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"""QQP dataset.""" |
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from megatron import print_rank_0 |
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from tasks.data_utils import clean_text |
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from .data import GLUEAbstractDataset |
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LABELS = [0, 1] |
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class QQPDataset(GLUEAbstractDataset): |
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def __init__(self, name, datapaths, tokenizer, max_seq_length, |
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test_label=0): |
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self.test_label = test_label |
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super().__init__('QQP', name, datapaths, |
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tokenizer, max_seq_length) |
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def process_samples_from_single_path(self, filename): |
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""""Implement abstract method.""" |
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print_rank_0(' > Processing {} ...'.format(filename)) |
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samples = [] |
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total = 0 |
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first = True |
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is_test = False |
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with open(filename, 'r') as f: |
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for line in f: |
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row = line.strip().split('\t') |
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if first: |
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first = False |
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if len(row) == 3: |
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is_test = True |
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print_rank_0(' reading {}, {}, and {} columns and ' |
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'setting labels to {}'.format( |
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row[0].strip(), row[1].strip(), |
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row[2].strip(), self.test_label)) |
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else: |
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assert len(row) == 6 |
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print_rank_0(' reading {}, {}, {}, and {} columns' |
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' ...'.format( |
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row[0].strip(), row[3].strip(), |
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row[4].strip(), row[5].strip())) |
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continue |
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if is_test: |
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assert len(row) == 3, 'expected length 3: {}'.format(row) |
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uid = int(row[0].strip()) |
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text_a = clean_text(row[1].strip()) |
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text_b = clean_text(row[2].strip()) |
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label = self.test_label |
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assert len(text_a) > 0 |
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assert len(text_b) > 0 |
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else: |
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if len(row) == 6: |
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uid = int(row[0].strip()) |
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text_a = clean_text(row[3].strip()) |
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text_b = clean_text(row[4].strip()) |
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label = int(row[5].strip()) |
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else: |
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print_rank_0('***WARNING*** index error, ' |
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'skipping: {}'.format(row)) |
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continue |
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if len(text_a) == 0: |
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print_rank_0('***WARNING*** zero length a, ' |
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'skipping: {}'.format(row)) |
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continue |
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if len(text_b) == 0: |
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print_rank_0('***WARNING*** zero length b, ' |
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'skipping: {}'.format(row)) |
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continue |
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assert label in LABELS |
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assert uid >= 0 |
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sample = {'uid': uid, |
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'text_a': text_a, |
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'text_b': text_b, |
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'label': label} |
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total += 1 |
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samples.append(sample) |
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if total % 50000 == 0: |
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print_rank_0(' > processed {} so far ...'.format(total)) |
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print_rank_0(' >> processed {} samples.'.format(len(samples))) |
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return samples |
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