load_dataset.py

# coding=utf-8
# 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.
"""
    Exercise 2: Part 2 - Extractive QA 30 points ,
    storing a dataset with HuggingFace
"""

import csv
import json
import os

import datasets

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{air21_grp13:tokenized_results,
title = {Adv. Information Retrieval - Exercise , Part 2: Storing Results with HuggingFace},
author={Alexander Genser, Lena Jiricka, Samuel Keller
},
year={2021}
}
"""

# You can copy an official description
_DESCRIPTION = """\
This new dataset are results from extractive QA, using our top-1 re-ranking results from our implementation of part 1
and the fira gold label 
"""

_HOMEPAGE = "https://github.com/tuwien-information-retrieval/air-2021-group_13"

_LICENSE = "MIT"

# The HuggingFace dataset library don't host the datasets but only point 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/datasets/huggingFaceUser02/air21_grp13_tokenized_results.zip",
    'second_domain': "https://huggingface.co/datasets/huggingFaceUser02/air21_grp13_tokenized_results.zip",
}


class AIR21Grp13TokenizedResults(datasets.GeneratorBasedBuilder):
    """
results from the top-1 re-ranking results from implementation of part 1 of Exercise 2, AIR 21.
These are compared with the FiRA gold label results, using the BERT Transformer
'bert-large-uncased-whole-word-masking-finetuned-squad'
    """

    VERSION = datasets.Version("0.1.0")

    # 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="inference_results", version=VERSION,
                               description="This part of my dataset covers the results of the inference")
    ]

    DEFAULT_CONFIG_NAME = "inference_results"  # 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
        features = datasets.Features(
                {
                    "doc_id": datasets.Value("long"),
                    "query_id": datasets.Value("long"),
                    "relevance": datasets.Value("float"),
                    "query_sentence": 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,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # 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):
        """Returns SplitGenerators."""
        # 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
        my_urls = _URLs[self.config.name]
        data_dir = dl_manager.download_and_extract(my_urls)
        return [
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    # These kwargs will be passed to _generate_examples
                    gen_kwargs={
                        "filepath": os.path.join(data_dir, "top1_bert_large_uncased_answers_01.tsv"),
                        "split": "dev",
                    },
                ),
            ]

    def _generate_examples(
            self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        with open(filepath, encoding="utf-8") as f:
            # logger.info("Reading instances from lines in file at: %s", file_path)
            for line_num, line in enumerate(f):
                line = line.strip("\n")

                if not line:
                    continue

                line_parts = line.split('\t')

                query_id, doc_id, relevance, answer = line_parts

                yield {
                    "query_id": query_id,
                    "doc_id": doc_id,
                    "relevance": relevance,
                    "answer": answer
                }