Datasets:

JeremyAlain
/

123_test

	# 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_0": ["https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/files_0.jsonl"],
	"data_1": ["https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/files_1.jsonl"],
	"data_2": ["https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/files_2.jsonl"],

	}
	logger = datasets.logging.get_logger(__name__)


	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_0", version=VERSION, description="This part of my dataset covers data_0"),
	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"),
	]

	DEFAULT_CONFIG_NAME = "data_0" # 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]

	local_extracted_path = dl_manager.download_and_extract(urls)[0]
	all_file_names_for_dataset_pd = pd.read_json(local_extracted_path, lines=True, orient="records")
	all_file_names_for_dataset = all_file_names_for_dataset_pd.values.tolist()
	all_file_names_for_dataset = [file_name[0] for file_name in all_file_names_for_dataset]

	all_local_extracted_paths = dl_manager.download_and_extract(all_file_names_for_dataset)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"file_paths": all_local_extracted_paths,
	},
	)
	]


	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, file_paths):
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	for file_idx, file_path in enumerate(file_paths):
	data = pd.read_json(file_path, orient="records", lines=True)
	for i in range(data.shape[0]):
	row = data.iloc[i]
	# Yields examples as (key, example) tuples
	key = str(row["task"]) + "{}_{}".format(file_idx, 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"],
	}