'duck', ',', 'cover', 'ug', 'hold', ',', 'maghidga', 'raman', 'diay', 'ko', 'basta', 'maglinog'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,110 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TOXIC', 'FILIPINO', 'CULTURE', '!', ''', 'Nag-post', 'og', 'hugot', 'ang', 'TV', 'host', 'og', 'karon', 'Binibining', 'Pilipinas', 'candidate', 'nga', 'si', 'Harlene', 'Nicole', 'Budol', 'o', 'mas', 'ila', 'sa', 'pangalan', 'nga', '"', 'Hipon', 'Girl', '"', 'sa', 'iyang', 'Instagram', 'account', 'kaniadtong', 'Mayo', '18.', 'Ingon', 'sad', 'siya', 'sa', 'iyang', 'post', 'nga'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,111 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Basta', 'putot', ',', 'gwapa'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0] | cebuaner |
5,112 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanang', 'stress', 'na', 'kaayo', 'ka', 'sa', 'kinabuhi.', 'pero', 'gwapa', 'lang', 'japon', 'ka.', '#', 'HugotBisaya'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,113 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanang', 'giihaw', 'naka', 'pero', 'attitude', 'gyapon', 'ka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,114 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BITAW', ',', 'BITAW', 'Famous', 'Cebuana', 'moto', 'vlogger', 'Jet', 'Lee', 'has', 'a', 'piece', 'of', 'funny', 'yet', 'witty', 'advice', 'to', 'her', 'followers.', 'she', 'wrote.', 'Sakto', 'nuh', '?', 'Bisaya', 'ra', 'gyod', 'ang', 'makasabot', '!', ':', 'Jet', 'Lee'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 0, 0, 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, 1, 2] | cebuaner |
5,115 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'tamo', 'pakyason', '“Daghang', 'salamat', 'sa', 'paghatag', 'sa', 'ako', 'og', 'bag-ong', 'higayon', 'nga', 'ipadayon', 'ang', 'akong', 'nasugdan.', 'Ang', 'resulta', 'sa', 'eleksiyon', 'kay', 'pruweba', 'sa', 'inyong', 'pagsalig', 'sa', 'akong', 'kaya', 'nga', 'mahimo'g', 'boses', 'ninyo', 'sa', 'Senado.', 'Dili', 'tamo', 'pakyason.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 0, 0, 0] | cebuaner |
5,116 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kasuway', 'sad', 'ko', 'ani', 'uyyy', ',', 'kaog', 'cracklings', 'sa', 'daplin'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,117 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kaniadtong', 'Martes', ',', 'Mayo', '10', ',', 'ang', 'beteranong', 'broadcast', 'journalist', 'sa', 'GMA-7', 'nga', 'si', 'Mike', 'Enriquez', 'nipahinumdom', 'sa', 'mga', 'mananaug', 'sa', 'Election', '2022', 'nga', 'bantayan', 'sila', 'sa', 'publiko', ',', 'ug', 'nakaani', 'kini', 'og', 'mga', 'positibong', 'reaksyon', 'sa', 'mga', 'Twitter', 'users', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0] | cebuaner |
5,118 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['OH', ',', 'NAKUYAWAN', 'KA', ',', ''NO', '?', '!', 'Sa', 'bag-ohay', 'nga', 'mga', 'adlaw', ',', 'ang', 'kanhi', 'action', 'star', 'nga', 'si', 'Robin', 'Padilla', 'ug', 'ang', 'sikat', 'nga', ''90s', 'rapper', 'nga', 'si', 'Andrew', 'E.', 'Andrew', 'nakuyawan', 'sa', 'kadaghanan', 'sa', 'iyang', 'kadaugan', 'ug', 'makanunayon', 'nga', 'performance', 'sa', 'mga', 'campaign', 'rallies', 'sa', 'UniTeam', '—', 'pareho', 'silang', 'midaog', 'sina', 'President', '-', 'elect', 'Bongbong', 'Marcos', 'ug', 'Vice', 'President', '-', 'elect', 'Sara', 'Duterte.', 'Si', 'Robin', ',', 'uban', 'sa', 'wa', 'niya', 'damha', 'nga', 'pamunoan', 'sa', 'mga', 'bag-ong', 'napiling', 'senador', ',', 'maoy', 'hinungdan', 'nga', 'nahimo', 'siyang', 'Senator-elect', 'Robin', 'Padilla', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
5,119 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CHILLIN', ''AND', 'SWIMMIN', ''', 'Nag-pahayahay', 'sa', 'daplin', 'sa', 'dagat', 'human', 'sa', 'pipila', 'ka', 'binulang', 'busy', 'sa', 'campaign', 'period', 'si', 'Presumptive', 'Vice', 'President', 'Sara', 'Duterte-Carpio', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 2, 0] | cebuaner |
5,120 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GIHIGUGMA', 'TA', ''MO', '!', '"', 'Daghang', ',', 'daghang', 'salamat', 'sa', 'matag', 'Pilipino', 'nga', 'misalig', 'ug', 'misuporta', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0] | cebuaner |
5,121 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ANTIQUE', ':', 'KULTURA.', 'TRADISYON.', 'SINING.', 'Giimbita', 'ni', 'Loren', 'Legarda', 'ang', 'Antique', 'Artists', 'Fair', 'nga', 'nagtumong', 'sa', 'pag-atiman', 'sa', 'kultura', 'sa', 'Antique', 'aron', 'mas', 'mo', 'daghan', 'ang', 'turismo', 'sa', 'probinsiya', 'ug', 'mapreserbar', 'ang', 'tradisyon', 'sa', 'Antiqueño', 'alang', 'sa', 'mga', 'sunod', 'nga', 'henerasyon.', 'Gisuportahan', 'sad', 'ni', 'Loren', 'ang', 'School', 'of', 'Living', 'Traditions', 'sa', 'katutubong', 'Pantad', 'Ati', 'sa', 'Tobias', 'Fornier', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 4, 4, 4, 0, 0, 5, 6, 0, 1, 2, 0] | cebuaner |
5,122 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Loren', 'Loves', 'Laguna', 'Daghang', 'salamat', 'sa', 'atong', 'mga', 'kababayan', 'sa', 'San', 'Pedro', ',', '#', 'Laguna', 'sa', 'inyong', 'init', 'nga', 'pagdawat', '!', 'Lipay', 'kaayo', 'ko', 'nga', 'makilala', 'ang', 'atong', 'mga', 'Lagunanay', ',', 'kabatan-onan', ',', 'senior', 'citizens', 'ug', 'uban', 'pang', 'mga', 'kababayan', ',', 'sa', 'bag-o', 'lang', 'nakong', 'pagbisita', 'ni', 'Cathy', 'Gonzaga', ',', 'sa', 'imbitasyon', 'nila', 'Atty', 'Ann', 'Matibag', 'ug', 'Secretary', 'Melvin', 'Matibag.', 'Makalipay', 'nga', 'mahibal-an', 'nga', 'ang', 'San', 'Pedro', ',', 'gitawag', 'nga', '"', 'human', 'capital', '"', 'sa', 'Laguna', 'tungod', 'sa', 'ilang', 'pagdayeg', 'sa', '#', 'edukasyonatkaalaman', 'Busa', 'malipayon', 'kong', 'ipaambit', 'nga', 'gawas', 'sa', 'Libreng', 'Edukasyon', 'sa', 'Kolehiyo', 'nga', 'atong', 'gipatuman', ',', 'atong', 'gipasiugdahan', 'usab', 'ang', 'One', 'Tablet', ',', 'One', 'Student', 'Bill', 'aron', 'mas', 'makatabang', 'pa', 'sa', 'mga', 'kabatan-onan.', 'Anaa', 'usab', 'ang', '#', 'MSME', 'nga', 'balaod', 'alang', 'sa', 'gagmay', 'nga', 'mga', 'negosyo', 'ug', 'ang', 'Universal', 'Health', 'Care', 'Law', 'alang', 'sa', 'mas', 'barato', 'nga', 'pagtambal.', 'Makasalig', 'ka', 'nga', 'ako', 'imong', 'partner', 'para', 'mas', 'molambo', 'pa', 'ang', 'Laguna', 'kay', 'duol', 'gyud', 'ka', 'sa', 'akong', 'kasingkasing.', 'Salamat', 'pag-usab❤️'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 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] | cebuaner |
5,123 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Bisag', 'unsa', 'atong', 'partido', 'o', 'kulay', ',', 'nagkasinabot', 'ta', 'nga', 'kinahanglan', 'nato', 'iluwas', 'atong', 'kalibutan—ikampanya', 'sad', 'ni', 'nato.', 'Sa', 'mga', 'kaubang', 'kandidato', ',', 'i-apil', 'unta', 'nato', 'sa', 'atong', 'plataporma', 'ang', 'pang', 'dugay', 'nga', 'solusyon', 'alang', 'sa', 'kaluwasan', 'sa', 'kalikopan.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,124 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['‘UTOK’', 'SA', 'SENADO', '“Ang', 'ganahan', 'nako', 'kay', 'naa'y', 'utok', ',', 'sama', 'ni', 'Loren', 'Legarda.', 'Sayang', 'kung', 'wala', 'na', 'sa', 'Senado', '...', 'Tanan', 'akong', 'gipangayo', ',', 'gisuportahan', 'niya', ':', 'free', 'irrigation', ',', 'siya', 'nag', 'sponsor', ';', 'siya', 'nagpadungag', 'sa', 'sweldo', 'sa', 'pulis', 'og', 'sundalo', ';', 'apil', 'ang', 'free', 'college', 'og', 'Universal', 'Health', 'Care', 'Law.”', 'Pangulong', 'Rodrigo', 'Duterte'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 2, 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, 7, 8, 8, 8, 0, 1, 2] | cebuaner |
5,125 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Simula', 'Hunyo', ',', 'mangongolekta', 'ang', 'PhilHealth', 'ng', 'mas', 'mataas', 'na', 'premium', 'contribution', 'sa', 'lahat', 'ng', 'miyembro', 'nito.', 'Mula', 'sa', '3', '%', 'na', 'nakagisnan', 'umakyat', 'sa', '4', '%', 'ang', 'dagdag', 'sa', 'kontribusyon', 'matapos', 'mabigo', 'ang', 'Senado', 'na', 'magpasa', 'ng', 'batas', 'na', 'nagpapaliban', 'sa', 'mandated', 'na', 'pagtaas', 'ng', 'singil', 'dahil', 'sa', 'Universal', 'Health', 'Care', '(', 'UHC', ')', 'Law', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0] | cebuaner |
5,126 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LIBRENG', 'KOLEHIYO', ':', 'Para', 'mo', 'gamay', 'ang', 'mga', 'estudyante', 'nga', 'ni-undang', 'sa', 'pag', 'eskuwela', 'tungod', 'kay', 'kuwang', 'sa', 'kuwarta', ',', 'gipatuman', 'ni', 'House', 'Deputy', 'Speaker', 'Loren', 'Legarda', 'ang', 'Universal', 'Access', 'to', 'Quality', 'Tertiary', 'Education', 'Act', '(', 'RA', '10931', ')', 'nga', 'mo', 'libre', 'sa', 'gamit', 'ug', 'uban', 'pang', 'school', 'fees', 'sa', 'mga', 'state', 'universities', 'and', 'colleges', '(', 'SUCs', ')', ',', 'local', 'universities', 'and', 'colleges', '(', 'LUCs', ')', ',', 'ug', 'technical-vocational', 'institutions', ',', 'dugang', 'sa', 'paghatag', 'og', 'student', 'loan', 'program', 'alang', 'sa', 'mga', 'pobre.', '#', 'LorenLegarda', '#', 'HugotBisaya'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 2, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0] | cebuaner |
5,127 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Loren', 'Legarda', 'Amoang', 'inspirayon', 'ang', 'istorya', 'sa', 'matag', 'kinabuhi', 'nga', 'nabag-o', 'ug', 'milambo', 'tungod', 'sa', 'mga', 'balaod', 'nga', 'among', 'gihimo', 'ug', 'sa', 'mga', 'programa', 'nga', 'among', 'gisuportahan.', 'Ubani', 'ko', 'ninyo', 'kay', 'daghan', 'pa', 'tay', 'buhaton', 'aron', 'mahatagan', 'ug', 'paglaom', 'ang', 'atong', 'mga', 'kababayan', 'kay', 'kamo', 'ang', 'akong', 'inspirasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [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, 0, 0, 0] | cebuaner |
5,128 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['OPENING', 'SA', 'PINAKABAG-ONG', 'MALL', 'SA', 'DAVAO', 'CITY', 'KARONG', 'BULANA', ',', 'GINAATANGAN', 'NA', '!', 'Ginaatangan', 'na', 'sa', 'mga', 'Dabawenyos', 'ang', 'gitakdang', 'soft', 'opening', 'sa', 'pinakabag-ong', 'mall', 'sa', 'Davao', 'City', 'nga', 'mao', 'ang', 'Vista', 'Mall', 'Davao', 'nga', 'nahimutang', 'sa', 'may', 'Davao-Bukidnon', 'Road', ',', 'Camella', 'Subdivision', ',', 'Sto.', 'Nino', ',', 'Tugbok', ',', 'Davao', 'City.', 'Kini', 'usab', 'ang', 'pinaka-unang', 'full-scale', 'service', 'mall', 'sa', 'Vista', 'Mall', 'sa', 'Mindanao.', 'Pipila', 'matud', 'pa', 'sa', 'mamahimong', 'tenants', 'sa', 'mall', 'mao', 'ang', 'NCCC', ',', 'Mang', 'Inasal', ',', 'Coffee', 'Project', ',', 'All', 'Home', ',', 'Bake', 'My', 'Day', ',', 'ug', 'uban', 'pa.', 'Excited', 'na', 'usab', 'ang', 'kadaghanan', 'na', 'makita', 'ang', 'itsura', 'sa', 'sinehan', 'niini.', 'Sa', 'anunsyo', 'sa', 'official', 'Facebook', 'page', 'sa', 'Vista', 'Malls', ',', 'target', 'na', 'ipahigayon', 'ang', 'soft', 'opening', 'sa', 'mall', 'karong', 'bulana', ',', 'apan', 'wala', 'pa', 'gibutyag', 'kung', 'kanus-a', 'ang', 'pormal', 'na', 'petsa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,129 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Law-di', 'Loren', 'Mitabang', 'si', 'Loren', 'Legarda', 'sa', 'nagkalain-laing', 'ahensya', 'sa', 'nasudnong', 'kagamhanan', 'alang', 'sa', 'pagpatuman', 'sa', 'nagkalain-laing', 'programa', 'ug', 'proyekto', 'aron', 'matabangan', 'ang', 'mga', 'Antiqueño.', 'Kini', 'nga', 'mga', 'programa', 'naglakip', 'sa', 'DSWD', ''s', 'Assistance', 'to', 'Individuals', 'in', 'Crisis', 'Situation', 'nga', 'naghatag', 'ug', 'pinansyal', 'nga', 'tabang', 'alang', 'sa', 'nagkalain-laing', 'panginahanglanon', 'sama', 'sa', 'mga', 'tambal', ',', 'food', 'supplies', ',', 'bungtod', ',', 'transportasyon', 'ug', 'edukasyon.', 'Ang', 'Sustainable', 'Livelihood', 'Program', '(', 'SLP', ')', 'livelihood', 'projects', 'naghatag', 'usab', 'sa', 'porma', 'sa', 'kapital', 'alang', 'sa', 'mga', 'mag-uuma', 'sa', 'baboy', ',', 'mga', 'tindahan', 'sa', 'komunidad', ',', 'ug', 'pagpalit', 'sa', 'mga', 'gamit', 'sa', 'pangingisda', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 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 |
5,130 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nawaan', 'og', 'trabaho', '?', 'GOBYERNO', 'ANG', 'BAHALA', 'NIMO', '!', '“’Di', 'na', 'kinahanglan', 'mangutang', 'ang', 'mga', 'nawagtangan', 'og', 'trabaho.', 'Hinuon', ',', 'sa', 'iyang', 'kaugalingong', 'komunidad', ',', 'gobyerno', 'na', 'ang', 'mo', 'hatag', 'og', 'trabaho.”', 'Vice', 'President', 'Leni', 'Robredo'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,131 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALIK-TRABAHO', ',', 'BALIK-KITA', '“Kung', 'naabot', 'nato', 'ang', 'kadaugan', 'sa', 'paghiusa', ',', 'makita', 'nato', 'atong', 'mga', 'kababayan', 'nga', 'makabalik', 'na', 'sa', 'ilang', 'trabaho', ',', 'nga', 'naa'y', 'kuwarta', 'nasad', 'sa', 'ilang', 'bulsa', ',', 'nga', 'mapakaon', 'na', 'ilang', 'mga', 'anak', ',', 'og', 'kaya', 'sila', 'palitan', 'sa', 'ilang', 'kinahanglanon.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,132 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pagtabang', ',', 'abot', 'hangtod', 'Davao', 'City', 'IKAW', 'NA', 'JUD', '!', '“Daghang', 'salamat', 'sa', 'SMNI', 'nga', 'gi', 'sugtan', 'kong', 'sorpresahon', 'og', 'regaluhan', 'og', 'bulak', 'ug', 'scented', 'candle', 'si', 'Senator', 'Loren', 'Legarda', ',', 'nga', 'personal', 'nasad', 'nakong', 'napasalamatan', 'sa', 'iyang', 'tabang', 'sa', 'Davao', 'City', ',', 'sama', 'sa', 'Social', 'Welfare', 'Building', ',', 'og', 'ang', 'gitukod', 'nga', 'National', 'Museum.', 'Mabuhay', 'ka', '!', '”', 'Davao', 'City', 'Mayor', 'Sara', 'Duterte'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 5, 6, 0, 1, 2] | cebuaner |
5,133 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MELAI', 'NANGAMPANYA', 'PARA', 'KAY', 'LENI', 'Melai', 'Cantiveros', 'mibisita', 'sa', 'Surigao', 'City', 'aron', 'mangampanya', 'sa', 'kandidatora', 'ni', 'Bise-Presidente', 'Leni', 'Robredo', 'Pagka-Presidente.', 'Kahapong', 'adlawa', 'Mayo', '5', ',', '2022', ',', 'nakig-abiabi', 'si', 'Cantiveros', 'sa', 'daghang', 'mga', 'botante', 'nga', 'Surigaonon', 'sa', 'diwa', 'sa', '#', 'TaoSaTaoParaKayRobredo', 'sa', 'mga', 'tindera.', 'Usa', 'si', 'Cantiveros', 'sa', 'daghang', 'mga', 'artista', 'nga', 'nag-endorso', 'sa', 'publiko', 'sa', 'pagdagan', 'ni', 'VP', 'Leni', 'sa', 'pagkapresidente', 'ug', 'personal', 'nga', 'miboluntaryo', 'sa', 'pag-apil', 'ug', 'pagpaningkamot', 'sa', 'mga', 'house', 'to', 'house', 'campaign', 'sa', 'mga', 'kabalayan', 'ug', 'sa', 'tibuok', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 0, 0, 0, 1, 1, 2, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,134 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Suwerte', 'kaayo', 'ko', ',', 'usa', 'ka', 'batan-ong', 'pulitiko', ',', 'tungod', 'kay', 'adunay', 'mga', 'senior', 'politicians', ',', 'sama', 'ni', 'Senador', 'Loren', 'Legarda', ',', 'nga', 'nagpaambit', 'nako', 'sa', 'ilang', 'mga', 'naagian', 'sa', 'pamuno.', 'Nag-uswag', 'ako', 'isip', 'usa', 'ka', 'lider', 'tungod', 'sa', 'mga', 'tip', 'ug', 'tabang', 'nga', 'akong', 'nadungog', 'gikan', 'niya.”', 'Davao', 'City', 'Mayor', 'Sara', 'Duterte'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 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, 5, 6, 0, 1, 2] | cebuaner |
5,135 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NGANO', 'GISULOD', 'ANG', 'PULITIKA', '?', '“Isip', 'usa', 'nga', 'taga', 'balita', 'sauna', ',', 'akong', 'gidokumento', 'ang', 'mga', 'problema', 'sa', 'katilingban.', 'Akong', 'nakita', ',', 'hugaw', 'ang', 'Pasig', 'River', ',', 'daghang', 'basura.', 'Naa'y', 'miduol', 'sa', 'ako', 'nga', 'babaye', ',', 'bata', ',', 'biktima', 'sa', 'pag-abuso.', 'Naghunahuna', 'ko', 'sauna', ',', 'paghimo', 'og', 'mga', 'balaod', 'nga', 'makasulbad', 'sa', 'mga', 'problema', 'sa', 'katilingban', 'nga', 'akong', 'personal', 'nga', 'nakit-an.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,136 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['WALA', 'SA', 'KULAY', ',', 'NASA', 'SERBISYO', '“Tanan', 'nga', 'bulak', ',', 'bisag', 'unsa', 'nga', 'klase', 'o', 'kulay', ',', 'kay', 'nindot.', 'Ingon', 'ana', 'sad', 'sa', 'pulitika.', 'Ang', 'importante', ',', 'mo', 'silbi', 'ta', 'og', 'tapat', 'sa', 'tanang', 'Pilipino', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 7, 0, 0] | cebuaner |
5,137 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ATONG', 'HIBAW-AN', '“Kinahanglan', 'nga', 'nahibaw-an', 'nato', 'ang', '94', 'protected', 'areas', ',', 'endangered', 'species', ',', 'sa', 'daghang', 'biodiversity.', 'Unsaon', 'nimo', 'protektahan', 'kung', 'wala', 'ka', 'kahibaw', 'nga', 'naa', '?', '”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,138 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Love', 'ba', 'nimo', 'si', 'Mother', 'Earth', '?', 'THINK', 'GREEN', '!', '“Ang', 'atong', 'environmental', 'laws', ',', 'sama', 'sa', 'Ecological', 'Solid', 'Waste', 'Management', 'Act', ',', 'Clean', 'Air', 'Act', ',', 'Clean', 'Water', 'Act', ',', 'Climate', 'Change', 'Act', ',', 'og', 'uban', 'pa', ',', 'mahimo'g', 'epektibo', 'lang', 'kung', 'mag', 'tabang-tabang', 'ta', 'sa', 'pagpatuman', 'og', 'pagsunod', 'niining', 'mga', 'balaod.”', 'House', 'Deputy', 'Speaker', 'Loren', 'Legarda'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 7, 8, 8, 8, 8, 0, 7, 8, 8, 0, 7, 8, 8, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2] | cebuaner |
5,139 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BISAG', '1', 'BANGSA', ',', 'NAGSUPORTA', 'Ang', 'One', 'Bangsamoro', 'Movement', '(', '1', 'BANGSA', ')', 'nipadayag', 'og', 'suporta', 'sa', 'kandidatura', 'sa', 'pagkasenador', 'ni', 'Loren', 'Legarda', ',', 'nga', 'mihimo', 'ug', 'daghang', 'mga', 'balaod', 'alang', 'sa', 'kaayohan', 'sa', 'mga', 'Muslim', 'nga', 'komunidad', ',', 'apil', 'ang', 'nagtukod', 'sa', 'Mindanao', 'Development', 'Authority', 'ug', 'ang', 'Marawi', 'Compensation', 'Act', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 7, 8, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 7, 8, 8, 0] | cebuaner |
5,140 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nindot', 'kaayo', 'nga', 'surpresa', 'gikan', 'ni', 'Mayor', 'Inday', 'Sara', 'ug', 'Pastor', 'Apollo', 'Quiboloy', '!', 'Daghang', 'salamat', 'sa', 'paghatag', 'og', 'oras', 'sa', 'pag-uban', 'kanako', 'sa', 'interbyu', 'kauban', 'si', 'PACQ', '!', 'Kung', 'ang', 'mga', 'naa', 'sa', 'imong', 'atubangan', 'ug', 'kaestorya', 'nimo', 'adunay', 'parehas', 'nga', 'mga', 'pangandoy', 'sama', 'kanimo', 'alang', 'sa', 'mga', 'tawo', ',', 'kultura', ',', 'kahimsog', ',', 'edukasyon', ',', 'ug', 'kalikasan', ',', 'dili', 'nimo', 'mamatikdan', 'ang', 'paspas', 'nga', 'paglabay', 'sa', 'panahon.', 'Salamat', 'sa', 'usa', 'ka', 'makahuluganon', 'nga', 'gabii', 'sa', 'pag-istorya', 'ug', 'panaghisgot.', 'Daghang', 'salamat', 'usab', ',', 'Mayor', 'Inday', 'Sara', ',', 'sa', 'imong', 'regalo', 'nga', 'mahumot', 'nga', 'mga', 'kandila', 'ug', 'nalipay', 'usab', 'ko', 'nga', 'nahibal-an', 'nimo', 'ang', 'akong', 'paborito', 'nga', 'bulak', '!', 'Asaha', 'ang', 'akong', 'padayon', 'nga', 'pagpaningkamot', 'nga', 'mahatagan', 'ang', 'angay', 'nga', 'programa', 'ug', 'tabang', 'sa', 'mga', 'Davaoeño', '!', 'Gihigugma', 'ko', 'kamo', 'tanan', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0] | cebuaner |
5,141 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DAOG', 'ANG', 'NAA'Y', 'TRABAHO', '!', 'Mo', 'tuo', 'ang', 'Trabaho', 'Party-list', 'nga', 'kinahanglan', 'nang', 'iseryoso', 'ang', 'kaayohan', 'sa', 'mga', 'trabahador', 'nga', 'Pilipino', ':', '-Pagwala', 'sa', 'sistemang', ''endo'', '-Dugang', 'na', 'suweldo', ',', 'ilabi', 'na', 'sa', 'health', 'workers', '-Pagwala', 'sa', 'provincial', 'rate', 'sa', 'suweldo', '-Parehas', 'nga', 'oportunidad', 'sa', 'trabaho'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,142 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HANGIN', 'N'YO', 'HUGAW', 'KAAYO', '!', '“Niingon', 'ang', 'World', 'Health', 'Organization', 'nga', 'walong', 'milyong', 'katao', 'sa', 'kalibutan', 'ang', 'namatay', 'kada', 'tuig', 'tungod', 'sa', 'polusyon', 'sa', 'hangin.', 'Husto', 'na', 'ang', 'kini', 'kadaghan', 'para', 'masabtan', 'nga', 'kulang', 'o', 'wala', 'nato', 'maproteksyonan', 'ang', 'atong', 'kalikupan.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,143 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Libreng', 'bakuna', 'kontra', 'Rabis', 'para', 'sa', 'mga', 'iro', 'ug', 'iring', 'headed', 'by', 'Dr.', 'Isabel', 'Gimenez.', 'Salamat', 'sad', 'sa', 'mga', 'volunteers', 'na', 'VSU', 'students.', 'Merida', ',', 'Leyte'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 3, 0, 5, 6, 6] | cebuaner |
5,144 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GI-ULANAN', 'OG', 'SUPORTA', '“Dakong', 'pasalamat', 'sa', 'matag', 'Pilipino', 'nga', 'nisalig', 'ug', 'nisuporta', 'sa', 'akong', 'pangandoy', 'nga', 'magpadayon', 'sa', 'pagserbisyo', 'sa', 'lungsod', 'ug', 'sa', 'katawhan.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,145 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Naadtoan', 'na', 'nato', 'sa', 'Pampanga', 'ang', 'pinakaunang', 'OFW', 'Hospital', 'sa', 'nasud', ',', 'nga', 'mo', 'hatag', 'og', 'libreng', 'medical', 'assistance', 'sa', 'mga', 'OFWs', 'og', 'sa', 'ilang', 'mga', 'dependents', 'isip', 'usa', 'sa', 'pag', 'salamat', 'sa', 'ilang', 'sakripisyo', 'og', 'amot', 'sa', 'atong', 'ekonomiya', ',', 'ilabi', 'na', 'niadtong', 'pandemya.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 5, 6, 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 |
5,146 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Dili', 'tamo', 'ipaubos', ',', 'padayon', 'atong', 'pangandoy', 'para', 'ilaban', 'at', 'ipatuman', 'ang', 'mga', 'balaod', 'og', 'programa', 'nga', 'mo', 'hatag', 'og', 'pag-asa', 'sa', 'ginhawa', 'og', 'kinbuhi', 'sa', 'matag', 'Pilipino.', 'Daghang', 'salamat', '!', '”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 7, 0, 0, 0, 0] | cebuaner |
5,147 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dungan', 'sa', 'atong', 'pagsaulog', 'sa', 'Labor', 'Day', 'nakahigayon', 'usab', 'kita', 'sa', 'pagbisita', 'sa', 'unang', 'tambalanan', 'sa', 'nasud', 'kansang', 'prayoridad', 'mao', 'ang', 'panglawas', 'sa', 'atong', 'mga', 'OFW', 'ug', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'dependents.', 'Uban', 'sa', 'kooperasyon', 'sa', 'gobyerno', 'ug', 'pribadong', 'sektor', ',', 'natukod', 'ang', 'ospital', 'nga', 'maghatag', 'ug', 'libreng', 'medikal', 'nga', 'tabang', 'isip', 'pag-ila', 'sa', 'talagsaong', 'sakripisyo', 'ug', 'kontribusyon', 'sa', 'atong', 'mga', 'OFWs', 'sa', 'pagbangon', 'sa', 'ekonomiya', 'ilabina', 'taliwala', 'sa', 'pandemya.', 'Sa', 'pag-abli', 'sa', 'OFW', 'Hospital', 'sa', 'Pampanga', ',', 'kini', 'maoy', 'sinugdanan', 'sa', 'pag-abli', 'sa', 'susamang', 'mga', 'pasilidad', 'sa', 'lain-laing', 'dapit', 'sa', 'nasud.', 'Mabuhi', 'ang', 'atong', 'mga', 'bantugan', 'nga', 'OFWs', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
5,148 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TINGNAN', '|', 'Miting', 'de', 'avance', 'ng', 'UniTeam', 'kahapon', ',', 'ika-3', 'ng', 'Mayo', 'sa', 'Guimbal', ',', 'Iloilo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 6, 6, 0] | cebuaner |
5,149 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Loren', ''s', 'Green', 'Philippines', 'Ang', 'Eco-warrior', 'nga', 'si', 'Loren', 'Legarda', 'nagtukod', 'sa', 'Luntiang', 'Pilipinas', 'Foundation', ',', 'usa', 'ka', 'urban', 'greening', 'movement', 'ug', 'national', 'urban', 'forestry', 'program', 'sa', 'Pilipinas', 'nga', 'gipahinungod', 'sa', 'pagpasiugda', 'sa', 'pagpanalipod', 'sa', 'palibot', 'ug', 'kahibalo', 'sa', 'mga', 'Pilipino.', 'Tinuod', 'nga', 'Green', 'Champion', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 0, 7, 8, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 4, 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, 7, 0, 0, 7, 8, 0] | cebuaner |
5,150 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'salamat', ',', '#', 'Cagayan', '!', 'Kasin', 'nindot', 'ang', 'inyong', 'lugar', 'sa', 'pagdawat', 'sa', 'akong', 'pagampanya', 'aron', 'padayon', 'na', 'makaserbisyo.', 'Isip', 'tagsulat', 'sa', 'balaod', 'sa', 'ENIPAS', ',', 'nalipay', 'ko', 'nga', 'naapil', 'dinhi', 'ang', 'duha', 'sa', 'atong', 'mga', 'protected', 'area', 'sa', 'Cagayan', ',', 'ang', 'Palaui', 'Island', 'Protected', 'Landscape', 'and', 'Seascape', 'ug', 'ang', 'Peñablanca', 'Protected', 'Landscape', 'and', 'Seascape.', 'Magpadayon', 'kita', 'sa', 'pagtinabangay', 'aron', 'mapanalipdan', 'ang', 'atong', 'natural', 'nga', 'kahinguhaan.', 'Isip', 'usa', 'sa', 'mga', 'probinsya', 'nga', 'benepisyaryo', 'usab', 'sa', 'mga', 'scholarship', 'program', 'ubos', 'sa', 'atong', 'pakigtambayayong', 'sa', 'TESDA', ',', 'nagpaabot', 'ko', 'sa', 'dugang', 'mga', 'programa', 'nga', 'atong', 'mapalapad', 'sa', 'umaabot', 'nga', 'mga', 'tuig.', 'Daghang', 'salamat', 'usab', 'sa', 'akong', 'Team', 'Loren', 'volunteers', 'sa', 'madasigong', 'pagpalapad', 'sa', 'akong', 'mga', 'plataporma', 'ug', 'adbokasiya', 'sa', 'atong', 'mga', 'kababayan', 'sa', 'Cagayan', '!', '❤️'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0] | cebuaner |
5,151 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'salamat', 'sa', 'kinasing-kasing', 'na', 'pagdawat', '!', 'Brgy.', 'Domonar', ',', 'Ormoc', 'City'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6] | cebuaner |
5,152 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kasunod', 'ng', 'Easter', 'Sunday', 'press', 'conference', 'ni', 'Manila', 'Mayor', 'Isko', 'Moreno', 'para', 'manawagang', 'iatras', 'ng', 'katunggaling', 'si', 'Vice', 'President', 'Leni', 'Robredo', 'ang', 'kandidatura', 'nito', ',', 'bumaba', 'sa', '4', '%', 'mula', 'sa', '8', '%', 'ang', 'nakuha', 'ni', 'Moreno', 'sa', 'latest', 'Pulse', 'Asia', 'survey', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 8, 0, 0, 0, 5, 0, 1, 2, 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, 3, 4, 0, 0] | cebuaner |
5,153 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SENADOR', 'PARA', 'SA', 'MGA', 'TRABAHADOR', '“Dungan', 'sa', 'pagsaulog', 'sa', 'Labor', 'Day', 'atong', 'Mayo', '1', ',', 'akong', 'gipaabot', 'ang', 'akong', 'kinasingkasing', 'nga', 'pasalamat', 'sa', 'Trade', 'Union', 'Congress', 'of', 'the', 'Philippines', '(', 'TUCP', ')', 'sa', 'pag-endorso', 'sa', 'akong', 'kandidatura', 'sa', 'Senado.', 'Kauban', 'ko', 'ninyo', 'sa', 'pagpauswag', 'sa', 'kaayohan', 'ug', 'katungod', 'sa', 'among', 'mga', 'trabahador.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,154 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanang', 'card', 'nga', 'malipay', 'kang', 'wala', 'jud', 'nimo', 'nagamit—HEALTH', 'CARD.', 'Kana', 'og', 'lain', 'pang', 'benepisyo', 'ang', 'ilaban', 'sa', 'Trabaho', 'Party-list', 'para', 'sa', 'mga', 'trabahador', 'nga', 'Pilipino', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 4, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
5,155 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nawaan', 'og', 'trabaho', 'HATAGA'G', 'PUHUNAN', 'SA', 'NEGOSYO', '“Daghan', 'sa', 'atong', 'kababayan', 'ang', 'nawagtanga'g', 'trabaho', 'og', 'panginabuhi', 'tungod', 'sa', 'pandemya.', 'Dili', 'nato', 'puwede', 'ibalewala', 'ilang', 'agulo', ',', 'mao', 'nga', 'atong', 'pakusgon', 'ang', 'atong', 'MSMEs—ang', 'ilang', 'panginabuhian', 'mao', 'ang', 'dugang', 'nga', 'kahigayonan', 'sa', 'trabaho.”', 'House', 'Deputy', 'Speaker', 'Loren', 'Legarda', 'Author', ',', 'RA', '9501', 'o', 'MSME', 'Law'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 0, 7, 8] | cebuaner |
5,156 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Loren', 'Loves', 'Isabela', 'Sa', 'pagbisita', 'sa', 'mga', 'boluntaryo', 'sa', 'Team', 'Loren', 'sa', 'Cabagan', ',', 'Tamauini', ',', 'Ilagan', ',', 'Gamu', ',', 'Burgos', ',', 'ug', 'Roxas', 'sa', 'Probinsya', 'sa', 'Isabela', ',', 'usab', 'ninyong', 'gipabati', 'kanako', 'ang', 'inyong', 'pagsalig', 'ug', 'suporta.', 'Nahinumdom', 'pa', 'ko', 'sa', 'unang', 'batch', 'sa', 'atong', 'Pantawid', 'Tuition', 'Program', 'sa', 'Isabela', 'State', 'University', 'niadtong', '2013', 'para', 'sa', 'mga', 'anak', 'sa', 'atong', 'mga', 'kugihang', 'mag-uuma.', 'Nasayod', 'ko', 'sa', 'importansya', 'sa', 'edukasyon', 'mao', 'nga', 'atong', 'giduso', 'ang', 'paghimo', 'ug', 'pondo', 'sa', 'Universal', 'Access', 'to', 'Quality', 'Tertiary', 'Education', 'ug', 'karon', ',', 'libre', 'na', 'ang', 'atong', 'mga', 'kabatan-onan', 'sa', 'tanang', 'State', 'Universities', 'and', 'Colleges.', 'Makadahom', 'mo', 'nga', 'padayon', 'nakong', 'iduso', 'ang', 'paghimo', 'ug', 'pondo', 'sa', 'One', 'Tablet', ',', 'One', 'Student', 'Act', 'isip', 'dugang', 'suporta', 'sa', 'atong', 'mga', 'estudyante.', 'Daghang', 'salamat', 'sa', 'mga', 'pahiyom', 'nga', 'naghatag', 'kanako', 'og', 'dugang', 'kusog', 'ug', 'inspirasyon', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 0, 5, 0, 0, 0, 0, 0, 0, 7, 8, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,157 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SALAMAT', ',', 'IGLESIA', 'NI', 'CRISTO', '!', '“Dako', 'nga', 'dungog', 'nga', 'makadawat', 'sa', 'pagsalig', ',', 'suporta', ',', 'ug', 'pag-endorso', 'sa', 'Iglesia', 'Ni', 'Cristo', 'sa', 'akong', 'gitinguha', 'nga', 'pagbalik', 'sa', 'Senado.', 'Daghang', ',', 'daghang', 'salamat', '!', '”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,158 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitanggong', 'karon', 'sa', 'Carbon', 'Police', 'Station', 'ang', 'langyaw', 'nga', 'si', 'Ken', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 1, 0] | cebuaner |
5,159 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dihang', 'wa', 'nakahatag', 'og', 'igong', 'rason', 'nganong', 'kuyog', 'niya', 'ang', 'mga', 'bata', ',', 'gi-turnover', 'si', 'Ken', 'ngadto', '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, 1, 0, 0, 0, 0] | cebuaner |
5,160 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hugot', 'nga', 'gipanghimakak', 'ni', 'Ken', 'nga', 'duna', 'siyay', 'dautang', 'tuyo', 'sa', 'mga', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,161 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', ',', 'lima', 'ka', 'tuig', 'na', 'siya', 'nga', 'nagbalik-balik', 'sa', 'Sugbo', 'aron', 'pagtabang', 'sa', 'pamilya', 'sa', 'maong', 'mga', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,162 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakurat', 'na', 'lang', 'siya', 'nga', 'gi-hold', 'sila', 'diha', 'sa', 'terminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,163 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'sab', 'niya', 'nga', 'upat', 'lang', 'sa', 'iyang', 'kuyog', 'ang', 'menor', 'de', 'edad', 'samtang', 'ang', 'duha', 'nag-edad', 'na', 'og', '19', 'ug', '18', 'anyos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,164 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagsugod', 'ang', 'panaghigalaay', 'nila', 'ni', 'Ken', 'sa', 'pamilya', 'dihang', 'nakaila', 'niya', 'sa', 'internet', 'ang', 'inahan', 'sa', 'mga', 'bata', 'lima', 'ka', 'tuig', 'na', 'ang', 'nakalabay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,165 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'og', 'naminyo', 'ug', 'nakaanak', 'na', 'kini', ',', 'nagpadayon', 'ang', 'ilang', 'panaghigalaay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,166 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gani', 'lakip', 'ang', 'mga', 'ginikanan', 'sa', 'ilang', 'ikakuyog', 'sa', 'Moalboal', 'ug', 'ila', 'lang', 'kining', 'hapiton', 'sa', 'Siyudad', 'sa', 'Naga', 'kon', 'nakadayon', 'pa', 'lang', 'unta', 'sila', 'sa', 'pagsakay', 'og', 'bus', 'sa', 'terminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,167 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihimug-atan', 'sa', 'langyaw', 'nga', 'di', 'lang', 'ang', 'maong', 'pamilya', 'ang', 'iyang', 'natabangan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,168 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gani', 'naka-volunteer', 'siya', 'ug', 'nitabang', 'sa', 'rehabilitasyon', 'sa', 'Bogo', 'City', 'human', 'ang', 'bagyong', 'Yolanda', 'niadtong', '2013', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 7, 8, 0, 0, 0] | cebuaner |
5,169 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'usa', 'sa', 'mga', 'dalagita', 'nga', 'kuyog', 'sa', 'langyaw', ',', 'nihimakak', 'nga', 'may', 'pagpangabuso', 'nga', 'himuon', 'ang', 'langyaw', 'kanila', 'bugti', 'sa', 'iyang', 'pagtabang', 'ug', 'paglibre', 'kanila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,170 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', ',', 'matag', 'anhi', 'ni', 'Ken', 'ubanon', 'sila', 'niini', 'sa', 'pag-', 'travel', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,171 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihikay', 'na', 'sa', 'Carbon', 'Police', 'Station', 'ang', 'mga', 'dokumento', 'alang', 'sa', 'pagpasaka', 'og', 'tukmang', 'kaso', 'batok', 'sa', 'langyaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,172 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'gisukit-sukit', 'sa', 'mga', 'pulis', 'ang', 'ginikanan', 'sa', 'mga', 'bata', 'nga', 'kuyog', '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] | cebuaner |
5,173 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mokabat', 'sa', '15', 'ka', 'mga', 'tawo', 'ang', 'nangaangol', 'sa', 'pabuto', ',', 'apan', 'way', 'naangol', 'tungod', 'sa', 'saag', 'nga', 'mga', 'bala', 'sukad', 'sa', 'gabii', 'sa', 'Pasko', 'hangtod', 'kagahapon', ',', 'matod', 'sa', 'usa', 'ka', 'opisyal', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', '7', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 4, 4, 4, 4, 4, 4, 0] | cebuaner |
5,174 | 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', 'tawo', 'ang', 'nabuthan', 'og', 'pabuto', 'atol', 'sa', 'pagsaulog', 'sa', 'Pasko', 'sa', 'dakbayan', 'sa', 'Lapu-Lapu', 'samtang', 'usa', 'ka', 'balay', 'kansang', 'kisame', 'nalungag', 'gumikan', 'sa', 'stray', 'bullet', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,175 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagka-gabii', 'sa', 'Disyembre', '24', 'ug', '25', 'nadala', 'sa', 'tambalanan', 'ang', 'mga', 'biktima', 'sa', 'dihang', 'ang', '8', 'anyos', 'nga', 'si', 'Inot', 'naka-angkon', 'og', 'paso', 'sa', 'nawong', 'ug', 'gamay', 'nga', 'gisi', 'sa', 'iyang', 'ngabil', 'sa', 'dihang', 'nabuthan', 'sa', 'whistle', 'bomb', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,176 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Capitan', 'nabuthan', 'sa', 'tuo', 'nga', 'mata', 'samtang', 'wa', 'matino', 'kon', 'asa', 'ang', 'paso', 'o', 'samad', 'nga', 'naangkon', 'ni', 'Agnes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 0] | cebuaner |
5,177 | 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', ',', 'usa', 'ka', 'ginang', 'nga', 'taga-barangay', 'Bankal', 'ang', 'mi-post', 'sa', 'iyang', 'kasinatian', 'sa', 'Pasko', 'diin', 'nipadayag', 'sa', 'iyang', 'kabalaka', 'human', 'ang', 'kisame', 'sa', 'ilang', 'balay', 'nabuslot', 'gumikan', 'sa', 'saag', 'nga', 'bala', 'nga', 'mitugpa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 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 |
5,178 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nanawagan', 'si', 'Carreon', 'sa', 'kapulisan', 'nga', 'palapdan', 'pa', 'ang', 'ilang', 'monitoring', 'ug', 'supervision', 'alang', 'sa', 'mas', 'luwas', 'nga', 'kasaulogan', ',', 'ilabi', 'na', 'nga', 'naghinangat', 'ang', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,179 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'pa', 'sa', 'mao', 'nga', 'mga', 'hitabo', ',', 'kapulisan', 'sa', 'Central', 'Visayas', 'mihulagway', 'nga', 'malinawon', 'ug', 'hapsay', 'ang', 'pagsaulog', 'sa', 'Pasko', 'karong', 'tuiga', 'itandi', 'sa', 'milabayng', 'tuig', 'samtang', 'menos', 'usab', 'ang', 'biktima', 'sa', 'pabuto', 'tungod', 'sa', 'pasidaan', '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, 5, 6, 0, 0, 0, 0, 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 |
5,180 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'traffic', 'enforcers', 'sa', 'Talisay', 'City', 'ug', 'lungsod', 'sa', 'Minglanilla', 'nanghimakak', 'sa', 'mga', 'alegasyon', 'sa', 'social', 'media', 'nga', 'wa', 'silay', 'gihimo', 'sa', 'pagsulbad', 'sa', 'grabeng', 'paghuot', 'sa', 'trapiko', 'sa', 'Cebu', 'South', 'Coastal', 'Road', 'niadtong', 'Sabado', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0] | cebuaner |
5,181 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Labing', 'minos', '10', 'ka', 'oras', 'nga', 'wa', 'hapit', 'makairog', 'ang', 'trapiko', 'niadtong', 'Sabado', ',', 'gikan', 'sa', 'hapon', ',', 'hangtod', 'na', 'nianang', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,182 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibasol', 'ni', 'de', 'la', 'Peña', 'ang', 'upat', 'ka', 'mga', 'aksidente', 'sa', 'sakyanan', 'nga', 'gitaho', 'nga', 'nahitabo', 'sa', 'samang', 'oras', 'sa', 'dapit', 'ubos', 'sa', 'jurisdiction', 'sa', 'Minglanilla', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,183 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'de', 'la', 'Peña', 'nga', 'ilang', 'gitawag', 'ang', 'ilang', 'mga', 'kauban', 'sa', 'Minglanilla', ',', 'apan', 'way', 'bisan', 'usa', 'nga', 'nanubag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,184 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niini', ',', 'nanawag', 'sila', 'sa', 'Minglanilla', 'Police', 'Station', 'aron', 'magpatabang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 4, 4, 0, 0, 0] | cebuaner |
5,185 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'di', 'tinuod', 'nga', 'way', 'traffic', 'enforcers', 'sa', 'lungsod', 'sa', 'Minglanilla', 'niadtong', 'Sabado', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
5,186 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'opisyal', 'sa', 'lungsod', 'nakadawat', 'og', 'mga', 'pagsaway', ',', 'labina', 'sa', 'social', 'media', ',', 'human', 'natanggong', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,187 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kahuot', 'sa', 'trapiko', 'naggikan', 'pa', 'sa', 'dakbayan', 'sa', 'Talisay', 'lahos', 'na', 'sa', 'lungsod', 'sa', 'Minglanilla', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,188 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'municipal', 'administrator', 'Concordio', 'Mejias', 'niingon', 'nga', 'siya', 'mismo', 'natanggong', 'usab', 'sa', 'trapiko', 'niadtong', 'higayona', 'og', 'duha', 'usab', 'ka', 'oras', 'nianang', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,189 | 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', 'dili', 'lang', 'ang', 'lungsod', 'sa', 'Minglanilla', 'ang', 'grabeng', 'trapik', ',', 'apil', 'na', 'usab', 'ang', 'Cebu', 'city', ',', 'Talisay', ',', 'Naga', ',', 'San', 'Fernando', 'hangtod', 'na', 'siyudad', 'sa', 'Carcar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 5, 0, 5, 6, 0, 0, 0, 0, 5, 0] | cebuaner |
5,190 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iya', 'usab', 'nga', 'gihimakak', 'nga', 'nag-party', 'ang', 'mga', 'traffic', 'enforcers', 'sa', 'maong', 'adlaw', ',', 'hinungdan', 'nga', 'walay', 'makita', 'nga', 'enforcers', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,191 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Mejias', 'nga', 'igo', 'lang', 'silang', 'naniudto', 'ug', 'human', 'sa', 'paniudto', ',', 'mibalik', 'ra', 'usab', 'sila', 'dayon', 'sa', 'ilang', 'puwesto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
5,192 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'taxi', 'driver', 'nga', 'nahinabi', 'sa', 'Superbalita', 'Cebu', 'nibutyag', 'nga', 'mga', 'alas', '3', 'sa', 'hapon', 'siyang', 'nihatod', 'sa', 'iyang', 'pasahero', 'sa', 'dakbayan', 'sa', 'Naga', 'gikan', 'sa', 'Tabunok', ',', 'Talisay', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 6, 6, 0] | cebuaner |
5,193 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niabot', 'sila', 'sa', 'Naga', 'mga', 'alas', '8', 'na', 'hapit', 'sa', 'gabii', 'tungod', 'sa', 'kahuot', 'sa', 'trapiko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,194 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niabot', 'siya', 'sa', 'Cebu', 'City', 'mga', 'alas', '11', 'na', 'pasado', 'sa', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,195 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alkanse', 'tong', 'biyahia', 'kay', 'P700', 'kapin', 'rang', 'patak', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,196 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'gilantawan', 'nga', 'tingsakay', 'sa', 'paghinangat', 'sa', 'Bag-ong', 'Tuig', ',', 'ang', 'mga', 'tinugyanan', 'sa', 'Cebu', 'South', 'Bus', 'Terminal', 'nanawagan', 'ug', 'nihangyo', 'karon', 'sa', 'mga', 'police', 'station', 'sa', 'matag', 'lungsod', 'nga', 'mag-deploy', 'og', 'personnel', 'nga', 'makatabang', 'sa', 'pagdumala', 'sa', 'trapiko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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 |
5,197 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'nakitang', 'hinungdan', 'nganong', 'mangatanggong', 'ang', 'mga', 'pasahero', 'diha', 'sa', 'terminal', 'mao', 'ang', 'kalangay', 'usab', 'sa', 'pagbalik', 'sa', 'mga', 'bus', 'tungod', 'sa', 'grabeng', 'traffic', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,198 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikan', 'Disyembre', '22', 'hangtud', 'Disyembre', '25', ',', 'anaa', 'sa', 'kapin', '131,000', 'ka', 'mga', 'pasahero', 'ang', 'gibana-banang', 'nidagsa', 'sa', 'CSBT', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,199 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'base', 'usab', 'sa', 'natala', 'nga', 'mga', 'bus', 'trips', 'nga', 'niabot', 'og', '592', 'niadtong', 'Biyernes', ',', '579', 'niadtong', 'Sabado', ',', '655', 'niadtong', 'Domingo', 'ug', '556', 'niadtong', 'Lunes', ',', 'adlaw', 'sa', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 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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