# 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], }