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import csv
import json
import logging as logger
import os
import datasets
_CITATION = """\
@misc{welleck2019dialogue,
title={Dialogue Natural Language Inference},
author={Sean Welleck and Jason Weston and Arthur Szlam and Kyunghyun Cho},
year={2019},
eprint={1811.00671},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
Dialogue NLI is a dataset that addresses the issue of consistency in dialogue models.
"""
_URL = "dnli.zip"
_HOMEPAGE = "https://wellecks.github.io/dialogue_nli/"
_LICENSE = "MIT"
class Dialogue_NLI(datasets.GeneratorBasedBuilder):
"""DNLI"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="dialogue_nli", version=VERSION, description="dnli"),
]
DEFAULT_CONFIG_NAME = "dialogue_nli" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"label": datasets.Value("string"),
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
# "triple1": datasets.Value("string"),
# "triple2": datasets.Value("string"),
"dtype": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "dnli/dialogue_nli/dialogue_nli_train.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "dnli/dialogue_nli/dialogue_nli_dev.jsonl"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "dnli/dialogue_nli/dialogue_nli_test.jsonl"),
"split": "test"
},
),
]
def _generate_examples(self, filepath, split):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath) as f:
data = json.load(f)
# print(data)
for example in data:
label = example.get("label", "").strip()
id_ = example.get("id", "").strip()
hypothesis = example.get("sentence1", "").strip()
premise = example.get("sentence2", "").strip()
dtype = example.get("dtype", "").strip()
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield id_, {
"premise": premise,
"hypothesis": hypothesis,
"id": id_,
"label": label,
"dtype": dtype,
}
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