bernice-pretrain-data / bernice-pretrain-data.py
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# 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")
# 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.
def _info(self):
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"{_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
}