datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
basic-math-problems-with-step-by-step-solutionsja_asr.jsut_basic5000qwen35-math-rl-by-length
수학 RL 학습 데이터 — 응답 예산별 3묶음
폴더 하나만 받으면 됩니다. 학습에 쓸 응답 길이 상한에 맞는 폴더를 고르세요.
각 폴더는 그 자체로 완결이고, 데이터·리워드·루브릭이 모두 들어 있습니다.
폴더
응답 상한
문제 수
이 예산에서 늘어난 분
힌트판
루브릭 보유
to-16k/
16,384
2,429
+2,429
2,069
2,313
to-32k/
32,768
5,542
+3,113
4,245
5,072
to-64k/
65,536
6,455
+913
4,245
5,724
폴더는 누적입니다. to-32k/ 는 to-16k/ 의 문제를 포함합니다.
응답 32,768 로 학습한다면 to-32k/ 하나만 받으면 됩니다.
구간 전용도 따로 있습니다 — bands/
누적이 아니라 그 구간에서 처음 learnable 이 되는 문제만 담았습니다.
세 파일을 합치면 to-64k/ 와 같아집니다.… See the full description on the dataset page: https://huggingface.co/datasets/BASIC-MATH/qwen35-math-rl-by-length.instruct-data-basics-smollm-H4Datasets of basic instructions and answers for SmolLM-Instruct models trainings: it includes answers to greetings and questions such as "Who are you". This dataset was included in training of SmolLM-Instruct v0.2 but we didn't notice that it had an impact on model generations.
We recommend using this generic larger dataset of multi-turn everyday conversations: https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k
cs336-basics-collection
CS336 Assignment 1 — Pre-tokenized Data & BPE Tokenizers
This repository contains preprocessing artifacts produced for Stanford CS336: Language Modeling from Scratch, Spring 2025 — Assignment 1: Basics.
It includes:
pre-tokenized TinyStories train/validation data,
pre-tokenized OpenWebText (OWT sample) train/validation data,
byte-level BPE vocabularies and merge tables for both datasets.
The main purpose of this repository is to avoid repeating the relatively expensive… See the full description on the dataset page: https://huggingface.co/datasets/victorhu493/cs336-basics-collection.part2-basic_foldThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "hand",
"total_episodes": 7536,
"total_frames": 3353104,
"total_tasks": 24,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:7536"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/gsethia08/part2-basic_fold.seed-basic_terminal_taskslol-basic-matches-challenger-10k
GPTilt: 10K League of Legends Challenger Matches
This dataset is part of the GPTilt open-source initiative, aimed at democratizing access to high-quality LoL data for research and analysis, fostering public exploration, and advancing the community's understanding of League of Legends through data science and AI. It provides detailed data from high-elo matches.
By using this dataset, users accept full responsibility for any consequences arising from its use. GPTilt assumes no… See the full description on the dataset page: https://huggingface.co/datasets/gptilt/lol-basic-matches-challenger-10k.basic-math-operations
Mathematical Reasoning Dataset
A comprehensive dataset of mathematical problems with detailed step-by-step reasoning and Python verification, designed for training language models to perform mathematical computations with clear explanations.
Dataset Description
This dataset contains mathematical problems across 20 different operation types, each with detailed step-by-step reasoning that explains the mathematical concepts, handles edge cases, and includes Python code… See the full description on the dataset page: https://huggingface.co/datasets/mkurman/basic-math-operations.editworld_data_basicv0
How to load the image from the parquet file?
import pyarrow.parquet as pq
test_parquet_path = 't2i_branch/group_0.parquet'
table = pq.read_table(test_parquet_path)
df = table.to_pandas()
from PIL import Image
import io
binary_image = df['original_image'][1]
binary_image_t = df['target_image'][1]
image = Image.open(io.BytesIO(binary_image))
image.save("origin.png")
image = Image.open(io.BytesIO(binary_image_t))
image.save("target.png")
SpatialReasoner-Basic3D-QAbasic-arithmetic
Basic Arithmetic
Difficulty-balanced arithmetic dataset (addition, subtraction, multiplication,
division) for evaluating and fine-tuning language models. Problems are classified
into four difficulty tiers (easy, medium_easy, medium_hard, hard) based on
Qwen2.5-0.5B-Instruct performance. Includes 10k training samples, 200
validation, and 400 test (in-domain + out-of-domain phrasings).
Splits
config
split
rows
what
default
train
10,000
training set… See the full description on the dataset page: https://huggingface.co/datasets/ChrisMcCormick/basic-arithmetic.bouncingsim-GRAVITY-basicbouncingsim-ROTOBJ-basicbouncingsim-ROTBOX-basicbouncingsim-ROTOBJ_ROTBOX-basicasia-who-population-using-at-least-basic-sanitation-services
Population using at least basic sanitation services (%) | Asia (WHO GHO)
🌏 3,206 observations · 48 Asia countries · 2000–2024 · Repackaged by Electric Sheep Asia
TL;DR
This dataset contains 3,206 observations of Population using at least basic sanitation services (%) data across 48 Asia countries, spanning 2000–2024, covering 1 distinct indicators.
About the source
Source: WHO Global Health Observatory
Publisher: World Health Organization… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-who-population-using-at-least-basic-sanitation-services.bouncingsim-MULTIOBJ-basicbouncingsim-MULTIBOX_MULTIOBJ-basicbouncingsim-ROTOBJ_MULTIBOX-basicbouncingsim-MOVBOX-basicbouncingsim-ROTOBJ_GRAVITY-basicbouncingsim-MOVBOX_GRAVITY-basicbouncingsim-MULTIBOX-basicbouncingsim-MULTIOBJ_MULTIOBJ-basicbouncingsim-ROTOBJ_MOVBOX-basicbouncingsim-ROTOBJ_MULTIOBJ-basicbasic-skillsbouncingsim-ROTBOX_MOVBOX-basicbasic_korean_dict
Dataset Card for "basic_korean_dict"
This dataset is a NLP learnable form of Korean Basic Dictionary(한국어기초사전).
It follows the original copyright policy (cc-by-sa-2.0)
Some words have usage examples in other languages, effectively rendering this into a parallel corpus.
This version is built from xls_20230601
한국어 기초 사전을 학습 가능한 형태로 처리한 데이터입니다.
한국어 기초 사전의 저작권을 따릅니다.
여러 언어로 이루어진 표제어들이 있어 병렬 말뭉치의 기능이 있습니다.
xls_20230601으로부터 생성되었습니다.