Datasets:

crossingminds
/

shopping-queries-image-dataset

@@ -1,181 +0,0 @@
-# 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.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
-import csv
-import json
-import os
-import datasets
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{SIGIR-eCom 2024,
-title = {Shopping Queries Image Dataset (SQID): An Image-Enriched ESCI Dataset for Exploring Multimodal Learning in Product Search},
-author={Marie Al Ghossein, Ching-Wei Chen, Jason Tang},
-year={2024}
-}
-"""
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-The Shopping Queries Image Dataset (SQID) is an extension of the Amazon Shopping Queries Dataset which has been enriched with image information associated with 190,000 products.
-"""
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "MIT"
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_BASE_URL = "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/resolve/data/"
-_URLS = {
-    "product_image_urls": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/resolve/main/data/product_image_urls.parquet",
-    "product_features": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/resolve/main/data/product_features.parquet",
-    "query_features": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/resolve/main/data/query_features.parquet",
-    "supp_product_image_urls": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/resolve/main/data/supp_product_image_urls.parquet",
-}
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class ShoppingQueriesImageDataset(datasets.GeneratorBasedBuilder):
-    """Shopping Queries Image Dataset"""
-    VERSION = datasets.Version("1.0.0")
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="product_image_urls", version=VERSION, description="Image URLs for products"),
-        datasets.BuilderConfig(name="product_features", version=VERSION, description="CLIP embeddings for products"),
-        datasets.BuilderConfig(name="query_features", version=VERSION, description="CLIP embeddings for queries"),
-        datasets.BuilderConfig(name="supp_product_image_urls", version=VERSION, description="Image URLs for supplemental set of products"),
-    ]
-    DEFAULT_CONFIG_NAME = "product_image_urls"
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "product_image_urls":
-            features = datasets.Features(
-                {
-                    "product_id": datasets.Value("string"),
-                    "image_url": datasets.Value("string")
-                }
-            )
-        elif self.config.name == "product_features":
-            features = datasets.Features(
-                {
-                    "product_id": datasets.Value("string"),
-                    "clip_text_features": datasets.Sequence(datasets.Value("float32")),
-                    "clip_image_features": datasets.Sequence(datasets.Value("float32"))
-                }
-            )
-        elif self.config.name == "query_features":
-            features = datasets.Features(
-                {
-                    "query_id": datasets.Value("string"),
-                    "clip_text_features": datasets.Sequence(datasets.Value("float32"))
-                }
-            )
-        elif self.config.name == "product_features":
-            features = datasets.Features(
-                {
-                    "product_id": datasets.Value("string"),
-                    "image_url": datasets.Value("string")
-                }
-            )
-        else:
-            raise ValueError(f"Invalid configuration name: {self.config.name}")
-        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
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        data_path = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_path,
-                    "split": "train",
-                },
-            ),
-            #datasets.SplitGenerator(
-            #    name=datasets.Split.VALIDATION,
-            #    # These kwargs will be passed to _generate_examples
-            #    gen_kwargs={
-            #        "filepath": os.path.join(data_dir, "dev.jsonl"),
-            #        "split": "dev",
-            #    },
-            #),
-            #datasets.SplitGenerator(
-            #    name=datasets.Split.TEST,
-            #    # These kwargs will be passed to _generate_examples
-            #    gen_kwargs={
-            #        "filepath": os.path.join(data_dir, "test.jsonl"),
-            #        "split": "test"
-            #    },
-        ]
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        """Yields examples as (key, example) tuples."""
-        # Load parquet file
-        print(f"filepath: {filepath}")
-        dataset = datasets.Dataset.from_parquet(filepath)
-        # Yield example tuples
-        for key, record in enumerate(dataset):
-            yield key, record