Datasets:

CLUTRR
/

v1

	# -- coding: utf-8 --
	"""CLUTRR_Dataset Loading Script.ipynb

	Automatically generated by Colaboratory.

	Original file is located at
	https://colab.research.google.com/drive/1q9DdeHA5JbgTHkH6kfZe_KWHQOwHZA97
	"""

	# coding=utf-8
	# Copyright 2019 The CLUTRR Datasets Authors and the HuggingFace Datasets Authors.
	#
	# CLUTRR is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) licensed, as found in the LICENSE file.
	#
	# 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
	"""The CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning) benchmark."""


	import csv
	import os
	import textwrap

	import numpy as np

	import datasets


	_CLUTRR_CITATION = """\
	@article{sinha2019clutrr,
	Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton},
	Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text},
	Year = {2019},
	journal = {Empirical Methods of Natural Language Processing (EMNLP)},
	arxiv = {1908.06177}
	}
	"""

	_CLUTRR_DESCRIPTION = """\
	CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning),
	a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177)
	to test the systematic generalization and inductive reasoning capabilities of NLU systems.

	"""
	_URL = "https://github.com/kliang5/CLUTRR_huggingface_dataset/tree/main/"
	_TASK = ["gen_train23_test2to10", "gen_train234_test2to10", "rob_train_clean_23_test_all_23", "rob_train_disc_23_test_all_23", "rob_train_irr_23_test_all_23","rob_train_sup_23_test_all_23"]

	class CLUTRR_v1(datasets.GeneratorBasedBuilder):
	"""BuilderConfig for CLUTRR."""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=task,
	version=datasets.Version("1.0.0"),
	description="",
	)
	for task in _TASK
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_CLUTRR_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"story": datasets.Value("string"),
	"query": datasets.Value("string"),
	"target": datasets.Value("string"),
	"clean_story": datasets.Value("string"),
	"proof_state": datasets.Value("string"),
	"f_comb": datasets.Value("string"),
	"task_name": datasets.Value("string"),
	"story_edges": datasets.Value("string"),
	"edge_types": datasets.Value("string"),
	"query_edge": datasets.Value("string"),
	"genders": datasets.Value("string"),
	"task_split": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both premise
	# and hypothesis as input).
	supervised_keys=None,
	homepage="https://www.cs.mcgill.ca/~ksinha4/clutrr/",
	citation=_CLUTRR_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs

	task = str(self.config.name)
	urls_to_download = {
	"test": _URL + task + "/test.csv",
	"train": _URL + task + "/train.csv",
	"validation": _URL + task + "/validation.csv",
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(downloaded_files["train"], task + "_train.jsonl"),
	"task": task,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(downloaded_files["validation"], task + "_val.jsonl"),
	"task": task,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(downloaded_files["test"], task + "_test.jsonl"),
	"task": task,
	},
	),
	]

	def _generate_examples(self, filepath, task):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	i = 0
	for line in f:
	data = json.loads(line)
	i += 1
	yield i, {
	"id": data["id"],
	"story": data["story"],
	"query": data["query"],
	"target": data["target"],
	"clean_story": data["clean_story"],
	"proof_state": data["proof_state"],
	"f_comb": data["f_comb"],
	"task_name": data["task_name"],
	"story_edges": data["story_edges"],
	"edge_types": data["edge_types"],
	"query_edge": data["query_edge"],
	"genders": data["genders"],
	"task_split": data["task_split"],
	}