Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Size:
1K<n<10K
License:
File size: 6,831 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""bAbI_nli datasets"""
from __future__ import absolute_import, division, print_function
import csv
import os
import textwrap
import six
import datasets
bAbI_nli_CITATION = r"""@article{weston2015towards,
title={Towards ai-complete question answering: A set of prerequisite toy tasks},
author={Weston, Jason and Bordes, Antoine and Chopra, Sumit and Rush, Alexander M and Van Merri{\"e}nboer, Bart and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1502.05698},
year={2015}
}
"""
_babi_nli_DESCRIPTION = """\
bAbi tasks recasted as natural language inference.
"""
DATA_URL = "https://www.dropbox.com/s/0b98tbrv2mej3cu/babi_nli.zip?dl=1"
LABELS=["not-entailed", "entailed"]
CONFIGS=['single-supporting-fact',
'two-supporting-facts',
'three-supporting-facts',
'two-arg-relations',
'three-arg-relations',
'yes-no-questions',
'counting',
'lists-sets',
'simple-negation',
'indefinite-knowledge',
'basic-coreference',
'conjunction',
'compound-coreference',
'time-reasoning',
'basic-deduction',
'basic-induction',
'positional-reasoning',
'size-reasoning',
'path-finding',
'agents-motivations']
class bAbI_nli_Config(datasets.BuilderConfig):
"""BuilderConfig for bAbI_nli."""
def __init__(
self,
text_features,
label_classes=None,
process_label=lambda x: x,
**kwargs,
):
"""BuilderConfig for bAbI_nli.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
label_column: `string`, name of the column in the tsv file corresponding
to the label
data_url: `string`, url to download the zip file from
data_dir: `string`, the path to the folder containing the tsv files in the
downloaded zip
citation: `string`, citation for the data set
url: `string`, url for information about the data set
label_classes: `list[string]`, the list of classes if the label is
categorical. If not provided, then the label will be of type
`datasets.Value('float32')`.
process_label: `Function[string, any]`, function taking in the raw value
of the label and processing it to the form required by the label feature
**kwargs: keyword arguments forwarded to super.
"""
super(bAbI_nli_Config, self).__init__(
version=datasets.Version("1.0.0", ""), **kwargs
)
self.text_features = text_features
self.label_column = "label"
self.label_classes = LABELS
self.data_url = DATA_URL
self.data_dir = self.name #os.path.join("babi_nli", self.name)
self.citation = textwrap.dedent(bAbI_nli_CITATION)
self.process_label = lambda x: str(x)
self.description = ""
self.url = "https://github.com/facebookarchive/bAbI-tasks/blob/master/LICENSE.md"
class bAbI_nli(datasets.GeneratorBasedBuilder):
"""The General Language Understanding Evaluation (bAbI_nli) benchmark."""
BUILDER_CONFIG_CLASS = bAbI_nli_Config
BUILDER_CONFIGS = [
bAbI_nli_Config(
name=name,
text_features={"premise": "premise", "hypothesis": "hypothesis"},
) for name in CONFIGS
]
def _info(self):
features = {
text_feature: datasets.Value("string")
for text_feature in six.iterkeys(self.config.text_features)
}
if self.config.label_classes:
features["label"] = datasets.features.ClassLabel(
names=self.config.label_classes
)
else:
features["label"] = datasets.Value("float32")
features["idx"] = datasets.Value("int32")
return datasets.DatasetInfo(
description=_babi_nli_DESCRIPTION,
features=datasets.Features(features),
homepage=self.config.url,
citation=self.config.citation + "\n" + bAbI_nli_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(self.config.data_url)
data_dir = os.path.join(dl_dir, self.config.data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": os.path.join(data_dir or "", "train.tsv"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": os.path.join(data_dir or "", "validation.tsv"),
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": os.path.join(data_dir or "", "test.tsv"),
"split": "test",
},
),
]
def _generate_examples(self, data_file, split):
process_label = self.config.process_label
label_classes = self.config.label_classes
with open(data_file, encoding="utf8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for n, row in enumerate(reader):
example = {
feat: row[col]
for feat, col in six.iteritems(self.config.text_features)
}
example["idx"] = n
if self.config.label_column in row:
label = row[self.config.label_column]
if label_classes and label not in label_classes:
label = int(label) if label else None
example["label"] = process_label(label)
else:
example["label"] = process_label(-1)
yield example["idx"], example
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