datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ITBench-AA
ITBench-AA
Artificial Analysis' release of the public scenarios from
IBM's ITBench benchmark, used for
the ITBench-AA leaderboard.
This repo currently contains the SRE subset (sre config). Each row is a
Kubernetes incident scenario with its expected contributing-factor entities. An
agent under evaluation is given access to an offline snapshot of the affected
cluster (alerts, events, traces, topology) and must identify the entity
(Deployment, Pod, ConfigMap, etc.) responsible for… See the full description on the dataset page: https://huggingface.co/datasets/ArtificialAnalysis/ITBench-AA.HawkEye-IT
Download Video
Please download the original videos from the provided links:
VideoChat: Based on InternVid, we created additional instruction data and used GPT-4 to condense the existing data.
VideoChatGPT: The original caption data was converted into conversation data based on the same VideoIDs.
Kinetics-710 & SthSthV2: Option candidates were generated from UMTtop-20 predictions.
NExTQA: Typos in the original sentences were corrected.
CLEVRER: For single-option multiple-choice QAs… See the full description on the dataset page: https://huggingface.co/datasets/wangyueqian/HawkEye-IT.FinSearchCompThis repository contains the FinSearchComp dataset, a benchmark for evaluating financial search and reasoning capabilities of LLM-based agents, as presented in the paper FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning.
Project Page: https://randomtutu.github.io/FinSearchComp/
FinSearchComp is the first fully open-source agent benchmark designed for realistic, open-domain financial search and reasoning. It comprises three tasks that closely… See the full description on the dataset page: https://huggingface.co/datasets/itsakhilyou/FinSearchComp.Inst-It-Dataset
Inst-IT Dataset: An Instruction Tuning Dataset with Multi-level Fine-Grained Annotations
introduced in the paper Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning
🌐 Homepage | Code | 🤗 Paper | 📖 arXiv
Inst-IT Dataset Overview
We create a large-scale instruction tuning dataset, the Inst-it Dataset. To the best of our knowledge, this is the first dataset that provides fine-grained annotations centric on specific… See the full description on the dataset page: https://huggingface.co/datasets/Inst-IT/Inst-It-Dataset.italic-softkd-pool
italic-softkd-pool
The exact training data of idealab-cs2/zagreus-0.4B-italic-softkd: 21,606 Italian multiple-choice questions with committee soft labels. One soft-KD training run from mii-llm/zagreus-0.4B-ita on the train split reaches 0.4787 on the full ITALIC 10K (official harness, 5-shot fast, temperature 0), from a 0.2802 base.
train is the full pool; the other three splits partition it by provenance:
split
rows
contents
train
21,606
the full training file (union… See the full description on the dataset page: https://huggingface.co/datasets/idealab-cs2/italic-softkd-pool.alpaca-cleaned-italian
Dataset Card for Alpaca-Cleaned-Italian
About the translation and the original data
The translation was done with X-ALMA, a 13-billion-parameter model that surpasses state-of-the-art open-source multilingual LLMs (as of Q1 2025, paper here).
The original alpaca-cleaned dataset is also kept here so that there is parallel data for Italian and English.
Additional notes on the translation
Despite the good quality of the translation, errors, though rare, are… See the full description on the dataset page: https://huggingface.co/datasets/DanielSc4/alpaca-cleaned-italian.turkish_medical_reasoning
Türkçe Medikal Reasoning Veri Seti
Bu veri seti FreedomIntelligence/medical-o1-verifiable-problem veri setinin Türkçeye çevirilmiş bir alt kümesidir.
Çevirdiğimiz veri seti 7,208 satır içermektedir. Veri setinde bulunan sütunlar aşağıda açıklanmıştır:
question: Medikal soruların bulunduğu sütun.
answer_content: DeepSeek-R1 modeli tarafından oluşturulmuş İngilizce yanıtların Türkçeye çevrilmiş hali.*
reasoning_content: DeepSeek-R1 modeli tarafından oluşturulmuş İngilizce akıl… See the full description on the dataset page: https://huggingface.co/datasets/ituperceptron/turkish_medical_reasoning.IT_Support_V2
Mack: IT Support & Admin Dataset
📋 Dataset Description
This dataset consists of 100,000+ conversation logs focused on IT Support and IT Administration tasks. It was generated to fine-tune the "Mack" model—an AI persona designed to act as an expert Tier 1 & Tier 2 IT Helpdesk agent.
The data covers a wide range of technical domains, including Windows troubleshooting, SQL Server administration, driver issues, network diagnostics, and hardware debugging.
Curated by: [Dev… See the full description on the dataset page: https://huggingface.co/datasets/benjaminmacklin/IT_Support_V2.VideoChatOnline-IT
Overview
This dataset provides a comprehensive collection for Online Spatial-Temporal Understanding tasks, covering multiple domains including Dense Video Captioning, Video Grounding, Step Localization, Spatial-Temporal Action Localization, and Object Tracking.
Data Formation
Our pipeline begins with 96K high-quality samples curated from 5 tasks across 12 datasets. The conversion process enhances online spatiotemporal understanding through template transformation. We… See the full description on the dataset page: https://huggingface.co/datasets/MCG-NJU/VideoChatOnline-IT.squad-it
Squad-it
This dataset is an adapted version of that squad-it to train on HuggingFace models.
It contains:
train samples: 87599
test samples : 10570
This dataset is for question answering and his format is the following:
[
{
"answers": [
{
"answer_start": [1],
"text": ["Questo è un testo"]
},
],
"context": "Questo è un testo relativo al contesto.",
"id": "1",
"question": "Questo è un testo?",
"title": "train test"
}
]
It can… See the full description on the dataset page: https://huggingface.co/datasets/z-uo/squad-it.lumos_unified_ground_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_unified_ground_iterative.X-TruthfulQA_en_zh_ko_it_es
X-TruthfulQA
🤗 Paper | 📖 arXiv
Dataset Description
X-TruthfulQA is an evaluation benchmark for multilingual large language models (LLMs), including questions and answers in 5 languages (English, Chinese, Korean, Italian and Spanish).
It is intended to evaluate the truthfulness of LLMs. The dataset is translated by GPT-4 from the original English-version TruthfulQA.
In our paper, we evaluate LLMs in a zero-shot generative setting: prompt the instruction-tuned LLM with… See the full description on the dataset page: https://huggingface.co/datasets/zhihz0535/X-TruthfulQA_en_zh_ko_it_es.itis-taxonomy-instruct-30k-v2-negatives
ITIS Taxonomy Instruction Dataset with Negative Samples
Overview
The ITIS Taxonomy Instruction Dataset with Negative Samples is a structured instruction-response dataset derived from the public domain Integrated Taxonomic Information System (ITIS) database.
It was designed for fine-tuning large language models on taxonomy-oriented tasks such as rank identification, lineage reconstruction, parent taxon retrieval, taxonomic validity checks, and common name mapping.… See the full description on the dataset page: https://huggingface.co/datasets/Jaymerry/itis-taxonomy-instruct-30k-v2-negatives.lumos_complex_qa_plan_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_complex_qa_plan_iterative.TOFU-dalumos_unified_plan_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_unified_plan_iterative.BLiMP-IT
BLiMP-IT
Dataset Summary
BLiMP-IT is a linguistically motivated benchmark for evaluating Italian language models through minimal pairs.
Each example consists of a grammatical sentence paired with a minimally different ungrammatical counterpart that isolates a single morphosyntactic contrast.
The benchmark is designed to evaluate whether language models assign higher probability to the grammatical sentence than to the ungrammatical one.
The benchmark is inspired by… See the full description on the dataset page: https://huggingface.co/datasets/NeTSlab/BLiMP-IT.X-SVAMP_en_zh_ko_it_es
X-SVAMP
🤗 Paper | 📖 arXiv
Dataset Description
X-SVAMP is an evaluation benchmark for multilingual large language models (LLMs), including questions and answers in 5 languages (English, Chinese, Korean, Italian and Spanish).
It is intended to evaluate the math reasoning abilities of LLMs. The dataset is translated by GPT-4-turbo from the original English-version SVAMP.
In our paper, we evaluate LLMs in a zero-shot generative setting: prompt the instruction-tuned LLM with… See the full description on the dataset page: https://huggingface.co/datasets/zhihz0535/X-SVAMP_en_zh_ko_it_es.italic-extkd-pool
italic-extkd-pool
The stage-3 training data of idealab-cs2/zagreus-0.4B-italic-extkd: 57,563 Italian multiple-choice questions from public, in-distribution datasets with teacher soft labels. One soft-KD stage from the stage-2 checkpoint on the agreement-filtered subset (28,561 items where the teacher agrees with the gold answer) reaches 0.4921 / 0.4929 / 0.4932 on the full ITALIC 10K (official harness, 5-shot fast, temperature 0, three independent runs). Full lineage: 0.2802… See the full description on the dataset page: https://huggingface.co/datasets/idealab-cs2/italic-extkd-pool.lumos_multimodal_ground_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_multimodal_ground_iterative.lumos_complex_qa_ground_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_complex_qa_ground_iterative.itis-taxonomy-instruct-30k
ITIS Taxonomy Instruction Dataset (30K)
This dataset has been superseded by the v2 release with negative and unknown samples.
Overview
The ITIS Taxonomy Instruction Dataset (30K) is a structured
instruction-response dataset derived from the public domain Integrated
Taxonomic Information System (ITIS) database.
It contains 30,000 instruction-output pairs designed for fine-tuning
large language models on taxonomic reasoning and biodiversity-related
tasks.
The… See the full description on the dataset page: https://huggingface.co/datasets/Jaymerry/itis-taxonomy-instruct-30k.lfqa-preprocessed-itTOFU-og-dait-support-l1-ticket-classification
IT Support L1 Multilingual Dataset
Dataset Summary
IT Support L1 Multilingual Dataset is a synthetic enterprise help desk dataset for ticket classification and troubleshooting response generation. It contains realistic Level 1 IT support scenarios in English and Czech, designed for experiments in structured classification, response generation, and multilingual support workflow prototyping.
This dataset contains synthetic IT Support L1 scenarios. The records were generated… See the full description on the dataset page: https://huggingface.co/datasets/w1z4rd3k/it-support-l1-ticket-classification.lumos_multimodal_plan_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_multimodal_plan_iterative.CodeX-Thinking-Gemma-4-31B-ITAll prompts were taken from Modotte/CodeX-2M-Thinking, which contains multiple traces per prompt whereas this dataset only provides one trace per prompt. Generations were with https://huggingface.co/nvidia/Gemma-4-31B-IT-NVFP4 (a mix of BF16/FP8 weights that NVIDIA configured with FP8 KV cache; benchmarks show performs similarly to BF16 for coding). No system prompt was used.
italian-sft-curated
Italian SFT — Curated Subset
A high-quality Italian instruction-following dataset, derived from
DeepMount00/OpenItalianData
via a filter cascade designed to remove machine-translation artifacts,
non-Italian content, low-quality pairs, and near-duplicates.
This dataset is part of the llm-lab course (repo),
module 03a — Curating SFT Data. The full filter pipeline that produced it
lives at part-1-data/03a-curating-sft-data/; see the module README for the
methodology in detail.… See the full description on the dataset page: https://huggingface.co/datasets/antoniogr7/italian-sft-curated.stambecco_data_it
🌁 Stambecco-Cleaned: Italian Instruction-Tuning Dataset
The Stambecco-Cleaned Dataset is an Italian translation and adaptation of the community-curated Alpaca-Cleaned dataset, created to enable and evaluate instruction-following capabilities in Italian Large Language Models (LLMs).
📌 Dataset Summary
Language: Italian (it)
Base Source: Alpaca-Cleaned (curated version of Stanford Alpaca)
Primary Use Case: Instruction fine-tuning, evaluation, and alignment for… See the full description on the dataset page: https://huggingface.co/datasets/mchl-labs/stambecco_data_it.arc_challenge_ita
Italian version of the Arc Challenge dataset (ARC-c)
The dataset has been automatically translate by using Argos Translate v. 1.9.1
Citation Information
@misc{basile2023llamantino,
title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language},
author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
year={2023},
eprint={2312.09993}… See the full description on the dataset page: https://huggingface.co/datasets/swap-uniba/arc_challenge_ita.