# 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/data" _URLS = { "product_image_urls": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data/product_image_urls.parquet", "product_features": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data/product_features.parquet", "query_features": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/data/query_features.parquet", "supp_product_image_urls": "https://huggingface.co/datasets/crossingminds/shopping-queries-image-dataset/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. with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "product_image_urls": # Yields examples as (key, example) tuples yield key, { "product_id": data["product_id"], "image_url": data["image_url"] } elif self.config_name == "product_features": yield key, { "product_id": data["product_id"], "clip_text_features": data["clip_text_features"], "clip_image_features": data["clip_image_features"], } elif self.config_name == "query_features": yield key, { "query_id": data["query_id"], "clip_text_features": data["clip_text_features"], } elif self.config_name == "supp_product_image_urls": yield key, { "product_id": data["product_id"], "image_url": data["image_url"] } else: raise ValueError(f"Unknown config name: {self.config_name}")