bernice-pretrain-data.py

# 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 = "https://huggingface.co/datasets/jhu-clsp/bernice-pretrain-data/resolve/main/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
                    }