EntityMatching.py

# 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
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"""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)