zidcenek/GLAMI-Entity-Matching-Dataset

GLAMI Duplication Detection

Product-duplicate detection over GLAMI e-commerce listings: ~1.3M product images plus multilingual titles, descriptions and attributes, with labelled groups of items that do or do not refer to the same physical product.

Released under the Apache License 2.0 — see LICENSE.

TODO: describe how the labels were produced.

Structure

Config	Files	Contents
images	images/shard-*.parquet	itemId → image bytes, one row per product image
items	items_train.csv, items_test.csv	itemId, price, colorTagIdsString, departmentIds, brandEditionTagId, title, description, geo, and label on train
splits	train_split.csv, val_split.csv	train/validation partition of the training items
groups	groups_*.csv	item1..itemN, label — candidate groups of 5–10 items, plus hard_ and category_ adversarial variants

Images are keyed by itemId alone and are not pre-split. Every other file references itemId, so all of them join against the same image table and no picture is stored twice.

Usage

from datasets import load_dataset

images = load_dataset("zidcenek/GLAMI-Entity-Matching-Dataset", "images", split="train")
items  = load_dataset("zidcenek/GLAMI-Entity-Matching-Dataset", "items",  split="train")
groups = load_dataset("zidcenek/GLAMI-Entity-Matching-Dataset", "groups", split="g5")

images[0]["image"]    # PIL.Image, decoded lazily
images[0]["itemId"]

The image config is ~TODO GB, so stream it if you do not want a full local copy:

images = load_dataset(
    "zidcenek/GLAMI-Entity-Matching-Dataset", "images", split="train", streaming=True
)

To attach images to items, build the itemId → row-index map once and index into it:

index = {item_id: i for i, item_id in enumerate(images["itemId"])}
row = images[index[items[0]["itemId"]]]

Reproducing the image shards

pip install -U datasets Pillow "huggingface_hub[hf_xet]"

python scripts/build_image_shards.py --inspect    # check the filename -> itemId mapping
python scripts/build_image_shards.py              # glami_images.tar.gz -> images/shard-*.parquet
hf auth login
python scripts/upload_to_hub.py                   # push to the Hub over HTTP

Both scripts are resumable: rerun the same command after an interruption. See the header of each for the full options.

Citation

TODO