Datasets:
Create ontolama.py
Browse files- ontolama.py +189 -0
ontolama.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""OntoLAMA Dataset Loading Script"""
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import csv
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import json
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import os
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import datasets
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@article{he2023language,
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title={Language Model Analysis for Ontology Subsumption Inference},
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author={He, Yuan and Chen, Jiaoyan and Jim{\'e}nez-Ruiz, Ernesto and Dong, Hang and Horrocks, Ian},
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journal={arXiv preprint arXiv:2302.06761},
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year={2023}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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OntoLAMA: LAnguage Model Analysis datasets for Ontology Subsumption Inference.
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"""
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_URL = "https://huggingface.co/datasets/krr-oxford/OntoLAMA/resolve/main/data/"
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = "https://krr-oxford.github.io/DeepOnto/"
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = "Apache License, Version 2.0"
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class OntoLAMA(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset.
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"""
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VERSION = datasets.Version("1.0")
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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# If you need to make complex sub-parts in the datasets with configurable options
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# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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# BUILDER_CONFIG_CLASS = MyBuilderConfig
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# You will be able to load one or the other configurations in the following list with
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="schemaorg-atomic-SI",
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version=VERSION,
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description="Atomic SI dataset created from the Schema.org Ontology."
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),
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datasets.BuilderConfig(
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name="doid-atomic-SI",
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version=VERSION,
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description="Atomic SI dataset created from the Disease Ontology."
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),
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datasets.BuilderConfig(
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name="foodon-atomic-SI",
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version=VERSION,
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description="Atomic SI dataset created from the Food Ontology."
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),
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datasets.BuilderConfig(
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name="go-atomic-SI",
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version=VERSION,
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description="Atomic SI dataset created from Gene Ontology."
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),
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]
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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if "atomic-SI" in self.config.name: # This is the name of the configuration selected in BUILDER_CONFIGS above
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features = datasets.Features(
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{
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"v_sub_concept": datasets.Value("string"),
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"v_super_concept": datasets.Value("string"),
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'label': datasets.ClassLabel(num_classes=2, names=['negative_subsumption', 'positive_subsumption'], names_file=None, id=None),
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"axiom": datasets.Value("string"),
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# These are the features of your dataset like images, labels ...
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}
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)
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elif "complex-SI" in self.config.name: # This is an example to show how to have different features for "first_domain" and "second_domain"
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features = datasets.Features(
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{
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"v_sub_concept": datasets.Value("string"),
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"v_super_concept": datasets.Value("string"),
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'label': datasets.ClassLabel(num_classes=2, names=['negative_subsumption', 'positive_subsumption'], names_file=None, id=None),
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"axiom": datasets.Value("string"),
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"anchor_axiom": datasets.Value("string") # the equivalence axiom used as anchor
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# These are the features of your dataset like images, labels ...
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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# supervised_keys=("sentence", "label"),
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = os.path.join(_URL, self.config.name)
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data_dir = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, "train.jsonl"),
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, "dev.jsonl"),
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"split": "dev",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, "test.jsonl"),
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"split": "test"
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},
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),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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with open(filepath, encoding="utf-8") as f:
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for key, row in enumerate(f):
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data = json.loads(row)
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if "atomic-SI" in self.config.name:
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# Yields examples as (key, example) tuples
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yield key, {
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"v_sub_concept": data["v_sub_concept"],
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"v_super_concept": data["v_super_concept"],
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"label": data["label"],
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"axiom": data["axiom"],
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}
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elif "complex-SI" in self.config.name:
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yield key, {
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"v_sub_concept": data["v_sub_concept"],
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"v_super_concept": data["v_super_concept"],
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"label": data["label"],
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"axiom": data["axiom"],
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"anchor_axiom": data["anchor_axiom"],
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}
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