# coding=utf-8 # 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. """Covid Dialog dataset in English and Chinese""" import copy import os import re import textwrap import json import datasets # BibTeX citation _CITATION = """ @inproceedings{mudgal2018deep, title={Deep learning for entity matching: A design space exploration}, author={Mudgal, Sidharth and Li, Han and Rekatsinas, Theodoros and Doan, AnHai and Park, Youngchoon and Krishnan, Ganesh and Deep, Rohit and Arcaute, Esteban and Raghavendra, Vijay}, booktitle={Proceedings of the 2018 International Conference on Management of Data}, pages={19--34}, year={2018} } """ # Official description of the dataset _DESCRIPTION = textwrap.dedent( """ """ ) # Link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/anhaidgroup/deepmatcher/blob/master/Datasets.md" _LICENSE = "" import datasets import os import json names = ["Beer", "iTunes_Amazon", "Fodors_Zagats", "DBLP_ACM", "DBLP_GoogleScholar", "Amazon_Google", "Walmart_Amazon", "Abt_Buy", "Company", "Dirty_iTunes_Amazon", "Dirty_DBLP_ACM", "Dirty_DBLP_GoogleScholar", "Dirty_Walmart_Amazon"] class EntityMatching(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description=_DESCRIPTION) for name in names] def _info(self): features = datasets.Features( { "productA": datasets.Value("string"), "productB": datasets.Value("string"), "same": datasets.Value("bool_"), } ) return datasets.DatasetInfo( description=f"EntityMatching dataset, as preprocessed and shuffled in HELM", features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): test = dl_manager.download(os.path.join(self.config.name, "test.jsonl")) train = dl_manager.download(os.path.join(self.config.name, "train.jsonl")) val = dl_manager.download(os.path.join(self.config.name, "valid.jsonl")) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"file": train}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"file": val}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"file": test}, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, file): with open(file, encoding="utf-8") as f: for ix, line in enumerate(f): yield ix, json.loads(line)