datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
standard-chess-games
[!CAUTION]
This dataset is still a work in progress and some breaking changes might occur.
Lichess Rated Standard Chess Games Dataset
Dataset Description
6,771,826,271 standard rated games, played on lichess.org, updated monthly from the database dumps.
This version of the data is meant for data analysis. If you need PGN files you can find those here. That said, once you have a subset of interest, it is trivial to convert it back to PGN as shown in the Dataset Usage… See the full description on the dataset page: https://huggingface.co/datasets/Lichess/standard-chess-games.MegaPairs-Standard
MegaPairs-Standard (Standardized Version)
Dataset Summary
This is a standardized, high-efficiency version of the JUNJIE99/MegaPairs dataset.
Why use this version?
The original dataset is distributed as a massive Tar archive containing millions of images, accompanied by a separate JSONL annotation file.
The Problem: Using the original format requires extracting terabytes of small files (which can exhaust disk inodes) or writing complex logic to read from archives. It… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/MegaPairs-Standard.home-standard-resultsStandard-Pipeline
Standard Pipeline
Environment adaptation, training evidence, latency profiles, raw demonstrations and evaluation traces, organized by environment and experiment stage.
AirRaid: zero-latency and profile-latency experiments.
Only raw demonstrations are distributed. Generate converted training datasets on the training server. Model weights remain in the dedicated model repository and are referenced from the experiment records. Pin a commit revision for reproducible downloads.… See the full description on the dataset page: https://huggingface.co/datasets/latency-sensitive-bench/Standard-Pipeline.pile-standard-pythia-preshuffledstandardebooks
Standard Ebooks Text Dataset
This dataset contains the full text of public domain books sourced from Standard Ebooks. It is intended for use in Natural Language Processing tasks, particularly Large Language Model pretraining, fine-tuning, and research.
Standard Ebooks provides high-quality, carefully formatted, and proofread versions of classic literature, making this a valuable collection of clean text data.
Dataset Structure
The dataset consists of a single split:… See the full description on the dataset page: https://huggingface.co/datasets/Nelathan/standardebooks.open-ner-standardized
Dataset Card for OpenNER 1.0
OpenNER 1.0 is a standardized collection of openly-available named entity recognition (NER) datasets.
OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies.
We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and… See the full description on the dataset page: https://huggingface.co/datasets/bltlab/open-ner-standardized.glaive_function_calling_v1_standardizedstandard-pde-benchmark
🚀 Quick Start: Download Data
The most reliable way to download this benchmark while preserving the recursive folder structure is using the huggingface_hub library.
1. Install requirements
pip install huggingface_hub h5py
2. Download the entire dataset
Run this Python snippet to mirror the full repository to your local machine:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="kmario23/standard-pde-benchmark"… See the full description on the dataset page: https://huggingface.co/datasets/kmario23/standard-pde-benchmark.riddle_sense_standardizedstandard_chatopenwwebtext-gpt2-50257-standardaddress_standardizationHH-RLHF-Helpful-standardWe process the helpful subset of Anthropic-HH into the standard format. The filtering script is as follows.
def filter_example(example):
if len(example['chosen']) != len(example['rejected']):
return False
if len(example['chosen']) % 2 != 0:
return False
n_rounds = len(example['chosen'])
for i in range(len(example['chosen'])):
if example['chosen'][i]['role'] != ['user', 'assistant'][i % 2]:
return False
if… See the full description on the dataset page: https://huggingface.co/datasets/RLHFlow/HH-RLHF-Helpful-standard.UltraFeedback-preference-standardWe include all the possible comparisons following the Instruct-GPT. We use the fine-grained_score.
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datasets import load_dataset, DatasetDict
from transformers import AutoTokenizer
from tqdm import tqdm
from transformers import AutoTokenizer
ds = load_dataset("openbmb/UltraFeedback", split="train")
import itertools
data = []
for example in ds:
prompt = example['instruction']
responses = {}… See the full description on the dataset page: https://huggingface.co/datasets/RLHFlow/UltraFeedback-preference-standard.standard_group1lichess-standard-rated-2025-07-uci
Lichess Standard Rated 2025-07 (UCI, 1 row per game)
Built from lichess_db_standard_rated_2025-07.pgn.zst.
Columns: event, date, time_control, variant, termination, result,
white_elo, black_elo, num_plies, moves_uci
rw_roman-empire_standard_1_maskrw_pubmed_standard_6_maskjeju-dialect-to-standardThis dataset was created by extracting only labeled text from the Jeju dialect utterance dataset available on AIHub.
by extracting only labeling text.
rw_roman-empire_standard_2_maskRetailAction
RetailAction Dataset
Paper: RetailAction: Dataset for Multi-View Spatio-Temporal Localization of Human-Object Interactions in RetailAccepted at: ICCV 2025 – Retail Vision WorkshopAuthors: Davide Mazzini, Alberto Raimondi, Bruno Abbate, Daniel Fischetti, David M. WoollardOrganization: Standard AI
Overview
RetailAction is a large-scale dataset designed for multi-view spatio-temporal localization of human–object interactions in real-world retail environments.
Unlike… See the full description on the dataset page: https://huggingface.co/datasets/standard-cognition/RetailAction.ride-gold-standard
RIDE Gold Standard
RIDE Gold Standard is the full benchmark-ready release of the RIDE dataset. It contains fixed train/test snapshot splits, a canonical evaluation table, and model-ready representations for train delay prediction on Belgian passenger railway operations.
This release is intended as the primary benchmark tier for RIDE. It is used for full-scale evaluation and comparison of models under the shared RIDE prediction task and evaluation protocol.
Links… See the full description on the dataset page: https://huggingface.co/datasets/orailix/ride-gold-standard.lichess_db_standard_rated_2024-07standard_chat_tool_calling_generalnew_8b_self_corr_standardhongyan_lift_bottle_standardThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "panda",
"total_episodes": 100,
"total_frames": 37265,
"total_tasks": 1,
"total_videos": 200,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:100"
},
"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/gaozj/hongyan_lift_bottle_standard.We-Math2.0-Standard
Dataset Card for We-Math 2.0
GitHub | Paper | Website
We-Math 2.0 is a unified system designed to comprehensively enhance the mathematical reasoning capabilities of Multimodal Large Language Models (MLLMs).
It integrates a structured mathematical knowledge system, model-centric data space modeling, and a reinforcement learning (RL)-based training paradigm to achieve both broad conceptual coverage and robust reasoning performance across varying difficulty levels.
The key… See the full description on the dataset page: https://huggingface.co/datasets/We-Math/We-Math2.0-Standard.rw_pubmed_standard_2_maskstandard_chat_manage_tabs_adversarial