Delete loading script

EntityMatching.py

@@ -1,101 +0,0 @@
-# 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)