# 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 UnpredicTable-rated-medium dataset.""" import json import os import pandas as pd import datasets _CITATION = """\ @misc{chan2022few, author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan}, title = {Few-shot Adaptation Works with UnpredicTable Data}, publisher={arXiv}, year = {2022}, url = {https://arxiv.org/abs/2208.01009} } """ _DESCRIPTION = """\ The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. """ _HOMEPAGE = "https://ethanperez.net/unpredictable" _LICENSE = "Apache 2.0" _URL = "https://huggingface.co/datasets/MicPie/unpredictable_rated-medium/resolve/main/data/unpredictable_rated-medium.jsonl" logger = datasets.logging.get_logger(__name__) class UnpredicTable(datasets.GeneratorBasedBuilder): """ The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. """ VERSION = datasets.Version("1.0.0") 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( description=_DESCRIPTION, features=features, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" data_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir}, ), ] def _generate_examples(self, filepath): """Yields examples.""" with open(filepath, encoding="utf-8") as f: for i, row in enumerate(f): data = json.loads(row) key = f"{data['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"], }