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# coding=utf-8
# Copyright 2020 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
import os
import datasets
_DESCRIPTION = """\
SimpleQuestions is a dataset for simple QA, which consists
of a total of 108,442 questions written in natural language by human
English-speaking annotators each paired with a corresponding fact,
formatted as (subject, relationship, object), that provides the answer
but also a complete explanation. Fast have been extracted from the
Knowledge Base Freebase (freebase.com). We randomly shuffle these
questions and use 70% of them (75910) as training set, 10% as
validation set (10845), and the remaining 20% as test set.
"""
_HOMEPAGE_URL = "https://research.fb.com/downloads/babi/"
_CITATION = """\
@misc{bordes2015largescale,
title={Large-scale Simple Question Answering with Memory Networks},
author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},
year={2015},
eprint={1506.02075},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
"""
_URL = "https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1"
class SimpleQuestionsV2Config(datasets.BuilderConfig):
def __init__(self, *args, data_type=None, **kwargs):
super().__init__(*args, version=datasets.Version("1.0.0", ""), **kwargs)
self.data_type = data_type
class SimpleQuestionsV2(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
SimpleQuestionsV2Config(name="annotated", data_type="annotated", description="Annotated dataset"),
SimpleQuestionsV2Config(name="freebase2m", data_type="freebase2m", description="Freebase subset 2M"),
SimpleQuestionsV2Config(name="freebase5m", data_type="freebase5m", description="Freebase subset 5M"),
]
BUILDER_CONFIG_CLASS = SimpleQuestionsV2Config
DEFAULT_CONFIG_NAME = "annotated"
def _info(self):
if self.config.data_type == "annotated":
features = datasets.Features(
{
"id": datasets.Value("string"),
"subject_entity": datasets.Value("string"),
"relationship": datasets.Value("string"),
"object_entity": datasets.Value("string"),
"question": datasets.Value("string"),
},
)
else:
features = datasets.Features(
{
"id": datasets.Value("string"),
"subject_entity": datasets.Value("string"),
"relationship": datasets.Value("string"),
"object_entities": datasets.Sequence(datasets.Value("string")),
},
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path = dl_manager.download_and_extract(_URL)
if self.config.data_type == "annotated":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")},
),
]
elif self.config.data_type == "freebase2m":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datapath": os.path.join(
path,
"SimpleQuestions_v2",
"freebase-subsets",
"freebase-FB2M.txt",
)
},
)
]
elif self.config.data_type == "freebase5m":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datapath": os.path.join(
path,
"SimpleQuestions_v2",
"freebase-subsets",
"freebase-FB5M.txt",
)
},
)
]
else:
raise Exception("Unknown data type. Try one of: annotated, freebase2m and freebase5m")
def _generate_examples(self, datapath):
if self.config.data_type == "annotated":
with open(datapath, encoding="utf-8") as f:
for sentence_counter, row in enumerate(f):
row = row.split("\t")
result = (
sentence_counter,
{
"id": str(sentence_counter),
"subject_entity": row[0],
"relationship": row[1],
"object_entity": row[2],
"question": row[3],
},
)
yield result
else:
with open(datapath, encoding="utf-8") as f:
for sentence_counter, row in enumerate(f):
row = row.split("\t")
result = (
sentence_counter,
{
"id": str(sentence_counter),
"subject_entity": row[0],
"relationship": row[1],
"object_entities": row[2].split(),
},
)
yield result
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