datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nano-banana-pro-prompts-datasets
🖼️ Nano Banana Pro Prompt Dataset
🖼️ The ultimate Nano Banana Pro prompt dataset (6GB+). 26,000+ image generation prompts with full metadata and preview images. Truly open source: No login, no ads, no redirection. Just pure data for AI image creators.
This project is a massive collection of prompts used for Nano Banana Pro AI image model and the resulting generated images. The entire dataset exceeds 6GB and contains 26,000+ images, all structured into a comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/Goku-OpenLab/nano-banana-pro-prompts-datasets.hubbleNanochatRAEv2-data
RAEv2 Data
Pre-processed datasets and pretrained encoders for RAEv2: Improved Baselines with Representation Autoencoders. All rights to the original owners; per-subset attribution below.
Repo Structure
RAEv2-data/
|-- imagenet-256/ # ImageNet-1k at 256x256 (Arrow)
|-- blip3o-256/ # BLIP3o captioned images (WDS)
|-- render-text-256/ # Rendered-text images (WDS)
|-- scale-rae-256/ # Synthetic FLUX images (WDS)
|-- recon-256/ # Robot… See the full description on the dataset page: https://huggingface.co/datasets/nanovisionx/RAEv2-data.nemotron-3-nano-30b-20260719-spare-games-envs
Nemotron-3-Nano-30B SPARE Self-Play Environments (run_20260719_final)
This dataset packages the self-play generated game environments produced
by a live SPARE (Self-Play with Adaptive cuRriculum Extension) training run
of NVIDIA-Nemotron-3-Nano-30B-A3B. It is a raw-data export for another
agent to pick up, replay, and build its own visualization / weave log from.
Provenance
Run: run_20260719_final
Source Ray job: spare_nemotron_games_mtpg768_1784556397 (the live… See the full description on the dataset page: https://huggingface.co/datasets/msr-spare-1/nemotron-3-nano-30b-20260719-spare-games-envs.laion_subset
Dataset Card for "laion_subset"
More Information needed
NanoBEIR-enNanoMSMARCOultrascale-playbook-dataNanoQuoraRetrievalkiiteitte
Kiiteitte history
Kiiteitte が収集した、今までの選曲履歴。
1時間おきに更新されます。
型
{
// 動画ID
"video_id": "sm44670499",
// タイトル
"title": "library->w4nderers / 足立レイ、つくよみちゃん",
// 投稿者
"author": "名無し｡",
// サムネイルのURL
"thumbnail": "https://nicovideo.cdn.nimg.jp/thumbnails/44670499/44670499.91820835",
// 選曲日時
"date": "2025-02-22 12:51:51",
// 新しく増えたお気に入り数。不明の場合は null
"new_faves": 5,
// 回ったユーザーの数。不明の場合は null
"spins": 13,
// イチ押しリストのユーザーのURL。イチ押しリスト以外から選曲された場合は null… See the full description on the dataset page: https://huggingface.co/datasets/sevenc-nanashi/kiiteitte.code-search-net-python
Dataset Card for "code-search-net-python"
Dataset Description
Homepage: None
Repository: https://huggingface.co/datasets/Nan-Do/code-search-net-python
Paper: None
Leaderboard: None
Point of Contact: @Nan-Do
Dataset Summary
This dataset is the Python portion of the CodeSarchNet annotated with a summary column.The code-search-net dataset includes open source functions that include comments found at GitHub.The summary is a short description of what the… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/code-search-net-python.NanoMTEB-Scandinavian
NanoMTEB-Scandinavian
This dataset is a Nano-style retrieval dataset for HAKARI-bench.
NanoMTEB-Scandinavian is a compact retrieval benchmark for Scandinavian-language MTEB-style task families. It includes Danish, Norwegian, and Swedish retrieval tasks spanning fact verification, question answering, news, encyclopedic content, FAQ retrieval, and social-media retrieval.
Usage
from datasets import load_dataset
dataset_id = "hakari-bench/NanoMTEB-Scandinavian"
split… See the full description on the dataset page: https://huggingface.co/datasets/hakari-bench/NanoMTEB-Scandinavian.nanochat-climbmix-arithmetic-base10
nanochat ClimbMix + Base-10 Arithmetic
This dataset contains the first 170 shuffled ClimbMix training shards
used by nanochat's speedrun. The deterministic base-10 arithmetic corpus is
mixed into shards 00000..00149; the final
20 train shards are unchanged web-only padding.
The original validation shard (shard_06542.parquet) is also
copied unchanged.
Arithmetic corpus
Family
Examples
a + b = c (all ordered pairs 0..2000, two exposures)
8,008,002
a + b… See the full description on the dataset page: https://huggingface.co/datasets/Yujivus/nanochat-climbmix-arithmetic-base10.ICL_UMI_16jan_pickleToolMind
ToolMind: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset
ToolMind is a large-scale, high-quality tool-agentic dataset with 160k synthetic data instances generated using over 20k tools and 200k augmented open-source data instances.
Our data synthesis pipeline first constructs a function graph based on parameter correlations and then uses a multi-agent framework to simulate realistic user–assistant–tool interactions.
Beyond trajectory-level validation, we employ fine-grained… See the full description on the dataset page: https://huggingface.co/datasets/Nanbeige/ToolMind.NanoFiQA2018nanarehananare
Bangumi Image Base of Na Nare Hana Nare
This is the image base of bangumi Na Nare Hana Nare, we detected 146 characters, 6818 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/nanarehananare.NanoJev-Data
NanoJev-Data — Unified game supervision and recorded evaluation
The complete data package for the current NanoJev model:
Maze, Snake, ViZDoom Basic and Predict Position. It includes the exact mixed
supervised-learning inputs, full expert episodes, frozen evaluation cohorts,
recorded comparisons, and the six-source hard Maze/Snake demonstration.
Training data
Split
Rows per hard/soft variant
Train
10,898
Dev
1,715
Calibration
1,709
Test
2,496
OOD… See the full description on the dataset page: https://huggingface.co/datasets/C-Tianyu/NanoJev-Data.RVCBench
RVCBench
RVCBench is a benchmark dataset for studying robustness in voice cloning, text-to-speech, speaker privacy, audio protection, adversarial audio perturbations, and related audio generation pipelines.
Dataset page: https://huggingface.co/datasets/Nanboy/RVCBench
Code repository: https://github.com/Nanboy-Ronan/RVCBench
Paper: https://arxiv.org/abs/2602.00443
RVCBench is designed for evaluating how modern voice cloning (VC), TTS, and audio generation systems behave under… See the full description on the dataset page: https://huggingface.co/datasets/Nanboy/RVCBench.c4-nanochatbpe-10B
c4-nanochatbpe-10B
C4 (en) (from allenai/c4), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536)
and packaged as flat uint16 token-id .bin files for fast memmap training.
file
split
tokens
train.bin
train
10,000,000,000
val.bin
val
168,272,017
train and val are disjoint held-out partitions. Each .bin is a raw
little-endian uint16 stream (no header); token count = filesize / 2, and
train.meta.json / val.meta.json carry the full metadata. The tokenizer/files… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/c4-nanochatbpe-10B.SIFT1B-DiskANN
SIFT-1B Dataset & Disk Index
The SIFT-1B (BigANN) dataset and pre-built disk-based ANN index.
Built February 2026 on Intel Xeon 8462Y+ (Sapphire Rapids) with 800GB RAM.
Build Parameters
Parameter
Value
Dataset
SIFT-1B (1,000,000,000 vectors, 128-dim, uint8)
Graph R
128 (max degree)
Build L
200 (search list size during construction)
PQ chunks
32 (4 dimensions per sub-quantizer)
Build time
~2 days
Files
Raw Data… See the full description on the dataset page: https://huggingface.co/datasets/Nanvivi/SIFT1B-DiskANN.nanatsunomaken
Bangumi Image Base of Nanatsu No Maken
This is the image base of bangumi Nanatsu no Maken, we detected 118 characters, 6989 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the… See the full description on the dataset page: https://huggingface.co/datasets/KotiyaSanae/nanatsunomaken.fineweb-nanochatbpe-100M
fineweb-nanochatbpe-100M
FineWeb-Edu (from karpathy/fineweb-edu-100b-shuffle), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536)
and packaged as flat uint16 token-id .bin files for fast memmap training.
This is a 100-million-token slice for data-constrained experiments. The train.bin
is the byte-exact first 100,000,000 tokens (bytes [0, 200000000)) of the parent
alexkstern/fineweb-nanochatbpe-20B
train.bin. The val.bin is byte-identical to the parent's val.bin.… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-100M.NanoSciFactNanoNFCorpusNanoBEIR-deminipile_100_samplesnanatsunotaizaimokushirokunoyonkishi
Bangumi Image Base of Nanatsu No Taizai - Mokushiroku No Yonkishi
This is the image base of bangumi Nanatsu no Taizai - Mokushiroku no Yonkishi, we detected 99 characters, 9289 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/nanatsunotaizaimokushirokunoyonkishi.NanoNQ