Datasets:

jhu-clsp
/

bernice-pretrain-data

	# 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
	"""Bernice pretrain data"""


	import csv
	import json
	import os
	import gzip
	import datasets

	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, and Mark Dredze. 2022.
	Bernice: A Multilingual Pre-trained Encoder for Twitter. In Proceedings of the 2022 Conference on
	Empirical Methods in Natural Language Processing, pages 6191–6205, Abu Dhabi, United Arab Emirates.
	Association for Computational Linguistics.
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder.
	The tweets are from the public 1% Twitter API stream from January 2016 to December 2021.
	Twitter-provided language metadata is provided with the tweet ID. The data contains 66 unique languages,
	as identified by ISO 639 language codes, including `und` for undefined languages.
	Tweets need to be re-gathered via the Twitter API.
	"""

	_HOMEPAGE = "https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415"

	# TODO: Add the licence for the dataset here if you can find it
	_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)
	# If the data files live in the same folder or repository of the dataset script,
	# you can just pass the relative paths to the files instead of URLs.
	# Only train data, validation split not provided
	_BASE_DATA_URL = "data"
	# _URLS = {
	# "all": f"{_BASE_URL}",
	# "indic": ["data/indic_tweet_ids.txt.gz"]
	# }


	# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
	class BernicePretrainData(datasets.GeneratorBasedBuilder):
	"""Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder."""
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	# 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="all", version=VERSION,
	description="Includes all tweets"),
	datasets.BuilderConfig(name="indic", version=VERSION,
	description="Only the Indic languages, plus `undefined'"),
	]

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

	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
	# 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
	features=datasets.Features(
	{
	"tweet_id": datasets.Value("string"),
	"lang": datasets.Value("string"),
	"year": datasets.Value("string")
	}
	),
	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
	data_url = f"{self._BASE_DATA_URL}/{self.config.name}"
	print(f"{data_url=}")
	print(f"{os.listdir()}")
	urls_to_download = [f"{data_url}/{f}" for f in os.listdir(data_url)]
	downloaded_files = dl_manager.download_and_extract(urls_to_download)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepaths": downloaded_files,
	"split": "train",
	},
	)
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepaths, 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.
	for filepath in filepaths:
	with gzip.open(filepath, "rb") as f:
	for line_number, instance in enumerate(f):
	tweet_id, lang, year = instance.strip().split("\t")
	yield tweet_id, {
	"tweet_id": tweet_id,
	"lang": lang,
	"year": year
	}