Datasets:

miracl
/

nomiracl

	# coding=utf-8
	# Copyright 2022 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
	"""NoMIRACL: A dataset to evaluation LLM robustness across 18 languages."""

	import os
	import json
	import csv
	import datasets

	from collections import defaultdict


	_CITATION = """\
	@article{thakur2023nomiracl,
	title={NoMIRACL: Knowing When You Don't Know for Robust Multilingual Retrieval-Augmented Generation},
	author={Nandan Thakur and Luiz Bonifacio and Xinyu Zhang and Odunayo Ogundepo and Ehsan Kamalloo and David Alfonso-Hermelo and Xiaoguang Li and Qun Liu and Boxing Chen and Mehdi Rezagholizadeh and Jimmy Lin},
	journal={ArXiv},
	year={2023},
	volume={abs/2312.11361}
	}
	"""

	_DESCRIPTION = """\
	Data Loader for the NoMIRACL dataset.
	"""

	_URL = "https://github.com/project-miracl/nomiracl"

	_DL_URL_FORMAT = "data/{name}"


	def load_topics(filepath: str):
	"""
	Loads queries from a file and stores them in a dictionary.
	"""
	queries = {}
	with open(filepath, 'r', encoding='utf-8') as f:
	reader = csv.reader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
	for row in reader:
	queries[row[0]] = row[1]
	return queries

	def load_corpus(filepath: str):
	"""
	Loads the corpus file as a dictionary.
	"""
	corpus = {}
	with open(filepath, encoding='utf8') as fIn:
	for line in fIn:
	line = json.loads(line)
	corpus[line.get("docid")] = {
	"text": line.get("text", "").strip(),
	"title": line.get("title", "").strip(),
	}
	return corpus


	def load_qrels(filepath: str):
	if filepath is None:
	return None

	qrels = defaultdict(dict)
	with open(filepath, encoding="utf-8") as f:
	for line in f:
	qid, _, docid, rel = line.strip().split('\t')
	qrels[qid][docid] = int(rel)
	return qrels


	class NoMIRACLConfig(datasets.BuilderConfig):
	"""BuilderConfig for NoMIRACL."""

	def __init__(self, name, **kwargs):
	"""
	Args:
	name: `string`, name of dataset config (=language)
	**kwargs: keyword arguments forwarded to super.
	"""
	super(NoMIRACLConfig, self).__init__(
	version=datasets.Version("1.0.0", ""), name=name.lower(), **kwargs
	)
	# relative path to full data inside a repo (for example `data/german`)
	self.data_root_url = _DL_URL_FORMAT.format(name=name)


	class NoMIRACL(datasets.GeneratorBasedBuilder):
	"""Multilingual NoMIRACL dataset."""

	BUILDER_CONFIGS = [
	NoMIRACLConfig(name="arabic", description="Arabic NoMIRACL dataset"),
	NoMIRACLConfig(name="chinese", description="Chinese NoMIRACL dataset"),
	NoMIRACLConfig(name="finnish", description="Finnish NoMIRACL dataset"),
	NoMIRACLConfig(name="german", description="German NoMIRACL dataset"),
	NoMIRACLConfig(name="indonesian", description="Indonesian NoMIRACL dataset"),
	NoMIRACLConfig(name="korean", description="Korean NoMIRACL dataset"),
	NoMIRACLConfig(name="russian", description="Russian NoMIRACL dataset"),
	NoMIRACLConfig(name="swahili", description="Swahili NoMIRACL dataset"),
	NoMIRACLConfig(name="thai", description="Thai NoMIRACL dataset"),
	NoMIRACLConfig(name="bengali", description="Bengali NoMIRACL dataset"),
	NoMIRACLConfig(name="english", description="English NoMIRACL dataset"),
	NoMIRACLConfig(name="french", description="French NoMIRACL dataset"),
	NoMIRACLConfig(name="hindi", description="Hindi NoMIRACL dataset"),
	NoMIRACLConfig(name="japanese", description="Japanese NoMIRACL dataset"),
	NoMIRACLConfig(name="persian", description="Persian NoMIRACL dataset"),
	NoMIRACLConfig(name="spanish", description="Spanish NoMIRACL dataset"),
	NoMIRACLConfig(name="telugu", description="Telugu NoMIRACL dataset"),
	NoMIRACLConfig(name="yoruba", description="Yoruba NoMIRACL dataset"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({
	'query_id': datasets.Value('string'),
	'query': datasets.Value('string'),
	'positive_passages': [{
	'docid': datasets.Value('string'),
	'text': datasets.Value('string'),
	'title': datasets.Value('string')
	}],
	'negative_passages': [{
	'docid': datasets.Value('string'),
	'text': datasets.Value('string'),
	'title': datasets.Value('string'),
	}],
	}),
	supervised_keys=("file", "text"),
	homepage=_URL,
	citation=_CITATION,
	task_templates=None,
	)

	def _split_generators(self, dl_manager):

	# Download downloaded_files
	downloaded_files = dl_manager.download_and_extract({
	"corpus": self.config.data_root_url + "/corpus.jsonl.gz",
	"dev": {"qrels": {"relevant": self.config.data_root_url + "/qrels/dev.relevant.tsv",
	"non_relevant": self.config.data_root_url + "/qrels/dev.non_relevant.tsv"},
	"topics": {"relevant": self.config.data_root_url + "/topics/dev.relevant.tsv",
	"non_relevant": self.config.data_root_url + "/topics/dev.non_relevant.tsv"}},
	"test": {"qrels": {"relevant": self.config.data_root_url + "/qrels/test.relevant.tsv",
	"non_relevant": self.config.data_root_url + "/qrels/test.non_relevant.tsv"},
	"topics": {"relevant": self.config.data_root_url + "/topics/test.relevant.tsv",
	"non_relevant": self.config.data_root_url + "/topics/test.non_relevant.tsv"}},
	})

	splits = [
	datasets.SplitGenerator(
	name="dev.relevant",
	gen_kwargs={
	"corpus_path": downloaded_files["corpus"],
	"qrels_path": downloaded_files["dev"]["qrels"]["relevant"],
	"topics_path": downloaded_files["dev"]["topics"]["relevant"],
	}
	),
	datasets.SplitGenerator(
	name="dev.non_relevant",
	gen_kwargs={
	"corpus_path": downloaded_files["corpus"],
	"qrels_path": downloaded_files["dev"]["qrels"]["non_relevant"],
	"topics_path": downloaded_files["dev"]["topics"]["non_relevant"],
	},
	),
	datasets.SplitGenerator(
	name="test.relevant",
	gen_kwargs={
	"corpus_path": downloaded_files["corpus"],
	"qrels_path": downloaded_files["test"]["qrels"]["relevant"],
	"topics_path": downloaded_files["test"]["topics"]["relevant"],
	}
	),
	datasets.SplitGenerator(
	name="test.non_relevant",
	gen_kwargs={
	"corpus_path": downloaded_files["corpus"],
	"qrels_path": downloaded_files["test"]["qrels"]["non_relevant"],
	"topics_path": downloaded_files["test"]["topics"]["non_relevant"],
	},
	),
	]

	return splits

	def _generate_examples(self, corpus_path, qrels_path, topics_path):

	corpus = load_corpus(corpus_path)
	qrels = load_qrels(qrels_path)
	topics = load_topics(topics_path)

	for qid in topics:
	data = {}
	data['query_id'] = qid
	data['query'] = topics[qid]

	pos_docids = [docid for docid, rel in qrels[qid].items() if rel == 1] if qrels is not None else []
	neg_docids = [docid for docid, rel in qrels[qid].items() if rel == 0] if qrels is not None else []
	data['positive_passages'] = [{
	'docid': docid,
	**corpus[docid]
	} for docid in pos_docids if docid in corpus]
	data['negative_passages'] = [{
	'docid': docid,
	**corpus[docid]
	} for docid in neg_docids if docid in corpus]
	yield qid, data