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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Alloprof: a new French question-answer education dataset and its use in an information retrieval case study"""
import json
import datasets
_CITATION = """\
@misc{lef23,
doi = {10.48550/ARXIV.2302.07738},
url = {https://arxiv.org/abs/2302.07738},
author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
"""
_DESCRIPTION = """\
This is a re-edit from the Alloprof dataset (which can be found here : https://huggingface.co/datasets/antoinelb7/alloprof).
For more information about the data source and the features, please refer to the original dataset card made by the authors, along with their paper available here : https://arxiv.org/abs/2302.07738
This re-edition of the dataset has been made for easier usage in the MTEB benchmarking pipeline. (https://huggingface.co/spaces/mteb/leaderboard). It is a filtered version of the original dataset, in a more ready-to-use format.
"""
_SPLITS = ["documents", "queries"]
_HOMEPAGE = "https://huggingface.co/datasets/antoinelb7/alloprof"
_LICENSE = "Creative Commons Attribution Non Commercial Share Alike 4.0 International"
_URLS = {
split: f"https://huggingface.co/datasets/lyon-nlp/alloprof/resolve/main/{split}.json"\
for split in _SPLITS
}
class Alloprof(datasets.GeneratorBasedBuilder):
"""Alloprof: a new French question-answer education dataset and its use in an information retrieval case study"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="documents", version=VERSION, description="Corpus of documents from the Alloprof website"),
datasets.BuilderConfig(name="queries", version=VERSION, description="Corpus of queries from students"),
]
# Avoid setting default config so that an error is raised asking the user
# to specify the piece of the dataset wanted
DEFAULT_CONFIG_NAME = "documents"
def _info(self):
if self.config.name == "documents":
features = {
"uuid": datasets.Value("string"),
"title": datasets.Value("string"),
"topic": datasets.Value("string"),
"text": datasets.Value("string"),
}
elif self.config.name == "queries":
features = {
"id": datasets.Value("int32"),
"text": datasets.Value("string"),
"answer": datasets.Value("string"),
"relevant": datasets.Sequence(datasets.Value("string")),
"subject": datasets.Value("string"),
}
else:
raise ValueError(f"Please specify a valid config name : {_SPLITS}")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.name == "documents":
dl_path = dl_manager.download_and_extract(_URLS["documents"])
return [datasets.SplitGenerator(name="documents", gen_kwargs={"filepath": dl_path})]
elif self.config.name == "queries":
dl_paths = dl_manager.download_and_extract(_URLS["queries"])
return [datasets.SplitGenerator(name="queries", gen_kwargs={"filepath": dl_paths})]
else:
raise ValueError(f"Please specify a valid config name : {_SPLITS}")
def _generate_examples(self, filepath):
if self.config.name in ["documents", "queries"]:
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for key, row in enumerate(data):
if self.config.name == "documents":
features = {
"uuid": row["uuid"],
"title": row["title"],
"topic": row["topic"],
"text": row["text"],
}
elif self.config.name == "queries":
features = {
"id": row["id"],
"text": row["text"],
"answer": row["answer"],
"relevant": row["relevant"],
"subject": row["subject"],
}
else:
raise ValueError(f"Please specify a valid config name : {_SPLITS}")
yield key, features
else:
raise ValueError(f"Please specify a valid config name : {_SPLITS}")
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