load_output_sample.py

# 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
"""Loading script for output_sample dataset, from the Historical American Buildings, Landscapes, and Engineering Records collection of the Library of Congress."""


import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
"""

_DESCRIPTION = """\
Sample dataset scraped from https://www.loc.gov/collections/historic-american-buildings-landscapes-and-engineering-records/?c=150&at!=content,pages&fo=json
The dataset contains images and metadata for historic buildings, landscapes, and engineering records. 
"""

_HOMEPAGE = "https://www.loc.gov/collections/historic-american-buildings-landscapes-and-engineering-records"

_LICENSE = "Creative Commons 1.0 Universal"


# 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",
}


class HABLER_LOC(datasets.GeneratorBasedBuilder):
    """Historical American Buildings, Landscapes, and Engineering Records dataset."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                    "call_number": datasets.Value("string"),
                    "control_number": datasets.Value("string"),
                    "created": datasets.Value("string"),
                    "created_published": datasets.Value("string"),
                    "created_published_date": datasets.Value("string"),
                    "creators": datasets.Sequence(feature={"link": datasets.Value("string"), "role": datasets.Value("string"), "title": datasets.Value("string") }),
			        "date": datasets.Value("string"),
			        "display_offsite": datasets.Bool(),
			        "id": datasets.Value("string"),
			        "link": datasets.Value("string"),
			        "medium_brief": datasets.Value("string"), 
			        "mediums": datasets.Sequence(datasets.Value("string")),
			        "modified": datasets.Value("string"),
			        "notes": datasets.Sequence(datasets.Value("string")),
			        "part_of": datasets.Value("string"),
			        "part_of_group": datasets.Value("string"),
			        "place": datasets.Sequence(features={
			        	"latitude": datasets.Value("string"),
			        	"link": datasets.Value("string"), 
			        	"longitude": datasets.Value("string"), 
			        	"title": datasets.Value("string")}),
			        "repository": datasets.Value("string"), 
			        "resource_links": datasets.Sequence(datasets.Value("string")),
			        "rights_advisory": datasets.Value("string"), 
			        "rights_information": datasets.Value("string"), 
			        "service_low": datasets.Value("string"), 
			        "service_medium": datasets.Value("string"), 
			        "source_created": datasets.Value("string"), 
			        "source_modified": datasets.Value("string"), 
			        "subject_headings": datasets.Sequence(datasets.Value("string")),
			        "thumb_gallery": datasets.Value("string"), 
			        "title": datasets.Value("string") 
                }
            )
        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": os.path.join(data_dir, "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, "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.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                if self.config.name == "first_domain":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "sentence": data["sentence"],
                        "option1": data["option1"],
                        "answer": "" if split == "test" else data["answer"],
                    }
                else:
                    yield key, {
                        "sentence": data["sentence"],
                        "option2": data["option2"],
                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                    }