Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29
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4,800 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Religious', 'man', 'gud', 'ko', ',', 'giampo', 'nako', 'nga', 'unta', 'akoa', 'na', 'lang', 'ka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,801 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Facebook', 'post', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'nakaani', 'og', 'lain-laing', 'komento', 'ug', 'reaksyon', 'sa', 'mga', 'netizen', '... | [0, 7, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,802 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LOVI', 'POE', ',', 'GISULTIHAN', 'NI', 'TONI', 'FOWLER', 'Mao', 'kini', 'ang', 'gisulti', 'sa', 'social', 'media', 'personality', 'ug', 'aktres', 'nga', 'si', 'Toni', 'Fowler', 'bahin',... | [1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0] | cebuaner |
4,803 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KUNG', 'AKO', ''YAN', ',', 'KAKASUHAN', 'KO', ''YAN', 'Padayon', 'nga', 'gibiaybiay', 'ang', 'mga', 'personalidad', 'sa', 'social', 'media', 'nga', 'sila', 'si', 'Xander', 'Ford', 'ug',... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,804 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Isip', 'usa', 'ka', 'awardee', 'sa', ''Men', 'Who', 'Matter', ''', '2023', ',', 'gipasiugda', 'sa', 'PeopleAsia', 'ang', 'mga', 'paningkamot', 'ni', 'Alfred', 'Vargas', ',', 'kinsa', 'n... | [0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 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, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,805 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'reaksyon', 'sa', 'Filipino', 'import', 'nga', 'si', 'Kairi', 'Rayosdelsol', 'nga', 'moduwa', 'isip', 'import', 'sa', 'Indonesia', 'alang', 'sa', 'iladong', 'ONIC',... | [0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 4, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 7, 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, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 7, 0] | cebuaner |
4,806 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', '47-anyos', 'nga', 'Macarine', ',', 'usa', 'ka', 'Surigaonon', 'ug', 'kasamtangang', 'Provincial', 'Prosecutor', 'sa', 'Bohol', 'ang', 'mosuway', 'paglangoy', 'tabok', 'sa',... | [0, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 5, 0] | cebuaner |
4,807 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'kaylap', 'nga', 'paggamit', 'sa', 'teknolohiya', 'sa', 'tanang', 'negosyo', 'sa', 'Pilipinas', ',', 'importante', 'ang', 'pagtutok', 'sa', 'pagpalambo', 'sa', 'digital',... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 8, 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, 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, 0, 0, 0... | cebuaner |
4,808 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'OFW', 'nga', 'taga', 'Leyte', 'ang', 'nag-trending', 'online', 'tungod', 'sa', 'prangka', 'ug', 'kataw-anan', 'nga', 'mensahe', 'nga', 'iyang', 'gisuwat', 'sa', 'iyang', 'b... | [0, 0, 3, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,809 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ginpahayag', 'ni', 'Iloilo', 'City', 'Mayor', 'Jerry', 'Treñas', 'ang', 'iya', '“disappointment”', 'kay', 'Vice', 'Mayor', 'El', 'Cid', 'Familiaran', 'sang', 'Bacolod', 'City', 'tuhoy',... | [0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 5, 6, 0, 0, 0, 0, 0, 3, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2, 0] | cebuaner |
4,810 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'up-to-date', 'nga', 'impormasyon', ',', 'ang', 'Pilipinas', 'Today', 'padayon', 'usab', 'nga', 'naghatod', 'og', 'cash', 'ug', 'gift', 'voucher', 'sa', 'atong', 'mga', 'f... | [0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,811 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Grabe', 'ka', 'Mae', '!', '#', 'PilipinasToday'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-L... | [0, 0, 1, 0, 0, 0] | cebuaner |
4,812 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakighinabi', 'kang', 'Solis', ',', 'human', 'sa', 'desisyon', 'daw', 'nibunot', 'na', 'usab', 'sila', 'og', 'tunok', 'tungod', 'kay', 'dugay', 'na', 'kining', 'gihatagan', 'og', ... | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 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, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,813 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hunyo', '17', 'dihang', 'gibalikbalik', 'ni', 'Senador', 'Sonny', 'Angara', 'ang', 'pagpatuman', 'sa', 'iyang', 'co-authored', 'nga', 'Salary', 'Standardization', 'Law', 'V', 'nga', 'na... | [0, 0, 0, 0, 0, 0, 1, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,814 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tapos', 'nagsige', 'mog', 'hatag', 'og', 'lawog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3... | [0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,815 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gusto', 'ni', 'Senador', 'Sonny', 'Angara', 'nga', 'ang', 'kababayen-an', 'dili', 'limitado', 'sa', 'ilang', 'panginabuhian', 'ug', 'pinaagi', 'sa', 'Act', 'Allowing', 'the', 'Employmen... | [0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 7, 8, 0, 7, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,816 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paambit', 'sa', 'Maa', 'Police', 'Station', 'nga', 'ang', '25-anyos', 'nga', 'lalaki', 'residente', 'sa', 'Catalunan', 'Pequeño.', 'Matod', 'sa', 'usa', 'ka', 'babayeng', 'residente', '... | [0, 0, 3, 4, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,817 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Trending', 'karon', 'sa', 'netizens', 'ang', 'Facebook', 'post', 'sa', 'vlogger-actress', 'nga', 'si', 'Toni', 'Fowler', 'karong', 'Huwebes', ',', 'Hunyo', '22', ',', 'nga', 'nag-featur... | [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0] | cebuaner |
4,818 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kung', 'unsa', 'man', 'ang', 'imong', 'naagian', 'karon', ',', 'sala', 'na', 'nimo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continua... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,819 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SHOT', 'PUNO', 'Mao', 'ba', 'kini', 'ang', 'reaksyon', 'sa', 'singer', 'nga', 'si', 'Juan', 'Karlos', 'Labajo', 'sa', 'birthday', 'greetings', 'ni', 'Miss', 'Universe', 'Philippines', '... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,820 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Dapitan', 'City', 'Tourism', 'Officer', 'Apple', 'Marie', 'Agolong', 'niingon', 'nga', 'usa', 'lang', 'kini', 'sa', 'mga', 'selebrasyon', 'nga', 'ipahigayon', 'sa', 'dakbayan', 's... | [0, 3, 4, 4, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,821 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Gusto', 'ng', 'bansang', 'Amerika', 'na', 'sila', 'lang', 'ang', 'maghari', 'sa', 'sanlibutan', ',', '”', 'saad', 'ng', '‘Kingdom', 'of', 'Jesus', 'Christ’', 'founder', 'na', 'si', 'Pa... | [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,822 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'sa', 'mga', 'taho', ',', 'si', 'Elon', 'Musk', 'nag-tweet', 'karong', 'Miyerkules', ',', 'Hunyo', '21', ',', 'ang', 'iyang', 'hagit', 'human', 'nahibal-an', 'nga', 'si', 'Mark... | [0, 0, 0, 0, 0, 0, 1, 2, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 1, 0] | cebuaner |
4,823 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nag-viral', 'karong', 'adlawa', 'ang', 'King', 'of', 'Talk', ',', 'ang', 'pangutana', 'ni', 'Boy', 'Abunda', 'ngadto', 'sa', 'komedyante', 'nga', 'aktres', 'nga', 'si', 'Kakai', 'Bautis... | [0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,824 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bro', ',', 'puwede', 'ra', 'man', 'dili', 'na', 'ka', 'moasa', 'pero', 'gahian', 'kaay'ka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: C... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,825 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FOOD', 'TRIP', 'SA', 'CLASSROOM', '!', 'Si', 'maestro', 'Jeric', 'Bocter', 'Maribao', 'wala', 'lang', 'mohatag', 'og', 'instruksiyon', ',', 'apan', 'naghatag', 'usab', 'siya', 'og', 'la... | [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,826 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'babaye', 'nga', 'pasyente', 'anaa', 'karon', 'sa', 'isolation', 'sa', 'Anislas', 'Infirmary', 'Center', ',', 'sumala', 'ni', 'Albay', 'Governor', 'Grex', 'Lagman.', 'Si', 'Lagman... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 5, 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, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,827 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi', 'luwa', 'nimo', 'kay', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of... | [0, 0, 0, 0, 0] | cebuaner |
4,828 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['What', 'if', 'kanang', 'gikaon', 'nato', 'nga', 'manok', ',', 'naa', 'pud', 'na', 'silay', 'pangandoy', 'sa', 'kinabuhi', '?'] Use the following schema: 1 = B-WIS: Beginning of a touris... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,829 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'ikaduhang', 'public', 'hearing', 'sa', 'Tatak', 'Pinoy', 'Bill', '(', 'Senate', 'Bill', 'No.', '2218', ')', ',', 'ang', 'tagsulat', 'sa', 'maong', 'balaudnon', ',', 'si', 'Sen.', ... | [0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 7, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0... | cebuaner |
4,830 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Police', 'Colonel', 'Noel', 'Aliño', ',', 'acting', 'director', 'sa', 'Bacolod', 'City', 'Police', 'Office', '(', 'BCPO', ')', ',', 'nipahibawo', 'karong', 'Miyerkules', ',', 'Hun... | [0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0] | cebuaner |
4,831 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ako', 'na', 'nagtampo', ',', 'ako', 'pa', 'gihapon', 'manuyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-rela... | [0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,832 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Commission', 'on', 'Human', 'Rights', '(', 'CHR', ')', 'sa', 'Negros', 'Occidental', 'mipaambit', 'karong', 'Martes', ',', 'Hunyo', '20', ',', 'nga', 'ang', 'pasiunang', 'imbesti... | [0, 3, 4, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 3, 4, 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] | cebuaner |
4,833 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Senador', 'Sonny', 'Angara', 'maoy', 'usa', 'sa', 'mga', 'tagsulat', 'sa', 'Mental', 'Health', 'Law', ',', 'nga', 'sukad', 'sa', 'pagkahimo', 'niini', 'niadtong', '2018', 'nagdala... | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 7, 8, 8, 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, 0, 1, 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, 7, 8, 8, 0, 0, 0, 7, 8, 8, 8, 0] | cebuaner |
4,834 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'taho', ',', 'ang', 'biktima', 'miadto', 'sa', 'pulisya', 'sa', 'Sityo', 'Pamongot', 'sa', 'Barangay', 'Talagunton', 'aron', 'paisumbong', 'nga', 'sagpaon', 'unta', 'siya', 'sa', '... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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] | cebuaner |
4,835 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'post', 'sa', 'usa', 'ka', 'netizen', 'nga', 'si', 'Crey', 'de', 'Luna', ',', 'niani', 'og', 'Laughing', 'trip', 'online', ',', 'human', 'niya', 'gipaambit', 'ang', 'istorya', 'sa... | [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,836 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giprayoridad', 'sa', 'Cebu-Cordova', 'Link', 'Expressway', 'Corporation', '(', 'CCLEC', ')', 'ni', 'Manny', 'V.', 'Pangilinan', 'ang', 'kaluwasan', 'sa', 'mga', 'motorista', ',', 'biker... | [0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
4,837 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SO', 'UBOS', 'NA', 'ANG', 'PAGSABOT', 'NI', 'MAMI', 'ONI', '?', 'Sa', 'Instagram', 'story', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'karong', 'Huwebes'... | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,838 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pamahayag', 'ni', 'Department', 'of', 'Education', '(', 'DepEd', ')', '-Region', '7', 'Director', 'Salustiano', 'Jimenez', 'niadtong', 'Martes', ',', 'Hunyo', '20', ',', 'siya', '... | [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,839 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INIT', 'KAAYO', 'BRETMAN', '!', 'Nagustohan', 'sa', 'mga', 'netizen', 'ang', 'post', 'sa', 'social', 'media', 'sa', 'Filipino-American', 'influencer', 'nga', 'nakabase', 'sa', 'Honolulu... | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 5, 6, 6, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,840 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BOMB', 'SCARE', 'Mga', 'ginikanan', 'nagdali', 'sa', 'pagkuha', 'sa', 'ilang', 'mga', 'anak', 'sa', 'Sta.', 'Ana', 'Elementary', 'School', 'sa', 'Davao', 'City', 'human', 'sa', 'gikatah... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 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] | cebuaner |
4,841 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pirme', 'na', 'lang', 'kuno', 'ko', 'nagbusangot.', 'Unsaon', '?', 'Kay', 'ang', 'usa', 'diha', 'happy', 'na', 'sa', 'uban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tou... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,842 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'hepe', 'sa', 'pulisya', 'nga', 'si', 'David', 'Smith', 'nagsulti', 'sa', 'lokal', 'nga', 'media', 'kaniadtong', 'Martes', 'nga', 'ang', '31-anyos', 'nga', 'si', 'Laura', 'Ilg', '... | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,843 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ibinunyag', 'ni', 'Pangulong', 'Ferdinand', '‘Bongbong’', 'Marcos', 'Jr.', 'ngayong', 'Huwebes', ',', 'Hunyo', '22', ',', 'na', 'agaran', 'nitong', 'pipirmahan', 'ang', 'panukalang', 'b... | [0, 0, 0, 1, 2, 2, 2, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,844 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UYON', '?', 'Mao', 'kini', 'ang', 'komento', 'sa', 'TV', 'host-actress', 'nga', 'si', 'Maine', 'Mendoza', 'sa', 'interbyu', 'karong', 'Martes', ',', 'Hunyo', '20', ',', 'human', 'sa', '... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0] | cebuaner |
4,845 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['sumala', 'ni', 'Ricky', 'Tijon', ',', 'Regional', 'Beneficiary', 'Data', 'Management', 'Officer', 'sa', 'Pantawid', 'Pamilya', 'Division', 'Davao', 'Field', 'Office.', 'Matod', 'ni', 'T... | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,846 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['saad', 'sa', 'usa', 'ka', 'fan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginni... | [0, 0, 0, 0, 0, 0] | cebuaner |
4,847 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pinaagi', 'sa', 'Republic', 'Act', '9504', 'nga', 'co-author', 'ni', 'Sen.', 'Sonny', 'Angara', ',', 'gipatuman', 'ang', 'income', 'tax', 'exemption', 'alang', 'niadtong', 'nagsweldo', ... | [0, 0, 7, 8, 8, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,848 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'tubag', 'sa', 'Miss', 'Grand', 'International', 'Philippines', '2023', 'contestant', 'nga', 'si', 'Herlene', 'Nicole', ''Hipon', 'Girl', ''', 'Budol', 'sa', 'interview', 'portion... | [0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,849 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'Philippine', 'Atmospheric', ',', 'Geophysical', 'and', 'Astronomical', 'Services', 'Administration', '(', 'PAGASA', ')', ',', 'alas', '5:28', 'sa', 'buntag.', 'pagsubang'... | [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,850 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Police', 'Major', 'Ivy', 'Bartolome', ',', 'hepe', 'sa', 'Argao', 'Police', ',', 'nga', 'nahitabo', 'ang', 'insidente', 'mga', 'alas', '9:30', 'sa', 'buntag', 'niadtong',... | [0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,851 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'netizen', 'nga', 'si', 'Rissa', 'Ejedio', ',', 'inahan', 'sa', 'tres', 'anyos', 'ug', '10', 'ka', 'buwan', 'nga', 'bata', 'nakakita', 'sa', 'kapin', 'o', 'kulang', 'siyam', 'ka',... | [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, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,852 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Isip', 'usa', 'ka', 'advocate', 'sa', 'Tatak', 'Pinoy', 'ug', 'author', 'sa', 'Tatak', 'Pinoy', 'Bill', '(', 'Senate', 'Bill', 'No.', '2218', ')', ',', 'nagtuo', 'si', 'Sen.', 'Sonny', ... | [0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 1, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,853 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TINUOD', 'ANG', 'BALITA', '!', 'Sa', ''Fast', 'Talk', 'with', 'Boy', 'Abunda', ''', 'karong', 'Miyerkules', ',', 'Hunyo', '21', ',', 'gibutyag', 'sa', 'King', 'of', 'Talk', 'nga', 'si',... | [0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,854 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nganong', 'mag-sorry', 'man', 'ko', 'niya', ',', 'eh', 'Pride', 'Month', 'man', 'karon', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Cont... | [0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0] | cebuaner |
4,855 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'pakigpulong', 'sa', 'graduation', 'ceremony', 'sa', 'Far', 'Eastern', 'University-Nicanor', 'Reyes', 'Medical', 'Foundation', ',', 'gipahinumdoman', 'ni', 'Alfred', 'Varg... | [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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] | cebuaner |
4,856 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mapasigarbuhon', 'nga', 'gipaambit', 'sa', 'usa', 'ka', 'summa', 'cum', 'laude', 'graduate', 'sa', 'West', 'Visayas', 'State', 'University', 'human', 'niya', 'gipaambit', 'ang', 'tribut... | [0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,857 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'maoy', 'gipaambit', 'ni', 'Davao', 'City', 'Police', 'Major', 'Joenel', 'Pederio', ',', 'hepe', 'sa', 'Buhangin', 'Police', 'Station', ',', 'human', 'nila', 'gipangutana', 'ang'... | [0, 0, 0, 0, 5, 6, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0] | cebuaner |
4,858 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Aw', 'nagrepair', 'diay', ',', 'abi', 'ko', 'ga', 'swimming', '"', 'Kini', 'ang', 'komedya', 'nga', 'komento', 'sa', 'usa', 'ka', 'netizen', 'sa', 'mga', 'litrato', ',', 'sa', 'laing',... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,859 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Office', 'of', 'Civil', 'Defense', '(', 'OCD', ')', 'Administrator', 'Undersecretary', 'Ariel', 'Nepomuceno', ',', 'ang', 'mga', 'hingtungdan', 'nga', 'ahensya', 's... | [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0] | cebuaner |
4,860 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'opisyal', 'na', 'Twitter', 'account', 'ng', 'GTV', 'ng', 'GMA', 'Network', 'ay', 'nag-post', 'ng', 'art', 'card', 'ng', 'noontime', 'show', 'ng', 'ABS-CBN', 'na', '‘It’s', 'Showt... | [0, 0, 0, 7, 0, 0, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,861 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'sa', 'gipa-isip', 'taka', ',', 'pero', 'basin', 'mao', 'dugay', 'siya', 'moreply', 'kay', 'nakigsulti', 'na', 'sa', 'uban', '?'] Use the following schema: 1 = B-WIS: Beginning o... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,862 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakigpulong', 'ni', 'Quezon', 'City', '5th', 'District', 'Councilor', 'Alfred', 'Vargas', 'atol', 'sa', 'Independence', 'Day', 'Job', 'Fairs', 'niadtong', 'Hunyo', '12', 'sa', 'SM... | [0, 0, 0, 5, 6, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5] | cebuaner |
4,863 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'utanon', 'ug', 'produkto', 'nga', 'gipang-apod-apod', 'gikan', 'sa', 'mga', 'grupo', 'sa', 'kababayen-an', 'nga', 'mag-uuma', 'nga', 'gipasiugdahan', 'ni', 'Albay', '3rd',... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0... | cebuaner |
4,864 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Kapitan', 'Melvin', 'Mercado', ',', 'commander', 'sa', 'Police', 'Station', '8', ',', 'nagkanayon', 'nga', 'niadtong', 'Biyernes', ',', 'Hunyo', '16', ',', 'nasikop', 'ang', 'susp... | [0, 0, 1, 2, 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, 1, 2, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,865 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpa-blood', 'test', 'ko.', 'Ingon', 'sa', 'test', ',', 'ikaw', 'lang', 'type', 'ko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Contin... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,866 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BBM', ',', 'DILI', 'GANAHAN', 'OG', 'FAKE', 'NEWS', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity;... | [1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,867 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SALAMAT', ',', 'DOC', '!', ''', 'Nalipay', 'ang', 'mga', 'netizen', 'sa', 'tubag', 'sa', 'vlogger', 'nga', 'si', 'Toni', 'Fowler', 'sa', 'dihang', 'iyang', 'giklaro', 'ang', 'pamahayag'... | [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, 5, 6, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,868 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'paghatag', 'sa', 'unang', 'geographical', 'indication', '(', 'GI', ')', 'sa', 'mga', 'mangga', 'sa', 'Guimaras', ',', 'si', 'Sen.', 'Sonny', 'Angara', ',', 'isip', 'tagsulat', 'sa... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 2, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0... | cebuaner |
4,869 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Roly', 'napalgang', 'patay', 'duol', 'sa', 'ilang', 'payag', 'niadtong', 'Hunyo', '14', ',', 'samtang', 'ang', 'lawas', 'sa', 'iyang', 'asawa', 'ug', 'mga', 'anak', 'nadiskubrehan... | [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, 1, 0, 1, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,870 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kung', 'biyaan', 'nimo', 'tali', 'sa', 'payong', 'o', 'panyo', ',', 'nganong', 'ako', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continu... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,871 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HAPPY', 'BIRTHDAY', ',', 'PEPE', '!', 'Gihandum', 'sa', 'mga', 'Pilipino', 'karong', 'Lunes', ',', 'Hunyo', '19', ',', '2023', ',', 'ang', 'ika-162', 'nga', 'anibersaryo', 'sa', 'pagkat... | [0, 0, 0, 1, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,872 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['June', '18', ',', '2023.', 'HAPPY', 'FATHER’S', 'DAY', '!', 'Saludo', 'sa', 'mga', 'paningkamot', ',', 'sakripisyo', ',', 'ug', 'gugma', 'sa', 'atong', 'mga', 'haligi', 'sa', 'panimalay... | [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, 0, 0, 0, 0, 0, 0, 1, 2] | cebuaner |
4,873 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['June', '18', ',', '2023.', 'ADLAW', 'SA', 'MGA', 'AMAHAN', '!', 'Sa', 'tunga-tunga', 'sa', 'selebrasyon', ',', 'ayaw', 'kalimot', 'sa', 'pagpasalamat', 'sa', 'walay', 'puas', 'nga', 'su... | [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, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 1, 2] | cebuaner |
4,874 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['June', '18', ',', '2023.', 'PALANGGA', 'TAKA', ',', 'TAY', '!', 'Ang', 'Pilipinas', 'Today', 'naghatag', 'pasidungog', 'sa', 'mga', 'superhero', 'sa', 'pamilya.', 'Tatay', ',', 'Tatang'... | [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, 0, 0, 0, 7, 8, 0, 0] | cebuaner |
4,875 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Fake', 'pala', 'giatay', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a ... | [0, 0, 0, 0] | cebuaner |
4,876 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGNA', 'MOM', 'LAUDE', 'Batan-ong', 'inahan', 'si', 'Jackilyn', 'Andres', 'apan', 'wala', 'siya', 'mohunong', 'sa', 'pagkab-ot', 'sa', 'iyang', 'pangandoy', 'nga', 'makagradwar', 'bisa... | [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, 7, 8, 8, 0, 0, 0, 0] | cebuaner |
4,877 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DESERVE', ',', 'ZEINAB', '!', 'Trending', 'karon', 'sa', 'mga', 'netizen', 'ang', 'social', 'media', 'post', 'ni', 'Professional', 'Basketball', 'Player', 'Bobby', 'Ray', 'Parks', 'Jr.'... | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 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, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,878 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Parehong', 'ganahan', 'og', 'sports', 'si', 'Sonny', 'Angara', 'ug', 'ang', 'iyang', 'kamagulangang', 'anak', 'nga', 'si', 'Manolo', ',', 'mao', 'nga', 'karong', 'Mayo', '6', ',', 'iyan... | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 3, 4, 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] | cebuaner |
4,879 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaambit', 'ni', 'Department', 'of', 'Tourism', '(', 'DOT', ')', '-Western', 'Visayas', 'Director', 'Crisanta', 'Marlene', 'Rodriguez', 'nga', 'ang', 'CPTEx', 'usa', 'ka', 'binuhi', 'n... | [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 1, 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, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 6, 0, 0, 0, 5, 0... | cebuaner |
4,880 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Danny', 'Ramirez', ',', '40', ',', 'molupyo', 'sa', 'Sitio', 'Bogo', ',', 'Barangay', 'Curva', ',', 'dakbayan', 'sa', 'Ormoc', ',', 'nipasilong', 'sulod', 'sa', 'kawayan', 'nga', ... | [0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,881 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'direktang', 'reaksyon', 'ni', 'kanhi', 'senador', 'Manny', 'Pacquiao', 'kon', 'modagan', 'siya', 'pag-usab', 'sa', 'sunod', 'nga', 'Eleksyon', '.'] Use the following sche... | [0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,882 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisag', 'layo', 'ka', 'sa', 'akoa', ',', 'gusto', 'ko', 'ma-feel', 'ka', 'nako.', 'Wala'y', 'labaw', ',', 'walay', 'kulang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tou... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,883 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GRABE', 'KA', 'LUOY', 'BA', '!', 'Base', 'sa', 'findings', 'sa', 'kapulisan', ',', 'hingpit', 'nga', 'nasunog', 'ang', 'ulo', 'ug', 'bukton', 'ni', 'Tulabing', 'samtang', 'grabeng', 'pa... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] | cebuaner |
4,884 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'komento', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'sa', 'post', 'sa', 'aktres', 'nga', 'si', 'Kakai', 'Bautista', 'sa', 'dihang',... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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] | cebuaner |
4,885 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Grabe', 'kaayo', 'akong', 'cravings', '!', 'Dili', 'na', 'pagkaon', ',', 'ang', 'imong', 'atensyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 =... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,886 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Basin', 'mao', 'to', 'nga', 'wala', 'ka', 'gipaglaban', ',', 'kay', 'mura', 'kag', 'kalaban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS:... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,887 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KA-KUYAW', 'BA', 'ANI', 'UY', '!', 'Gironda', 'sa', '103rd', 'Brigade', 'sa', 'Philippine', 'Army', 'ang', 'gidudahang', 'hide-out', 'ni', 'Islamic', 'State', '(', 'IS', ')', 'extremist... | [0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 3, 4, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 1, 2, 0, 5, 6, 6, 6, 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, 5, 6, 0] | cebuaner |
4,888 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nunot', 'sa', 'magnitude', '6.3', 'nga', 'linog', 'nga', 'mitay-og', 'sa', 'Calatagan', ',', 'Batangas', ',', 'nga', 'nabatyagan', 'sa', 'mga', 'silingang', 'lugar', 'hangtod', 'sa', 'M... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0... | cebuaner |
4,889 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UYON', 'BA', 'MO', ',', 'BOYS', '?', 'Nakaani', 'og', 'nagkadaiyang', 'reaksyon', 'ang', 'tubag', 'sa', 'mga', 'netizen', 'sa', 'Facebook', 'post', 'sa', 'social', 'media', 'personality... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,890 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Guard', ',', 'gusto', 'lang', 'nako', 'iyang', 'lambing', '!', '!', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,891 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'taho', ',', 'ang', 'biktima', 'natulog', 'uban', 'sa', 'iyang', 'igsuon', 'ug', 'sa', 'iyang', 'asawa', 'dihang', 'gilabayan', 'og', 'granada', 'ang', 'balay', 'sa', 'bikt... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,892 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'pamahayag', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'sa', 'iyang', 'Facebook', 'post', 'karong', 'Miyerkules', ',', 'Hunyo', '14'... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,893 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pag-amendar', 'sa', 'PDIC', '(', 'Philippine', 'Deposit', 'Insurance', 'Corporation', ')', 'Charter', 'Act', '(', 'R.A.', '11840', ')', 'sa', 'miaging', 'tuig', ',', 'si', 'Senado... | [0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 4, 4, 4, 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... | cebuaner |
4,894 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Cebu', 'City', 'Police', 'Office', '(', 'CCPO', ')', 'niingon', 'nga', 'ang', 'mga', 'nasakmit', 'nga', 'ilegal', 'nga', 'drugas', 'gikan', 'sa', 'Enero', 'hangtod', 'Mayo', 'nia... | [0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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] | cebuaner |
4,895 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Barangayan', 'gihimo', 'sa', 'Sugbo', 'aron', 'mas', 'makapangandam', 'sa', 'sunod', 'tuig', 'nga', 'Sinulog.', 'Matod', 'ni', 'Cebu', 'City', 'Mayor', 'Michael', 'Rama', 'nga', ... | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0] | cebuaner |
4,896 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Na-', 'laugh', 'trip', 'ang', 'netizen', 'kay', 'Juliane', 'Sanchez', 'Obradorll', 'sa', 'Cagayan', 'de', 'Oro', 'human', 'niya', 'gi-post', 'ang', 'iyang', 'kasagmuyo', 'sa', 'iyang', ... | [0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 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, 6, 0, 0, 0... | cebuaner |
4,897 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mipasalamat', 'kang', 'House', 'Speaker', 'Martin', 'Romualdez', 'ang', 'independent', 'minority', 'congressman', 'ug', 'Liberal', 'Party', '(', 'LP', ')', 'President', ',', 'Albay', '1... | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 5, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0... | cebuaner |
4,898 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giganahan', 'ang', 'mga', 'netizen', 'sa', 'Instagram', 'post', 'ni', 'Actress', 'ug', 'ang', 'unang', 'Transgender', 'News', 'Anchor', 'sa', 'Frontline', 'Pilipinas', ',', 'KaladKaren'... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,899 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Namulong', 'sa', 'kapin', 'sa', '1,000', 'ka', 'mga', 'propesyonal', 'nga', 'tigpamuhunan', 'ug', 'mga', 'eksperto', 'sa', 'industriya', 'nga', 'mitambong', 'sa', 'Nomura', 'Investment'... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 2, 2, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... | cebuaner |
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