fewshot_pretraining_loading_script.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.
"""This loads the fewshot-pretraining dataset."""

import json
import os
import pandas as pd

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# You can copy an official description
_DESCRIPTION = """\
The Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora "contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the Common Crawl, the largest and most up-to-date Web corpus that is currently available to the public."
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

_LICENSE = "Apache 2.0"

# 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)
_URLS = {
    "data_1": "https://huggingface.co/datasets/JeremyAlain/fewshot-ptretraining/data/1",
    "data_2": "https://huggingface.co/datasets/JeremyAlain/fewshot-ptretraining/data/2",
    "data_3": "https://huggingface.co/datasets/JeremyAlain/fewshot-ptretraining/data/3",

}


class FewshotPretraining(datasets.GeneratorBasedBuilder):
    """The Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora "contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the Common Crawl, the largest and most up-to-date Web corpus that is currently available to the public."
"""

    VERSION = datasets.Version("1.1.0")

    # 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.

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', '1')
    # data = datasets.load_dataset('my_dataset', '2')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="data_1", version=VERSION, description="This part of my dataset covers data_1"),
        datasets.BuilderConfig(name="data_2", version=VERSION, description="This part of my dataset covers data_2"),
        datasets.BuilderConfig(name="data_3", version=VERSION, description="This part of my dataset covers data_3"),
    ]

    DEFAULT_CONFIG_NAME = "data_1"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        features = datasets.Features(
            {

                "task": datasets.Value("string"),
                "input": datasets.Value("string"),
                "output": datasets.Value("string"),
                "options": datasets.Sequence([datasets.Value("string")]),
                "pageTitle": datasets.Value("string"),
                "outputColName": datasets.Value("string"),
                "url": datasets.Value("string"),
                "wdcFile": 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, 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
            # TODO ACTIVATE IF WE HAVE HOMEPAGE 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
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "folder_path": data_dir,
                    "split": "train",
                },
            )


    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, folder_path, split):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        for filepath in os.listdir(folder_path):
            with open(filepath, encoding="utf-8") as f:
                data = pd.read_json(filepath, orient="records", lines=True)
                for i in range(data.shape[0]):
                    row = data.iloc[i]
                    # Yields examples as (key, example) tuples
                    key = row["task"] + "_i"
                    yield key, {
                        "task": data["task"],
                        "input": data["input"],
                        "output": data["output"],
                        "options": data["options"],
                        "pageTitle": data["pageTitle"],
                        "outputColName": data["outputColName"],
                        "url": data["url"],
                        "wdcFile": data["wdcFile"],
                    }