datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
africa-synth-aid-flows-medical-multimodal-fracture-all
Africa Synth Aid Flows Medical Multimodal Fracture All | Africa (Electric Sheep Africa metadata inventory)
Size category: 1K<n<10K - Formats: json - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-aid-flows-medical-multimodal-fracture-all.multimodality-poc-llama31-ruler16k
Multimodality PoC corpus — Llama-3.1-8B-Instruct on RULER-16K
Raw pre-RoPE query and hidden-state tensors captured during prefill, used
to study whether the per-(layer, kv_head) query distribution is unimodal
Gaussian (the assumption underpinning Expected Attention's MGF closed-form
in kvpress).
What's in here
65 .npz files, one per (RULER task, prompt_index) pair (13 tasks × 5
prompts).
Each file (~414 MB) contains:
field
dtype
shape
meaning
hidden
float16… See the full description on the dataset page: https://huggingface.co/datasets/June30916/multimodality-poc-llama31-ruler16k.multimodal-annual-reports
Multimodal Annual Reports
A document question-answering benchmark built from 20 complete corporate annual and integrated reports. It contains 595 English questions with reference answers, source evidence, original PDFs, reviewed HTML and Markdown representations, and figure/table crops. Questions require interpreting narratives, tables, and non-tabular visuals, including Japanese and French sources.
MIT covers original benchmark contributions only. Source reports and their… See the full description on the dataset page: https://huggingface.co/datasets/hashmortar/multimodal-annual-reports.multimodal-video-annotation-samples
Video Annotation Samples – SuperviseLab
SuperviseLab provides professional video annotation data for training multimodal AI models. This public sample dataset demonstrates our annotation methodology and output quality across diverse video content categories.
Note: All visual assets in this dataset have been abstracted (pixelated mosaic) to protect source privacy. Uploader identity, original titles, and all identifiable metadata have been removed. This is a demonstration dataset… See the full description on the dataset page: https://huggingface.co/datasets/superviselab/multimodal-video-annotation-samples.multimodal-peer-collaboration-samples
Multimodal Peer Collaboration Samples - Embodied Map Task with Two Camera Angles
Two non-experts collaborate to build working circuits under asymmetric information: the instructor has the manual, the student has the components, and synchronized audio and dual-camera video capture how shared understanding emerges.
▶ Watch the interactions · See Expert Instruction samples · Discuss the full collection
Sister collection: Expert Instruction, a teacher and a student in… See the full description on the dataset page: https://huggingface.co/datasets/fluid-concepts/multimodal-peer-collaboration-samples.agent-spaces-tracesmultimodalpragmatic
Multimodal Pragmatic Jailbreak on Text-to-image Models
Project page | Paper | Code
The Multimodal Pragmatic Unsafe Prompts (MPUP) is a dataset designed to assess the multimodal pragmatic safety in Text-to-Image (T2I) models.
It comprises two key sections: image_prompt, and text_prompt.
Dataset Usage
Downloading the Data
To download the dataset, install Huggingface Datasets and then use the following command:
from datasets import load_dataset… See the full description on the dataset page: https://huggingface.co/datasets/tongliuphysics/multimodalpragmatic.hse-multimodal-rag-corpus
HSE Multimodal RAG Corpus
Chunks, labeled QA (including out-of-scope abstention), and published retrieval metrics.
chunks.jsonl
qa_pairs.jsonl
eval_results.json
benchmark_report.json
unified-multimodal-ingestion-pipeline
Unified Multimodal Ingestion Pipeline - flattened dataset + audit trail
This dataset is the output of the
unified-multimodal-ingestion-pipeline.
A synthetic messy nested archive of mixed PDF / image / text files (wrong or
missing extensions, exact copies, and near-identical variants) is flattened by
content type, then deduplicated in two passes (exact md5, then fuzzy
MinHash/perceptual-hash), and every routing decision is recorded in a
confidence-scored audit log.
Task… See the full description on the dataset page: https://huggingface.co/datasets/narinzar/unified-multimodal-ingestion-pipeline.multimodal-image-text-retrieval-artifactsnekoneko-industry-multimodal-retrieval-benchmark
Nekoneko Industry Multimodal Retrieval Benchmark v0.1
English description: A fully synthetic Japanese benchmark for evaluating retrieval across office documents, spreadsheets, presentations, text-to-speech audio, and silent videos.
公開版: v0.1。架空の企業情報を使う小規模な検索ベンチマークです。オリジナルの資料・質問・注釈はCC BY 4.0、評価スクリプトはMITで提供します。合成音声の作成方法と権利確認の範囲は SOURCE_NOTICE.md を参照してください。
概要… See the full description on the dataset page: https://huggingface.co/datasets/MakiAi/nekoneko-industry-multimodal-retrieval-benchmark.