datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LLaVA-OneVision-1.5-Instruct-Data-qwen-formatlogiqa2_formatted
Dataset Card for "logiqa2_formatted"
More Information needed
doc-formats-parquet-1glaive-function-calling-v2-formatted
original dataset: glaiveai/glaive-function-calling-v2
{'system_message': 'You are a helpful assistant with access to the following functions. Use them if required -',
'function_description': '{\n "name": "get_random_quote",\n "description": "Get a random quote",\n "parameters": {}\n}',
'conversations': [{'content': 'Hi, can you help me with something?',
'role': 'user'},
{'content': "Of course! I'm here to assist you. What do you need help with?",
'role': 'assistant'}… See the full description on the dataset page: https://huggingface.co/datasets/heegyu/glaive-function-calling-v2-formatted.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)
fineweb-edu-format-topic
FineWeb-Edu w/ Topic and Format Annotations
FineWeb-Edu dataset consists of 1.3T tokens annotated for Topic and Format using wissamantoun/WebOrganizer-TopicClassifier-ModernBERT and wissamantoun/WebOrganizer-FormatClassifier-ModernBERT classifiers.
Similar to WebOrganizer/Corpus-200B but using FineEdu instead of DCLM.
Topic Labels:
Adult
Art & Design
Software Dev.
Crime & Law
Education & Jobs
Hardware
Entertainment
Social Life
Fashion & Beauty
Finance & Business
Food & Dining… See the full description on the dataset page: https://huggingface.co/datasets/wissamantoun/fineweb-edu-format-topic.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-formattedglaive-function-calling-v2-formatted
Dataset Card for "glaive-function-calling-v2-formatted"
More Information needed
hle-no-img-prompt-completion-formatmath-hendrycks-genesys-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.gpqa_formatted
Dataset Card for GPQA
Formatted version of original GPQA dataset. This removes most columns and adds single columns options and answer to contain a list of the possible answers and the index of the correct one.
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy… See the full description on the dataset page: https://huggingface.co/datasets/jeggers/gpqa_formatted.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.vidore_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.DeepScaleR-Preview-Dataset-verl-formatTriviaQA-in-SQuAD-format
Dataset Card for "TriviaQA-in-SQuAD-format"
More Information needed
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.tool-use-llama-format
Open Paws Tool Use 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: Tool Use 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/tool-use-llama-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.descent-format-geom
Dataset Card for Meta-OMol25 Descent Formatted GEOM v1.0
Dataset Details
Dataset Description
Meta-OMol25 provides molecular structures, coordinates, energies, and forces, and we derived mapped SMILES for broad OpenFF parameter fitting workflows. This release is designed for general fitting and evaluation of van der Waals and valence terms.
Curated by: Jennifer A Clark; jaclark5
Funded by: Open Force Field Initiative
Shared by: Open Force Field Initiative, Open… See the full description on the dataset page: https://huggingface.co/datasets/openforcefield/descent-format-geom.coco_captioning_complete_format