datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
turkish-llm-dataset
Turkish Pretraining Corpus
Dataset Description
This dataset is a Turkish pretraining corpus created by combining BellaTurca (excluding ForumSohbetleri), Cosmos-Turkish-Corpus-v1.0, and FineWeb-2 Turkish Categorized, followed by cleaning, normalization, and deduplication. It is intended for the development, training, and evaluation of Turkish language models.
This dataset was prepared as part of a capstone project conducted by a group of students from Sabancı… See the full description on the dataset page: https://huggingface.co/datasets/tascib/turkish-llm-dataset.Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.llm_datasetsharmful-datasetru-llm-judge-dataset
RU-LLM-Judge-Dataset
Датасет собирается инкрементально в течение нескольких сессий бесплатного Google Colab.
Текущий объём: 19,503 суждений (по состоянию на последний запуск).
Прогресс к цели (5,000 суждений)
[████████████████████] 100% (19,503 / 5,000)
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2026-08-13… See the full description on the dataset page: https://huggingface.co/datasets/AtesiT/ru-llm-judge-dataset.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.Bitext-events-ticketing-llm-chatbot-training-dataset
Bitext - Events and Ticketing Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset.moai-llm-tokenized-dataset-cacheawesome-llm-datasets-only-ChineseSwallow-Nemotron-Post-Training-Dataset-v1
Swallow-Nemotron-Post-Training-Dataset-v1
The Swallow LLM Project constructed the Swallow-Nemotron-Post-Training-Dataset-v1 based on the math, code, and stem subsets of the NVIDIA Nemotron-Post-Training-Dataset-v1, as illustrated in the figure below.
Dataset Construction
The original Thinking Trajectories and Assistant Outputs in the Nemotron-Post-Training-Dataset-v1 were synthesized using DeepSeek-R1-0528.
However, we identified an issue with the Thinking… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/Swallow-Nemotron-Post-Training-Dataset-v1.llm-jailbreak-prompt-injection-dataset
LLM Jailbreak & Prompt-Injection Dataset
A unified safety dataset combining 30+ public sources for training LLM
guardrails, content moderation classifiers, and response-safety filters.
Schema (orthogonal multi-label, WildGuard-style)
Instead of a single binary is_dangerous, every example carries four
orthogonal labels matching the structure used by AI2 WildGuard, IBM Granite
Guardian, and Azure Prompt Shields:
Column
Type
Description
prompt
str
The user/attack… See the full description on the dataset page: https://huggingface.co/datasets/Necent/llm-jailbreak-prompt-injection-dataset.benign-datasetllm_plan_gen_dataset_accu_t1_t3_t4
[!IMPORTANT]
This is the training dataset for the ICAPS 2025 paper "Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation".
from pathlib import Path
import os
import jsonlines
from copy import deepcopy
from datasets import load_dataset
from icecream import ic
import enum
from enum import IntEnum
from enum import auto
class CONFIG_TYPES(enum.Enum):
# "type_id": ['t0', 'accu-t1', 'accu-t2', 'accu-t3', 'accu-t4', 'accu-t4', 'accu-t1+t4', 'accu-t2+t4']… See the full description on the dataset page: https://huggingface.co/datasets/huangsukai/llm_plan_gen_dataset_accu_t1_t3_t4.llm_plan_gen_dataset_accu_t4
[!IMPORTANT]
This is the training dataset for the ICAPS 2025 paper "Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation".
from pathlib import Path
import os
import jsonlines
from copy import deepcopy
from datasets import load_dataset
from icecream import ic
import enum
from enum import IntEnum
from enum import auto
class CONFIG_TYPES(enum.Enum):
# "type_id": ['t0', 'accu-t1', 'accu-t2', 'accu-t3', 'accu-t4', 'accu-t4', 'accu-t1+t4', 'accu-t2+t4']… See the full description on the dataset page: https://huggingface.co/datasets/huangsukai/llm_plan_gen_dataset_accu_t4.nepali_llm_datasets
Nepali LLM Datasets
This repository contains two configurations of Nepali LLM datasets:
Configurations
1. Scrapy Engine
Description: Contains data collected using a web scraping engine.
Files: [List any specific files or formats]
2. Nepberta
Description: This dataset is derived from the Nepberta project and contains cleaned data specifically related to the project. The dataset contains **cleaned text chunks of size ~50 mb ** of all… See the full description on the dataset page: https://huggingface.co/datasets/Aananda-giri/nepali_llm_datasets.KJV-LLM-Datasetsllm-japanese-dataset
llm-japanese-dataset
LLM構築用の日本語インストラクション(チャット)データセット
主に，英語で構築されたLLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます．
※様々な公開言語資源を利用させていただきました．関係各位にはこの場を借りて御礼申し上げます．
updates
2023/5/15にAlpaca datasetがNCにライセンス変更されたことに対応し，安心してご利用いただけるように，データセットから当該データセットをドロップしました．
v1.0.1にて，ドロップ後のデータセットをご利用いただけます．
2024/1/4にWikipedia summaryに空白文字のみで構成される出力を削除することに対応し，Wikipediaのバージョンアップデート(20240101)をしました(v1.0.2)．
2024/1/18にAsian Language Treebank (ALT)データセットの欠損した出力を削除しました(v1.0.3)．… See the full description on the dataset page: https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset.Grounded_3D_LLM_with_Referent_Tokens_Dataset
Grounded 3D-LLM Dataset
For detailed information and resources, please visit the following links:
Paper
Arxiv
Project Website
Dataset Access
Code
We are in the process of releasing our data incrementally:
Processed ScanNet200 PCD(~7G):
Each .npyfile represents a N*12 array with the following structure:
coordinates, color, normals, segments, labels = (
points[:, :3],
points[:, 3:6],
points[:, 6:9],
points[:, 9]… See the full description on the dataset page: https://huggingface.co/datasets/ShuaiYang03/Grounded_3D_LLM_with_Referent_Tokens_Dataset.nepali_dataset_llmBitext-retail-banking-llm-chatbot-training-dataset
Bitext - Retail Banking Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail Banking] sector can be easily achieved using our two-step approach to LLM Fine-Tuning.… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset.llm_plan_gen_dataset_accu_t2_t4
[!IMPORTANT]
This is the training dataset for the ICAPS 2025 paper "Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation".
from pathlib import Path
import os
import jsonlines
from copy import deepcopy
from datasets import load_dataset
from icecream import ic
import enum
from enum import IntEnum
from enum import auto
class CONFIG_TYPES(enum.Enum):
# "type_id": ['t0', 'accu-t1', 'accu-t2', 'accu-t3', 'accu-t4', 'accu-t4', 'accu-t1+t4', 'accu-t2+t4']… See the full description on the dataset page: https://huggingface.co/datasets/huangsukai/llm_plan_gen_dataset_accu_t2_t4.llm-japanese-dataset-vanilla
llm-japanese-dataset-vanilla
LLM構築用の日本語チャットデータセット
izumi-lab/llm-japanese-dataset から，日英翻訳のデータセット等を抜いたものです．
主に，日本語LLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます．
※様々な公開言語資源を利用させていただきました．関係各位にはこの場を借りて御礼申し上げます．
データの詳細
データの詳細は，izumi-lab/llm-japanese-dataset に関する，以下の論文を参照してください．
日本語: https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383
英語: https://arxiv.org/abs/2305.12720
GitHub: https://github.com/masanorihirano/llm-japanese-dataset
最新情報: llm.msuzuki.me.… See the full description on the dataset page: https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset-vanilla.ft-llm-2026-qa-dataset
FT-LLM 2026 QA Dataset
A Japanese visual-question-answering dataset used for Stage 1-2 visual instruction tuning of the COMPASS Vision-Language Model. Each sample contains a document or natural image together with one or more Japanese question–answer pairs, and is designed to give the VLM its instruction-following and VQA capabilities. Images are embedded in the dataset, so no external downloads are required.
Part of the Compass collection.
License
Released under the… See the full description on the dataset page: https://huggingface.co/datasets/Yana/ft-llm-2026-qa-dataset.jailbreak-detection-dataset
Jailbreak Detection Dataset (MLCommons-Aligned)
A comprehensive dataset for training AI safety classifiers, aligned with the MLCommons AI Safety taxonomy.
Dataset Description
This dataset combines multiple sources for robust jailbreak and safety detection:
Primary Sources
nvidia/Aegis-AI-Content-Safety-Dataset-2.0: 18,164 samples with MLCommons-aligned labels
lmsys/toxic-chat: Toxic content detection
jackhhao/jailbreak-classification: Jailbreak attack patterns… See the full description on the dataset page: https://huggingface.co/datasets/llm-semantic-router/jailbreak-detection-dataset.Bitext-telco-llm-chatbot-training-dataset
Bitext - Telco Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [telco] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset.Bitext-insurance-llm-chatbot-training-dataset
Bitext - Insurance Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset.llm_plan_gen_dataset_accu_t1_t4
[!IMPORTANT]
This is the training dataset for the ICAPS 2025 paper "Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation".
from pathlib import Path
import os
import jsonlines
from copy import deepcopy
from datasets import load_dataset
from icecream import ic
import enum
from enum import IntEnum
from enum import auto
class CONFIG_TYPES(enum.Enum):
# "type_id": ['t0', 'accu-t1', 'accu-t2', 'accu-t3', 'accu-t4', 'accu-t4', 'accu-t1+t4', 'accu-t2+t4']… See the full description on the dataset page: https://huggingface.co/datasets/huangsukai/llm_plan_gen_dataset_accu_t1_t4.fact-check-classification-dataset
Fact-Check Classification Dataset
🎯 Purpose: Binary classification dataset for determining whether a prompt needs external fact-checking.
Dataset Description
This dataset is designed to train classifiers that can route LLM requests based on whether they require external fact verification. It's part of the vLLM Semantic Router project.
Labels
FACT_CHECK_NEEDED (1): Information-seeking questions requiring external verification
Factual questions about dates… See the full description on the dataset page: https://huggingface.co/datasets/llm-semantic-router/fact-check-classification-dataset.AMALIA-VL-SFT-Dataset
AMALIA-VL-Training-Dataset
Dataset Description
This dataset is provided as part of the AMALIA project.
This is the vision+language training mix for AMALIA-VL-SFT. Each subset is one
source dataset in the mix, each with a single train split. The only datasets that are absent from this mix are those that derive directly from the core LLM training mix, and can be found in the AMALIA-LLM Post Training Collection.
Example usage:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/AMALIA-VL-SFT-Dataset.turkish-llm-dataset
Turkish Pretraining Corpus
Dataset Description
This dataset is a Turkish pretraining corpus created by combining BellaTurca (excluding ForumSohbetleri), Cosmos-Turkish-Corpus-v1.0, and FineWeb-2 Turkish Categorized, followed by cleaning, normalization, and deduplication. It is intended for the development, training, and evaluation of Turkish language models.
This dataset was prepared as part of a capstone project conducted by a group of students from Sabancı… See the full description on the dataset page: https://huggingface.co/datasets/VoidOaz/turkish-llm-dataset.