ESCI-product-dataset-corpus-jp.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
# limitations under the License.

# Lint as: python3
"""Improving Product Search dataset ."""

import json

import datasets

_CITATION = """
@misc{reddy2022shopping,
      title={Shopping Queries Dataset: A Large-Scale {ESCI} Benchmark for Improving Product Search},
      author={Chandan K. Reddy and Lluís Màrquez and Fran Valero and Nikhil Rao and Hugo Zaragoza and Sambaran Bandyopadhyay 
and Arnab Biswas and Anlu Xing and Karthik Subbian},
      year={2022},
      eprint={2206.06588},
      archivePrefix={arXiv}
}
"""

_DESCRIPTION = "dataset load script for Improve Product Search Dataset"

_DATASET_URLS = {
    'train': 
"https://huggingface.co/datasets/spacemanidol/ESCI-product-dataset-corpus-jp/resolve/main/collection.jsonl.gz"
}


class ProductSearchCorpus(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("0.0.1")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(version=VERSION,
                               description="Product Search Dataset 100-word splits"),
    ]
    def _info(self):
        features = datasets.Features(
            {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
             'title': datasets.Value('string'), 'bullet_points': datasets.Value('string'),
             'brand': datasets.Value('string'), 'color': datasets.Value('string'),
             'locale': datasets.Value('string'), 'contents': 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
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage="",
            # License for the dataset if available
            license="",
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
        splits = [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "filepath": downloaded_files["train"],
                },
            ),
        ]
        return splits

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            for line in f:
                data = json.loads(line)
                if data.get('locale') is None:
                    data['locale'] = "jp"
                if data.get('title') is None:
                    data['title'] = ''
                if data.get('text') is None:
                    data['text'] = ''
                if data.get('brand') is None:
                    data['brand'] = ''
                if data.get('color') is None:
                    data['color'] = ''
                if data.get('contents') is None:
                    data['contents'] = ''
                if data.get('bullet_points') is None:
                    data['bullet_points'] = ''   
                yield data['docid'], data