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# Copyright 2020 The HuggingFace Datasets Authors.
# Copyright 2023 Yuan He.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""OntoLAMA Dataset Loading Script"""
import csv
import json
import os
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{he2023language,
title={Language Model Analysis for Ontology Subsumption Inference},
author={He, Yuan and Chen, Jiaoyan and Jim{\'e}nez-Ruiz, Ernesto and Dong, Hang and Horrocks, Ian},
booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
year={2023}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
OntoLAMA: LAnguage Model Analysis datasets for Ontology Subsumption Inference.
"""
_URL = lambda name: f"https://zenodo.org/record/7700458/files/{name}.zip?download=1"
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://krr-oxford.github.io/DeepOnto/"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "Apache License, Version 2.0"
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class OntoLAMA(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.0.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="bimnli", version=VERSION, description="BiMNLI dataset created from the MNLI dataset."
),
datasets.BuilderConfig(
name="schemaorg-atomic-SI",
version=VERSION,
description="Atomic SI dataset created from the Schema.org Ontology.",
),
datasets.BuilderConfig(
name="doid-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Disease Ontology."
),
datasets.BuilderConfig(
name="foodon-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Food Ontology."
),
datasets.BuilderConfig(
name="foodon-complex-SI", version=VERSION, description="Complex SI dataset created from the Gene Ontology."
),
datasets.BuilderConfig(
name="go-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Gene Ontology."
),
datasets.BuilderConfig(
name="go-complex-SI", version=VERSION, description="Complex SI dataset created from the Gene Ontology."
),
]
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if "atomic-SI" in self.config.name: # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"v_sub_concept": datasets.Value("string"),
"v_super_concept": datasets.Value("string"),
"label": datasets.ClassLabel(
num_classes=2, names=["negative_subsumption", "positive_subsumption"], names_file=None, id=None
),
"axiom": datasets.Value("string"),
# These are the features of your dataset like images, labels ...
}
)
elif (
"complex-SI" in self.config.name
): # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
{
"v_sub_concept": datasets.Value("string"),
"v_super_concept": datasets.Value("string"),
"label": datasets.ClassLabel(
num_classes=2, names=["negative_subsumption", "positive_subsumption"], names_file=None, id=None
),
"axiom": datasets.Value("string"),
"anchor_axiom": datasets.Value("string") # the equivalence axiom used as anchor
# These are the features of your dataset like images, labels ...
}
)
elif self.config.name == "bimnli":
features = datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.ClassLabel(
num_classes=2, names=["contradiction", "entailment"], names_file=None, id=None
),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# 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
urls = _URL(self.config.name)
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.name, "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, self.config.name, "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, self.config.name, "test.jsonl"), "split": "test"},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
if "atomic-SI" in self.config.name:
# Yields examples as (key, example) tuples
yield key, {
"v_sub_concept": data["v_sub_concept"],
"v_super_concept": data["v_super_concept"],
"label": data["label"],
"axiom": data["axiom"],
}
elif "complex-SI" in self.config.name:
yield key, {
"v_sub_concept": data["v_sub_concept"],
"v_super_concept": data["v_super_concept"],
"label": data["label"],
"axiom": data["axiom"],
"anchor_axiom": data["anchor_axiom"],
}
elif self.config.name == "bimnli":
yield key, {
"premise": data["premise"],
"hypothesis": data["hypothesis"],
"label": data["label"],
}
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