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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.
"""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"],
}
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