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Sep 18, 2018
Merged

fixed ParseTextField postprocessing, added genre field to MultiNLI #386

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56bbef6
fixed ParseTextField postprocessing function, added option to whether…
Sep 17, 2018
328d8c4
Merge branch 'master' into master
putama Sep 17, 2018
e6bae40
fix code lines length
Sep 17, 2018
be25f61
Merge branch 'master' of https://github.com/putama/text
Sep 17, 2018
cb8e946
remove lambda expression
Sep 18, 2018
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67 changes: 37 additions & 30 deletions test/nli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,58 +2,65 @@
from torchtext import datasets

# Testing SNLI
TEXT = data.Field()
LABEL = data.Field(sequential=False)
print("Run test on SNLI...")
TEXT = datasets.nli.ParsedTextField()
LABEL = data.LabelField()
TREE = datasets.nli.ShiftReduceField()

train, val, test = datasets.SNLI.splits(TEXT, LABEL)
train, val, test = datasets.SNLI.splits(TEXT, LABEL, TREE)

print(train.fields)
print(len(train))
print(vars(train[0]))
print("Fields:", train.fields)
print("Number of examples:\n", len(train))
print("First Example instance:\n", vars(train[0]))

TEXT.build_vocab(train)
LABEL.build_vocab(train)

train_iter, val_iter, test_iter = data.BucketIterator.splits(
(train, val, test), batch_size=3)
train_iter, val_iter, test_iter = data.Iterator.splits((train, val, test), batch_size=3)

batch = next(iter(train_iter))
print(batch.premise)
print(batch.hypothesis)
print(batch.label)
print("Numericalize premises:\n", batch.premise)
print("Numericalize hypotheses:\n", batch.hypothesis)
print("Entailment labels:\n", batch.label)

train_iter, val_iter, test_iter = datasets.SNLI.iters(batch_size=4)
print("Test iters function")
train_iter, val_iter, test_iter = datasets.SNLI.iters(batch_size=4, trees=True)

batch = next(iter(train_iter))
print(batch.premise)
print(batch.hypothesis)
print(batch.label)
print("Numericalize premises:\n", batch.premise)
print("Numericalize hypotheses:\n", batch.hypothesis)
print("Entailment labels:\n", batch.label)


# Testing MultiNLI
TEXT = data.Field()
LABEL = data.Field(sequential=False)
print("Run test on MultiNLI...")
TEXT = datasets.nli.ParsedTextField()
LABEL = data.LabelField()
GENRE = data.LabelField()
TREE = datasets.nli.ShiftReduceField()

train, val, test = datasets.MultiNLI.splits(TEXT, LABEL)
train, val, test = datasets.MultiNLI.splits(TEXT, LABEL, TREE, GENRE)

print(train.fields)
print(len(train))
print(vars(train[0]))
print("Fields:", train.fields)
print("Number of examples:\n", len(train))
print("First Example instance:\n", vars(train[0]))

TEXT.build_vocab(train)
LABEL.build_vocab(train)
GENRE.build_vocab(train, val, test)

train_iter, val_iter, test_iter = data.BucketIterator.splits(
(train, val, test), batch_size=3)
train_iter, val_iter, test_iter = data.Iterator.splits((train, val, test), batch_size=3)

batch = next(iter(train_iter))
print(batch.premise)
print(batch.hypothesis)
print(batch.label)
print("Numericalize premises:\n", batch.premise)
print("Numericalize hypotheses:\n", batch.hypothesis)
print("Entailment labels:\n", batch.label)
print("Genre categories:\n", batch.genre)

train_iter, val_iter, test_iter = datasets.MultiNLI.iters(batch_size=4)
print("Test iters function")
train_iter, val_iter, test_iter = datasets.MultiNLI.iters(batch_size=4, trees=True)

batch = next(iter(train_iter))
print(batch.premise)
print(batch.hypothesis)
print(batch.label)
print("Numericalize premises:\n", batch.premise)
print("Numericalize hypotheses:\n", batch.hypothesis)
print("Entailment labels:\n", batch.label)
78 changes: 50 additions & 28 deletions torchtext/datasets/nli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,23 @@ def __init__(self):


class ParsedTextField(data.Field):

def __init__(self, eos_token='<pad>', lower=False):

super(ParsedTextField, self).__init__(
eos_token=eos_token, lower=lower, preprocessing=lambda parse: [
t for t in parse if t not in ('(', ')')],
postprocessing=lambda parse, _, __: [
list(reversed(p)) for p in parse])
"""
Field for parsed sentences data in NLI datasets.
Expensive tokenization could be omitted from the pipeline as
the parse tree annotations are already in tokenized form.
"""
def __init__(self, eos_token='<pad>', lower=False, reverse=False):
if reverse:
super(ParsedTextField, self).__init__(
eos_token=eos_token, lower=lower,
preprocessing=lambda parse: [t for t in parse if t not in ('(', ')')],
postprocessing=lambda parse, _: [list(reversed(p)) for p in parse],
include_lengths=True)
else:
super(ParsedTextField, self).__init__(
eos_token=eos_token, lower=lower,
preprocessing=lambda parse: [t for t in parse if t not in ('(', ')')],
include_lengths=True)


class NLIDataset(data.TabularDataset):
Expand All @@ -34,8 +43,9 @@ def sort_key(ex):
len(ex.premise), len(ex.hypothesis))

@classmethod
def splits(cls, text_field, label_field, parse_field=None, root='.data',
train='train.jsonl', validation='val.jsonl', test='test.jsonl'):
def splits(cls, text_field, label_field, parse_field=None,
extra_fields={}, root='.data', train='train.jsonl',
validation='val.jsonl', test='test.jsonl'):
"""Create dataset objects for splits of the SNLI dataset.

This is the most flexible way to use the dataset.
Expand All @@ -46,6 +56,7 @@ def splits(cls, text_field, label_field, parse_field=None, root='.data',
label_field: The field that will be used for label data.
parse_field: The field that will be used for shift-reduce parser
transitions, or None to not include them.
extra_field: A dict[json_key: Tuple(field_name, Field)]
root: The root directory that the dataset's zip archive will be
expanded into.
train: The filename of the train data. Default: 'train.jsonl'.
Expand All @@ -57,21 +68,23 @@ def splits(cls, text_field, label_field, parse_field=None, root='.data',
path = cls.download(root)

if parse_field is None:
return super(NLIDataset, cls).splits(
path, root, train, validation, test,
format='json', fields={'sentence1': ('premise', text_field),
'sentence2': ('hypothesis', text_field),
'gold_label': ('label', label_field)},
filter_pred=lambda ex: ex.label != '-')
fields = {'sentence1': ('premise', text_field),
'sentence2': ('hypothesis', text_field),
'gold_label': ('label', label_field)}
else:
fields = {'sentence1_binary_parse': [('premise', text_field),
('premise_transitions', parse_field)],
'sentence2_binary_parse': [('hypothesis', text_field),
('hypothesis_transitions', parse_field)],
'gold_label': ('label', label_field)}

for key in extra_fields:
if key not in fields.keys():
fields[key] = extra_fields[key]

return super(NLIDataset, cls).splits(
path, root, train, validation, test,
format='json', fields={'sentence1_binary_parse':
[('premise', text_field),
('premise_transitions', parse_field)],
'sentence2_binary_parse':
[('hypothesis', text_field),
('hypothesis_transitions', parse_field)],
'gold_label': ('label', label_field)},
format='json', fields=fields,
filter_pred=lambda ex: ex.label != '-')

@classmethod
Expand Down Expand Up @@ -122,8 +135,9 @@ class SNLI(NLIDataset):
def splits(cls, text_field, label_field, parse_field=None, root='.data',
train='snli_1.0_train.jsonl', validation='snli_1.0_dev.jsonl',
test='snli_1.0_test.jsonl'):
return super(SNLI, cls).splits(text_field, label_field, parse_field,
root, train, validation, test)
return super(SNLI, cls).splits(text_field, label_field, parse_field=parse_field,
root=root, train=train, validation=validation,
test=test)


class MultiNLI(NLIDataset):
Expand All @@ -132,9 +146,17 @@ class MultiNLI(NLIDataset):
name = 'multinli'

@classmethod
def splits(cls, text_field, label_field, parse_field=None, root='.data',
def splits(cls, text_field, label_field, parse_field=None, genre_field=None,
root='.data',
train='multinli_1.0_train.jsonl',
validation='multinli_1.0_dev_matched.jsonl',
test='multinli_1.0_dev_mismatched.jsonl'):
return super(MultiNLI, cls).splits(text_field, label_field, parse_field,
root, train, validation, test)
extra_fields = {}
if genre_field is not None:
extra_fields["genre"] = ("genre", genre_field)

return super(MultiNLI, cls).splits(text_field, label_field,
parse_field=parse_field,
extra_fields=extra_fields,
root=root, train=train,
validation=validation, test=test)