--- configs: - config_name: corpus data_files: - split: computer_science path: corpus/computer_science-* - split: energy path: corpus/energy-* - split: finance_en path: corpus/finance_en-* - split: finance_fr path: corpus/finance_fr-* - split: hr path: corpus/hr-* - split: industrial path: corpus/industrial-* - split: pharmaceuticals path: corpus/pharmaceuticals-* - split: physics path: corpus/physics-* - config_name: queries data_files: - split: computer_science path: queries/computer_science-* - split: energy path: queries/energy-* - split: finance_en path: queries/finance_en-* - split: finance_fr path: queries/finance_fr-* - split: hr path: queries/hr-* - split: industrial path: queries/industrial-* - split: pharmaceuticals path: queries/pharmaceuticals-* - split: physics path: queries/physics-* - config_name: qrels data_files: - split: computer_science path: qrels/computer_science-* - split: energy path: qrels/energy-* - split: finance_en path: qrels/finance_en-* - split: finance_fr path: qrels/finance_fr-* - split: hr path: qrels/hr-* - split: industrial path: qrels/industrial-* - split: pharmaceuticals path: qrels/pharmaceuticals-* - split: physics path: qrels/physics-* license: other license_name: vidore-v3-mixed license_link: https://huggingface.co/collections/vidore/vidore-benchmark-v3 task_categories: - visual-document-retrieval - text-retrieval language: - en - fr - de - es - it - pt tags: - nano - nanobeir-format - vidore - wip pretty_name: NanoViDoRe v3 (WIP, NanoBEIR-format conversion) --- # NanoViDoRe v3 — WIP NanoBEIR-format conversion > [!WARNING] > **This is a temporary, work-in-progress dataset for experimentation.** > The format is **not final** and may change without notice. This repository will be **moved, renamed, or deleted** in the future > (for example, once a canonical location is agreed on). Please do not depend on it for anything long-lived. > For the original dataset, use [`lightonai/NanoViDoRe_v3`](https://huggingface.co/datasets/lightonai/NanoViDoRe_v3). ## What this is A re-packaging of [`lightonai/NanoViDoRe_v3`](https://huggingface.co/datasets/lightonai/NanoViDoRe_v3) (revision `0ae74913373848e65871c072831ef30f710642ce`) into the same layout as the NanoBEIR datasets (e.g. [`sentence-transformers/NanoBEIR-en`](https://huggingface.co/datasets/sentence-transformers/NanoBEIR-en)): **one split per task**, each with its own corpus, queries and qrels. It was created to explore whether a generic Nano-set evaluator in Sentence Transformers (discussion: [huggingface/sentence-transformers#4327](https://github.com/huggingface/sentence-transformers/issues/4327)) can evaluate visual document retrieval without any dataset-specific loading code. No content was added, removed or edited. Only the layout and column names changed: - The original shares one 4,000-page corpus across all 8 tasks and separates tasks with a `source_dataset` column. Every original qrel points to a page of its own `source_dataset` (0 of 2,061 cross-task qrels), so each page was assigned to the split of its `source_dataset`. The total number of pages is unchanged (4,000). - Columns were renamed to the NanoBEIR convention. IDs are strings. ## Structure Configs: `corpus`, `queries`, `qrels`. Splits (identical across configs): `computer_science`, `energy`, `finance_en`, `finance_fr`, `hr`, `industrial`, `pharmaceuticals`, `physics`. | config | columns | | --- | --- | | `corpus` | `_id` (str), `text` (str, the OCR `markdown` of the page), `image` (page image) | | `queries` | `_id` (str), `text` (str), `language` (str), `query_format` (str) | | `qrels` | `query-id` (str), `corpus-id` (str), `score` (int, graded: 1 = critically relevant, 2 = fully relevant) | | split | corpus | queries | qrels | | --- | ---: | ---: | ---: | | computer_science | 358 | 50 | 227 | | energy | 434 | 50 | 200 | | finance_en | 548 | 50 | 255 | | finance_fr | 479 | 50 | 221 | | hr | 365 | 50 | 283 | | industrial | 894 | 50 | 320 | | pharmaceuticals | 455 | 50 | 226 | | physics | 467 | 50 | 329 | Queries are spread evenly over 6 languages (en/fr/de/es/it/pt), as in the original. ## Usage ```python from datasets import get_dataset_split_names, load_dataset dataset_id = "hotchpotch/NanoViDoRe_v3_WIP_converted" for split in get_dataset_split_names(dataset_id, "qrels"): corpus = load_dataset(dataset_id, "corpus", split=split) # use "image" for visual retrieval, "text" for OCR text queries = load_dataset(dataset_id, "queries", split=split) qrels = load_dataset(dataset_id, "qrels", split=split) ``` ## Scores are not comparable with the original protocol The original README evaluates every query against the full merged 4,000-page corpus. Here each task only searches its own pages, so pages from other tasks no longer act as extra distractors and scores are somewhat higher. Example with [`vidore/colqwen2-v1.0`](https://huggingface.co/vidore/colqwen2-v1.0), mean nDCG@10 over the 8 tasks: | setting | binary qrels | graded qrels | | --- | ---: | ---: | | original (merged 4,000-page corpus) | 0.4663 | 0.4685 | | this conversion (per-task corpus) | 0.4876 | 0.4889 | ## License Same terms as the source datasets. The query/qrels annotations of ViDoRe v3 are released under CC BY 4.0. The page images and their `markdown` text inherit the license of their original publishers (public domain, CC BY 4.0, Etalab 2.0, CC BY-SA 3.0 and other open reuse licenses); see the `documents_metadata` config of each [ViDoRe v3 dataset](https://huggingface.co/collections/vidore/vidore-benchmark-v3) for the per-document license. ## Citation Please cite the original works: ```bibtex @misc{loison2026vidorev3, title = {ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios}, author = {António Loison and Quentin Macé and Antoine Edy and Victor Xing and Tom Balough and Gabriel Moreira and Bo Liu and Manuel Faysse and Céline Hudelot and Gautier Viaud}, year = {2026}, eprint = {2601.08620}, archivePrefix = {arXiv}, primaryClass = {cs.AI}, url = {https://arxiv.org/abs/2601.08620}, } ``` The Nano subsampling is by LightOn: [`lightonai/NanoViDoRe_v3`](https://huggingface.co/datasets/lightonai/NanoViDoRe_v3).