datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pptx-format-error-200
PPTX 格式错误数据集（200 样本子集）
每个 pptx 都是单页幻灯片。perturbed_<id>.pptx 是在 original_<id>.pptx 基础上注入
格式扰动后的版本，两者一一配对，文件名中的 <id> 即样本编号。
目录
perturbed/ — 200 个含格式错误的 pptx（核心）
original/ — 200 个对应的未扰动 pptx（参考/对照）
png/ — 每个样本 original 与 perturbed 的渲染图
labels/ — 标注
MANIFEST.tsv — 全部文件的 sha256 + 字节数
标注说明
labels/perturb_info.json — 记录了具体扰动的样本，字段 perturb_types 取值为
size / position / zorder / font_size / font / italic，indices 为被改动的
shape… See the full description on the dataset page: https://huggingface.co/datasets/XINLI1997/pptx-format-error-200.Krea-2-Turbo-Checkpoint-Format-Benchmark
Krea 2 Turbo ComfyUI Format Fidelity Benchmark
This release is a paired, deterministic comparison of eight Krea 2 Turbo checkpoint formats in ComfyUI: BF16, FP8 Scaled, INT8 ConvRot, MXFP8, NVFP4, INT4 ConvRot W4A4, GGUF Q8_0, and GGUF Q4_K_M. It contains 240 scored 1024×1024 images, saved float32 decoded tensors and final latents, every denoising trajectory, raw metric tables, telemetry, statistical comparisons, and reproduction code.
Main result
BF16 is the… See the full description on the dataset page: https://huggingface.co/datasets/Merserk/Krea-2-Turbo-Checkpoint-Format-Benchmark.text-dataset-tiny-code-script-py-format
USED of tahamajs/medicine_ds_persian for .parquet file
USED of Alijafarixcs2/persian-it-llama2-2k for .parquet file
USED of Abirate/english_quotes for .jsonl file
NEW FILES (05/12/2025)
NEW FILES (12/26/2025)
NEW FILES (02/15/2026)
coda-lm-llava-format
CODA-LM Dataset Card
CODA-LM is the multi-modal version of the CODA dataset, used in the CODA-LM paper. Both English and Chinese annotations are available. Check detailed usage in our Github repo.
This repo contains the CODA-LM dataset, which has been reorganized in the LLaVA data format.
You are also welcome to check the original CODA-LM data which contains more metadata vanilla annotations.
Usage
from datasets import load_dataset
# name can be selected from… See the full description on the dataset page: https://huggingface.co/datasets/KaiChen1998/coda-lm-llava-format.Rexverse-2M-formattedhle-no-img-prompt-completion-formatpico-banana-smolvlm-format-with-rejected-answer
pico-banana-smolvlm-format-with-rejected-answer
Balanced image-level tampering detection dataset in SmolVLM-style format
with chosen/rejected answer pairs, derived from the pico-banana MCQ
pipeline. Suitable for preference learning (e.g. DPO) and RLHF-style training.
Dataset overview
Same as vanloc1808/pico-banana-smolvlm-format, but each example includes a
rejected_answer field: the answer from the counterpart sample (same
edited/original image pair, opposite… See the full description on the dataset page: https://huggingface.co/datasets/vanloc1808/pico-banana-smolvlm-format-with-rejected-answer.vidore_v3_finance_en_mteb_format
Vidore3FinanceEnRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_finance_en
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3FinanceEnRetrieval")
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_en_mteb_format.vidore_v3_computer_science_mteb_format
Vidore3ComputerScienceRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_computer_science
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_computer_science_mteb_format.calvin_task_D_D_scale_100_lerobo_formatThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "panda",
"total_episodes": 5124,
"total_frames": 303794,
"total_tasks": 389,
"total_videos": 0,
"total_chunks": 6,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:5124"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/ducido/calvin_task_D_D_scale_100_lerobo_format.vidore_v3_industrial_mteb_format
Vidore3IndustrialRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_industrial
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3IndustrialRetrieval")… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_industrial_mteb_format.droidvidore_v3_hr_mteb_format
Vidore3HrRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_hr
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3HrRetrieval")
evaluator = mteb.MTEB([task])… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_hr_mteb_format.vidore_v3_finance_fr_mteb_format
Vidore3FinanceFrRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_finance_fr
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3FinanceFrRetrieval")
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_fr_mteb_format.vidore_v3_pharmaceuticals_mteb_format
Vidore3PharmaceuticalsRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_pharmaceuticals
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_pharmaceuticals_mteb_format.rlaif-v_formattedfrom datasets import load_dataset, features
def format(examples):
"""
Convert prompt from "xxx" to [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "xxx"}]}]
and chosen and rejected from "xxx" to [{"role": "assistant", "content": [{"type": "text", "text": "xxx"}]}].
Images are wrapped in a list.
"""
output = {"images": [], "prompt": [], "chosen": [], "rejected": []}
for image, question, chosen, rejected in zip(examples["image"]… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/rlaif-v_formatted.vidore_v3_energy_mteb_format
Vidore3EnergyRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_energy
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3EnergyRetrieval")
evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_energy_mteb_format.vidore_v3_physics_mteb_format
Vidore3PhysicsRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_physics
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3PhysicsRetrieval")
evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_physics_mteb_format.VG150-coco-format
VG150 — Visual Genome 150 (COCO format)
This dataset is the standard VG150 split of
Visual Genome
(Krishna et al., 2017), the most widely used benchmark for Scene Graph Generation,
reformatted in standard COCO-JSON format. VG150 contains the top 150 object categories
and 50 relations from the original Visual Genome dataset, selected by frequency in the
Scene Graph Generation by Iterative Message Passing paper.
This version in COCO format was produced as part of the… See the full description on the dataset page: https://huggingface.co/datasets/maelic/VG150-coco-format.JL1-CUP-2024-Second-Format
JL1 CUP 2024 — Second-track format for semantic change detection
Bi-temporal 256×256 RGB patches with per-pixel semantic maps at times T1/T2 and a binary change map, aligned with the data split described in the literature for the JL1 cropland change-detection benchmark (Second Track / JL1-Second style layout).
Source
Resource
URL
JL1 Mall contest information
contest page
JL1 data / resources portal
resrepo
Data are provided by the JL1 / Jilin-1 ecosystem… See the full description on the dataset page: https://huggingface.co/datasets/BiliSakura/JL1-CUP-2024-Second-Format.visual-qa-llama-format
Open Paws Visual Qa Llama Format
This dataset is part of the Open Paws initiative to develop AI training data aligned with animal liberation and advocacy principles. Created to train AI systems that understand and promote animal welfare, rights, and liberation.
Dataset Details
Dataset Type: Multimodal Data
Format: JSONL (JSON Lines)
Languages: Multilingual (primarily English)
Focus: Animal advocacy and ethical reasoning
Organization: Open Paws
License: Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/open-paws/visual-qa-llama-format.BDD100K-enrichedcoco_captioning_complete_formatGQA200-coco-format
GQA — General Question Answering (COCO format)
This dataset is the GQA200 split of
the GQA dataset
(Hudson et al., 2019), reformatted in standard COCO-JSON format.
GQA200 contains the top 200 object categories
and 100 relations from the original GQA dataset, selected by frequency in the
Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation
paper. This dataset has no official test split since it was used
for question answering rather than… See the full description on the dataset page: https://huggingface.co/datasets/maelic/GQA200-coco-format.LLaVA_Instruct_150K_complete_formatlocalized_narratives_trajectory_formatworldcuisines_format_sea_country_only_with_metadatahle-no-img-conversational-formatlaion-1m
LAION-Subset (Lance Format)
A Lance-formatted slice of the LAION image-text corpus (~1M rows) with inline JPEG bytes, CLIP image embeddings (img_emb), full metadata, and a pre-built ANN index — all available directly from the Hub at hf://datasets/lance-format/laion-1m/data/train.lance.
Key features
Inline JPEG bytes in the image column — no sidecar files, no image folders.
Pre-computed CLIP image embeddings (img_emb, 768-dim) with a bundled IVF_PQ index for… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/laion-1m.handwriting-ocr
Handwriting OCR (Lance Format)
This Lance-formatted version of the Doctor's Handwritten Prescription BD dataset contains 4,680 cropped PNG images of handwritten medicine names from Bangladesh. Each row keeps the original image bytes with the medicine and generic-name labels, plus deterministic search metadata derived from those labels. The dataset contains three source-preserved splits: train, validation, and test.
[!NOTE]
Training note: The same samples appear repeatedly… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/handwriting-ocr.