stackexchange_math_jsonl.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the Flax Sentence Embeddings team.
#
# 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.
"""The dataset is a collection of Question and Answer automatically extracted from Stack Exchange community network."""


import csv
import json
import os

import datasets


_CITATION = """\
@misc{StackExchangeDataset,
  author = {Flax Sentence Embeddings Team},
  title = {Stack Exchange question pairs},
  year = {2021},
  howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://huggingface.co/datasets/flax-sentence-embeddings/"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "https://archive.org/details/stackexchange"

# TODO: Add link to the official dataset URLs here
# 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 = {
    'titlebody_upvoted_downvoted_answer': "titlebody_upvoted_downvoted_answer.jsonl.gz",
    'title_answer': "title_answer.jsonl.gz",
    'titlebody_answer': "titlebody_answer.jsonl.gz",
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class StackExchangeMatch(datasets.GeneratorBasedBuilder):
    """The dataset is a collection of Question and Answer automatically extracted from match Stack Exchange community."""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="titlebody_upvoted_downvoted_answer", version=VERSION, 
                               description="Includes title and body from the question as well as most upvoted and downvoted answer."),
        datasets.BuilderConfig(name="title_answer", version=VERSION, 
                               description="Includes title from the question as well as most upvoted answer."),
        datasets.BuilderConfig(name="titlebody_answer", version=VERSION, 
                               description="Includes title and body from the question as well as most upvoted answer.")
    ]

    DEFAULT_CONFIG_NAME = "title_answer"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        if self.config.name == "titlebody_upvoted_downvoted_answer":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "title_body": datasets.Value("string"),
                    "upvoted_answer": datasets.Value("string"),
                    "downvoted_answer": datasets.Value("string")
                }
            )
        elif self.config.name == "titlebody_answer":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "title_body": datasets.Value("string"),
                    "upvoted_answer": datasets.Value("string"),
                }
            )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            features = datasets.Features(
                {
                    "title": datasets.Value("string"),
                    "upvoted_answer": 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_file = dl_manager.download_and_extract(my_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_file,
                },
            )
        ]

    def _generate_examples(
        self, filepath  # 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:
            for id_, row in enumerate(f):
                data = json.loads(row)
                if self.config.name == "titlebody_upvoted_downvoted_answer":
                    yield id_, {
                        "title_body": data[0],
                        "upvoted_answer": data[1],
                        "downvoted_answer": data[2],
                    }
                elif self.config.name == "titlebody_answer":
                    yield id_, {
                        "title_body": data[0],
                        "upvoted_answer": data[1],
                    }
                else:
                    yield id_, {
                        "title": data[0],
                        "upvoted_answer": data[1],
                    }