<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def ner(sentence, format=None):
 """ Location and classify named entities in text Parameters ========== sentence: {unicode, str} raw sentence Returns ======= tokens: list of tuple with word, pos tag, chunking tag, ner tag tagged sentence Examples -------- [('Ông', 'Nc', 'B-NP', 'O'), ('Putin', 'Np', 'B-NP', 'B-PER'), ('ca ngợi', 'V', 'B-VP', 'O'), ('những', 'L', 'B-NP', 'O'), ('thành tựu', 'N', 'B-NP', 'O'), ('vĩ đại', 'A', 'B-AP', 'O'), ('của', 'E', 'B-PP', 'O'), ('Liên Xô', 'Np', 'B-NP', 'B-LOC')] """

    sentence = chunk(sentence)
    crf_model = CRFNERPredictor.Instance()
    result = crf_model.predict(sentence, format)
    return result