datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MobileGym-ConAct-Trajectories
MobileGym-ConAct-Trajectories
Dataset Viewer · MobileGym · MemGUI-Agent · Paper
Abstract
MobileGym-ConAct-Trajectories is a release of successful mobile GUI-agent rollouts collected in the MobileGym simulator. Each trajectory is selected from judge-verified rollouts using a deterministic per-task rule: retain the shortest structurally valid success, then break ties by source run and episode ID. The release preserves screenshots, the rendered prompt supplied to the… See the full description on the dataset page: https://huggingface.co/datasets/Ma-Vector/MobileGym-ConAct-Trajectories.HumaniBench
HumaniBench: A Human-Centric Benchmark for Large Multimodal Models Evaluation
**HumaniBench** is a benchmark for evaluating large multimodal models (LMMs) using real-world, human-centric criteria. It consists of 32,000+ image–question pairs across 7 tasks:
✅ Open/closed VQA
🌍 Multilingual QA
📌 Visual grounding
💬 Empathetic captioning
🧠 Robustness, reasoning, and ethics
Each example is annotated with GPT-4o drafts, then verified by experts to ensure quality and… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/HumaniBench.VectorGym
VectorGym: A Multi-Task Benchmark for SVG Code Generation and Manipulation
Dataset Description
VectorGym is a unified corpus for training and evaluating multimodal models on complex vector graphics understanding and manipulation. This dataset provides high-quality, human-annotated data supporting four key SVG tasks:
Sketch-to-SVG: Converting hand-drawn sketches into clean vector graphics
Text-to-SVG: Generating SVG content from natural language descriptions
SVG… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow/VectorGym.vectors2vibes-discogs-metadata
Vectors2Vibes Discogs Metadata
Metadata for 24.6k tracks derived from Discogs Data and MTG Discogs-VI-YT. No audio files.
Note that earliest release year data is derived from MusicBrainz, as Discogs release year data is sparse and often unreliable.
Quick Facts:This dataset contains 24,689 tracks (release dates spanning from 1890-2026).
The top 5 represented decades are: 1960s (19.39%), 1970s (15.09%), 1980s (14.81%), 1950s (14.96%), and 1990s (14.27%).
The top 5 represented genres… See the full description on the dataset page: https://huggingface.co/datasets/vectors2vibes/vectors2vibes-discogs-metadata.VLDBench
VLDBench: Evaluating Multimodal Disinformation with Regulatory Alignment
📜 Paper (Preprint)
📄 VLDBench Evaluating Multimodal Disinformation with Regulatory Alignment (arXiv)
Website
Link
Dataset Summary
VLDBench is a multimodal dataset for news disinformation detection, containing text, images, and metadata extracted from various news sources. The dataset includes headline, article text, image descriptions, and images stored as byte arrays… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/VLDBench.arXiv-AI-papers-multi-vector
Overview
This is a dataset containing individual pages from the top-40 most cited AI papers on arXiv](https://arxiv.org/abs/2412.12121) from the period 2023-01-01 to 2024-09-30.
Only the first 10 pages from each paper is included.
The dataset includes an image of each page as well as a multi-vector embedding using vidore/colqwen2-v1.0.
noto-emoji-vector-512-svg
Dataset Card for "noto-emoji-vector-512-svg"
More Information needed
vectorized_objectsObject image, Text Description (best fit to SD1.5~)
ibm-hls-burn-vectorizeddirectv-zocalos-agosto-5fps_vectors
Dataset Card for "directv-zocalos-agosto-5fps_vectors"
More Information needed
19-4-embeddingsvector-lora-dataset28-3-26-both-emeddingsDotsOnCurveinsert-vectors-test-1fps
Dataset Card for "insert-vectors-test-1fps"
More Information needed
aixpert
AIXPERT
License
This dataset is licensed under the CC BY-NC-SA 4.0 License.
📚 Citation
@article{TODO
}
vessels-motifs-stamps-vectors-previewnmb-plus-cleanfrom datasets import load_dataset
from IPython.display import display, HTML
# Load the dataset
ds = load_dataset("vector-institute/newsmediabias-plus-clean")
# Shuffle the dataset and select 50 random records
random_records = ds['train'].shuffle(seed=42).select(range(50)) # Adjust 'train' if needed
# Display the first few records (e.g., article text and image if available)
for i, record in enumerate(random_records):
article_text = ' '.join(record['article_text'].split()[:200]) # First… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/nmb-plus-clean.vessels-motifs-stamps-vectorsvessels-motifs-vectorsLinePlotIntersectionflux-schnell-vector-dataset