# 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. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" 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 = """\ @article{ilievski2021cskg, title={CSKG: The CommonSense Knowledge Graph}, author={Ilievski, Filip and Szekely, Pedro and Zhang, Bin}, journal={Extended Semantic Web Conference (ESWC)}, year={2021} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation: ATOMIC, ConceptNet, FrameNet, Roget, Visual Genome, Wikidata (We use the Wikidata-CS subset), and WordNet. CSKG is represented as a hyper-relational graph, by using the KGTK data model and file specification. Its creation is entirely supported by KGTK operations. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://cskg.readthedocs.io/en/latest/" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) # _URLS = { # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", # } _URLS = { "cskg": "https://zenodo.org/record/4331372/files/cskg.tsv.gz", } # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class CSKG(datasets.GeneratorBasedBuilder): """a commonsense knowledge graph""" VERSION = datasets.Version("1.1.0") # 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="cskg", version=VERSION, description="The relationships defined by cskg"), # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), ] DEFAULT_CONFIG_NAME = "cskg" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset if self.config.name == "cskg": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "id": datasets.Value("string"), "node1": datasets.Value("string"), "relation": datasets.Value("string"), "node2": datasets.Value("string"), "node1;label": datasets.Value("string"), "node2;label": datasets.Value("string"), "relation;label": datasets.Value("string"), "relation;dimension": datasets.Value("string"), "source": datasets.Value("string"), "sentence": datasets.Value("string"), # These are the features of your dataset like images, labels ... } ) # else: # This is an example to show how to have different features for "first_domain" and "second_domain" # features = datasets.Features( # { # "sentence": datasets.Value("string"), # "option2": datasets.Value("string"), # "second_domain_answer": datasets.Value("string") # # These are the features of your dataset like images, labels ... # } # ) 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 = _URLS[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": data_dir, "split": "train", }, ), # datasets.SplitGenerator( # name=datasets.Split.VALIDATION, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": os.path.join(data_dir, "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, "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. # jump the first row with open(filepath, 'rb') as f: for id_, row in enumerate(f): if id_ == 0: continue if self.config.name == "cskg": row = row.split(b"\t") # Yields examples as (key, example) tuples yield id_, { # "sentence": data["sentence"], # "option1": data["option1"], # "answer": "" if split == "test" else data["answer"], "id": row[0].decode("utf-8"), "node1": row[1].decode("utf-8"), "relation": row[2].decode("utf-8"), "node2": row[3].decode("utf-8"), "node1;label": row[4].decode("utf-8"), "node2;label": row[5].decode("utf-8"), "relation;label": row[6].decode("utf-8"), "relation;dimension": row[7].decode("utf-8"), "source": row[8].decode("utf-8"), "sentence": row[9].decode("utf-8"), } # else: # yield key, { # "sentence": data["sentence"], # "option2": data["option2"], # "second_domain_answer": "" if split == "test" else data["second_domain_answer"], # }