glam
Datasets
All datasets matching “glam”glami-1m-t2i-mteb
GLAMI-1M text-to-image retrieval
This MTEB-formatted derivative uses the complete 116,004-row official GLAMI-1M test split. Product names and descriptions are text queries and product images are the corpus. Repeated image IDs and exact repeated texts are deduplicated within each language, and qrels retain every observed text-image association.
The unchanged source archives are already hosted by the original authors in glami/glami-1m. GLAMI-1M-dataset--test-only.zip is pinned at… See the full description on the dataset page: https://huggingface.co/datasets/artist/glami-1m-t2i-mteb.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… See the full description on the dataset page: https://huggingface.co/datasets/zidcenek/GLAMI-Entity-Matching-Dataset.glami-1m
GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem.
Each sample contains an image, country code, name in corresponding language, description, target category and source of the label which can be of multiple types… See the full description on the dataset page: https://huggingface.co/datasets/glami/glami-1m.GLAMI-1M
This is fork of original dataset converted to dataset format.
GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem.
Each sample contains an image, country code, name in corresponding language… See the full description on the dataset page: https://huggingface.co/datasets/pySilver/GLAMI-1M.glami-1m-mteb
GLAMI-1M MTEB multimodal classification
This is an MTEB-ready derivative of the official
glami/glami-1m
release for multilingual image+text fashion classification. The source is
pinned at revision befda45d8d4e8b8082bb8a1912d1f9eb9483991c and remains
licensed under Apache-2.0.
Each example contains the official product image, name and description
joined as text, and the official category ID as label. The complete
116,004-row human-labeled test split is unchanged.
To keep… See the full description on the dataset page: https://huggingface.co/datasets/artist/glami-1m-mteb.glam-extraction-benchmark
GLAM extraction benchmark
Structured extraction from cultural-heritage documents. The first configuration is
nls-index-cards: 98 manuscript catalogue cards from the National Library of Scotland.
Source and credits
Derived from NationalLibraryOfScotland/index-cards-eval,
revision 2a81070549d8493c2c538744a9dbbc1dc72cb146 (CC0). Images and checked outputs are preserved.
NLS cataloguers reviewed the model-drafted labels: 66 accepted as drafted, 32 corrected.
Drafting… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/glam-extraction-benchmark.