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# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MNLI dataset."""
from megatron import print_rank_0
from tasks.data_utils import clean_text
from .data import GLUEAbstractDataset
LABELS = {'contradiction': 0, 'entailment': 1, 'neutral': 2}
class MNLIDataset(GLUEAbstractDataset):
def __init__(self, name, datapaths, tokenizer, max_seq_length,
test_label='contradiction'):
self.test_label = test_label
super().__init__('MNLI', name, datapaths,
tokenizer, max_seq_length)
def process_samples_from_single_path(self, filename):
""""Implement abstract method."""
print_rank_0(' > Processing {} ...'.format(filename))
samples = []
total = 0
first = True
is_test = False
with open(filename, 'r') as f:
for line in f:
row = line.strip().split('\t')
if first:
first = False
if len(row) == 10:
is_test = True
print_rank_0(
' reading {}, {} and {} columns and setting '
'labels to {}'.format(
row[0].strip(), row[8].strip(),
row[9].strip(), self.test_label))
else:
print_rank_0(' reading {} , {}, {}, and {} columns '
'...'.format(
row[0].strip(), row[8].strip(),
row[9].strip(), row[-1].strip()))
continue
text_a = clean_text(row[8].strip())
text_b = clean_text(row[9].strip())
unique_id = int(row[0].strip())
label = row[-1].strip()
if is_test:
label = self.test_label
assert len(text_a) > 0
assert len(text_b) > 0
assert label in LABELS
assert unique_id >= 0
sample = {'text_a': text_a,
'text_b': text_b,
'label': LABELS[label],
'uid': unique_id}
total += 1
samples.append(sample)
if total % 50000 == 0:
print_rank_0(' > processed {} so far ...'.format(total))
print_rank_0(' >> processed {} samples.'.format(len(samples)))
return samples
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