AI Dataset2026
Provides 1000+ hours of high-precision optical motion-capture for humanoid robotics and embodied AI, including full-body skeleton, 20+DoF hands, object 6D, and multi-view video at 120 Hz. Sub-mm spatial accuracy, BVH/CSV/NPZ outputs and Unitree G1 retargets; ideal for imitation learning and sim-to-real, with some raw captures gated by license.
AI Dataset2026
Benchmark for evaluating multimodal LLM safety in Korean cultural contexts — includes KSAFE-MM-G which localizes global safety queries into Korean scenarios and KSAFE-MM-C which targets culture-specific visual-textual vulnerabilities. Provides curated image–text pairs and jailbreak-style prompts to reveal both unsafe behaviors and over-refusal.
AI Dataset2026
Provides kanji-level evaluation data for Japanese TTS: disambiguated sentence contexts targeting 4,378 kanji-reading pairs (2,136 Jōyō kanji) with 13,095 native-speaker–verified sentences and katakana-marked ground-truth readings for kanji-level error metrics.
AI Dataset2026
Collects raw coding-agent sessions—developer prompts, model replies, tool calls, and command output—donated from public repositories and anonymized locally. Organized by agent harness (raw session files + Parquet table), useful for studying agent behavior and tool use; anonymization is best-effort.
AI Dataset2026
A JSON dataset of ~1.1M anonymized coding-assistant instruction→response interactions for training and evaluating code-generation and instruction-following models; packaged for use with pandas/polars and sized at ~459 MB.
AI Dataset2026
Curates ~1.1M instruction–response examples for 'vibe coding' scenarios where developers prompt LLMs to produce implementation plans, architecture choices, and deployment steps. Covers conversation memory, prompt templates, model routing, streaming responses, and scaling considerations; Apache-2.0.
AI Dataset2026
A JSON-format text dataset of 'vibe-coding' prompt–response examples sized in the 1M–10M category. Packaged for Hugging Face Datasets with pandas/polars-ready structure; useful for fine-tuning or evaluation but lacks an explicit license and detailed provenance.
AI Dataset2026
Provides 4,659 agentic single-turn SFT training pairs extracted from Claude Fable‑5, formatted as a single-column parquet for Qwen-style fine-tuning. Includes explicit chain-of-thought (<think>) blocks, XML-serialized <tool_use> calls, PII redaction, and AGPL-3.0 licensing.
AI Dataset2026
A collection of 953 JSON-formatted Fable 5 interaction traces (includes chain-of-thought entries), published on Hugging Face under AGPL-3.0 — meant for fine-tuning or analyzing LLM behavior but subject to license and provenance constraints.
AI Dataset2026
Provides 130k+ bimanual teleoperation trajectories for robot imitation learning, recorded on low-cost YAM two-arm rigs and shared as MCAP episodes with subtask annotations, training code, and checkpoints.
AI Dataset2026
Provides 1.8M synthetic Belgian personas (1.2M records; 300k per language) in Dutch/French/German/English, grounded in Belgian census distributions to improve representativeness for LLM training and evaluation. Includes 23 persona and contextual fields, CC BY 4.0 license, produced with NeMo Data Designer.
AI Dataset2026
Contains 603 coding and math prompt–response pairs produced by Claude Fable‑5 (generated 2026-06-10), provided as a JSONL subset for fine-tuning, evaluation, and behavior analysis. Responses are 'non-thinking' (no chain-of-thought); small, anonymized, and lacking an explicit license.