# 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/blob/main/data/0.zip", "data_1": "https://huggingface.co/datasets/JeremyAlain/123_test/blob/main/data/1.zip", "data_2": "https://huggingface.co/datasets/JeremyAlain/123_test/blob/main/data/2.zip", } 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] 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"], }