ESCI-product-dataset.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
"""ESCI-product-dataset 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 ESCI-product-dataset recall"

_DATASET_URLS = {
    'train': "https://huggingface.co/datasets/spacemanidol/ESCI-product-dataset/resolve/main/train.jsonl.gz",
    'dev': "https://huggingface.co/datasets/spacemanidol/ESCI-product-dataset/resolve/main/dev.jsonl.gz",
    #'test': "https://huggingface.co/datasets/resolve/main/nq-test.jsonl.gz",
}


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

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(version=VERSION,
                               description="Wikipedia NQ train/dev/test datasets"),
    ]

    def _info(self):
        features = datasets.Features({
            'query_id': datasets.Value('string'),
            'query': datasets.Value('string'),
            'positive_passages': [
                {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
                 'title': datasets.Value('string'), 'bullet_points': datasets.Value('string'),
                 'color': datasets.Value('string'), 'locale': datasets.Value('string'),
                 'brand': datasets.Value('string'),'contents': datasets.Value('string')}
            ],
            'positive_passages_substitue': [
               {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
                 'title': datasets.Value('string'), 'bullet_points': datasets.Value('string'),
                 'color': datasets.Value('string'), 'locale': datasets.Value('string'),
                 'brand': datasets.Value('string'),'contents': datasets.Value('string')}

            ],
            'negative_passages': [
                {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
                 'title': datasets.Value('string'), 'bullet_points': datasets.Value('string'),
                 'color': datasets.Value('string'), 'locale': datasets.Value('string'),
                 'brand': datasets.Value('string'),'contents': datasets.Value('string')}
            ],
            'negative_passages_true': [
                {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
                 'title': datasets.Value('string'), 'bullet_points': datasets.Value('string'),
                 'color': datasets.Value('string'), 'locale': datasets.Value('string'),
                 'brand': datasets.Value('string'),'contents': datasets.Value('string')}
            ],
            'negative_passages_complement': [
                {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
                 'title': datasets.Value('string'), 'bullet_points': datasets.Value('string'),
                 'color': datasets.Value('string'), 'locale': datasets.Value('string'),
                 'brand': datasets.Value('string'),'contents': datasets.Value('string')}
            ],
            'query-inject-det': datasets.Value('string'),
            'query-stem': datasets.Value('string'),
            'query-synonym': datasets.Value('string'),
            'query-random-char-swap': datasets.Value('string'),
            'query-char-keyboard': datasets.Value('string'),
            'query-paraphrase': datasets.Value('string'),
            'query-reorder-words': datasets.Value('string'),
            'query-backtranslation': datasets.Value('string'),
            'query-char-delete': datasets.Value('string'),
            'query-lemmatize': 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):
        if self.config.data_files:
            downloaded_files = self.config.data_files
        else:
            downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
        splits = [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
                },
            ) for split in downloaded_files
        ]
        return splits

    def _generate_examples(self, files):
        """Yields examples."""
        for filepath in files:
            with open(filepath, encoding="utf-8") as f:
                for line in f:
                    data = json.loads(line)
                    if data.get('negative_passages') is None:
                        data['negative_passages'] = []
                    if data.get('positive_passages') is None:
                        data['positive_passages'] = []
                    if data.get('answers') is None:
                        data['answers'] = []
                    yield data['query_id'], data