Delete loading script

OntoLAMA.py

@@ -1,213 +0,0 @@
-# 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 Jimenez-Ruiz, Ernesto and Dong, Hang and Horrocks, Ian},
-  booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
-  pages={3439--3453},
-  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"],
-                    }