datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HRM-Text-data-io-cleaned-20260515Pre-built HRM-Text pretraining dataset from raw data using the data_io cleaning scripts.
Citation
If you find this project or our paper useful, please consider citing our paper:
@misc{wang2026hrmtextefficientpretrainingscaling,
title={HRM-Text: Efficient Pretraining Beyond Scaling},
author={Guan Wang and Changling Liu and Chenyu Wang and Cai Zhou and Yuhao Sun and Yifei Wu and Shuai Zhen and Luca Scimeca and Yasin Abbasi Yadkori},
year={2026}… See the full description on the dataset page: https://huggingface.co/datasets/sapientinc/HRM-Text-data-io-cleaned-20260515.dromedario-3-sft-dataset
🐪 Dataset Card for Dromedario 3
📋 Dataset Summary
Dromedario 3 is a large-scale Italian instruction-tuning dataset derived from the English Tülu 3 SFT mixture through a principled translation pipeline: instructions and responses are classified according to the Natural Instructions taxonomy, classes are manually reviewed for translation safety, and items in validated classes are machine-translated into Italian. The full procedure is described in… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/dromedario-3-sft-dataset.mmlu_italian
MMLU - Italian (IT)
This dataset is an Italian translation of Massive Multitask Language Understanding (MMLU). MMLU is a dataset that is composed of multiple-choice questions from 57 different topics, including math, science, and social studies. The dataset is designed to evaluate the ability of models to answer questions across a wide range of topics.
Dataset Details
The dataset consists of multiple-choice questions from 57 different topics. Each question is associated… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/mmlu_italian.arc_italian
ARC - Italian (IT)
This dataset is an Italian translation of the AI2 Reasoning Challenge (ARC). ARC is a question-answering dataset that requires an understanding of natural language text and reasoning capabilities to answer questions correctly.
Dataset Details
The dataset consists of multiple-choice questions, where each question is associated with a set of answer choices (up to 5 choices). The task is to choose the correct answer choice based on the context provided in… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/arc_italian.boolq_italian
BoolQ - Italian (IT)
This dataset is an Italian translation of BoolQ. BoolQ is a question-answering dataset composed of user queries issued to a search engine.
Dataset Details
The task is to predict whether the answer to the question is true or false based on the context provided in the question. A text snippet from Wikipedia is provided as the context for each question.
The dataset includes the following splits:
Train: 9,427 rows
Validation: 3,270 rows… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/boolq_italian.gsm8k_italian
GSM8K - Italian (IT)
This dataset is an Italian translation of GSM8K. GSM8K stands for Grade School Math 8K, a dataset for math word problems, which should be easy to solve for people with an elementary school education.
Dataset Details
The dataset consists of math word problems, where each problem is associated with a possible explanation of how to solve it. The task is to generate the answer to the math problem. The dataset is split into a training set and a test set.… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/gsm8k_italian.ea-mt-benchmark
Dataset Card for EA-MT
EA-MT (Entity-Aware Machine Translation) is a multilingual benchmark for evaluating the capabilities of Large Language Models (LLMs) and Machine Translation (MT) models in translating simple sentences with potentially challenging entity mentions, e.g., entities for which a word-for-word translation may not be accurate.
Here is an example of a simple sentence with a challenging entity mention:
English: "What is the plot of The Catcher in the Rye?"
Italian:… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/ea-mt-benchmark.hellaswag_italian
HellaSwag - Italian (IT)
This dataset is an Italian translation of HellaSwag. HellaSwag is a large-scale commonsense reasoning dataset, which requires reading comprehension and commonsense reasoning to predict the correct ending of a sentence.
Dataset Details
The dataset consists of instances containing a context and a multiple-choice question with four possible answers. The task is to predict the correct ending of the sentence. The dataset is split into a training set… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/hellaswag_italian.hw-mnlp-2026
Dataset for Multilingual Natural Language Processing (MNLP) Homeworks
This dataset serves for both Homework 1 and Homework 2 of the Multilingual Natural Language Processing (MNLP) course.
Homework 1 - Semantic Search
In the first homework, you are asked to build semantic search systems. You must only use the following variables:
query: A single question in natural language.
query_id: The question (query) identifier.
candidate_chunks: List of candidate answers (only one… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp-course-materials/hw-mnlp-2026.piqa_italian
PIQA - Italian (IT)
This dataset is an Italian translation of PIQA. PIQA stands for Physical Interaction Question Answering, a dataset of questions about common scenarios that require an understanding of the physical world.
Dataset Details
The dataset consists of questions about common scenarios that require an understanding of the physical world. Each question is associated with a correct answer and a distractor. The task is to predict the correct answer to the… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/piqa_italian.truthful_qa_italian
TruthfulQA - Italian (IT)
This dataset is an Italian translation of TruthfulQA. TruthfulQA is a dataset for fact-based question answering, which contains questions that require factual knowledge to answer correctly. These questions are designed so that some humans would answer them incorrectly because of common misconceptions.
Dataset Details
The dataset is a question answering dataset that contains questions that require factual knowledge to answer correctly and avoid… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/truthful_qa_italian.winogrande_italian
Winogrande - Italian (IT)
This dataset is an Italian translation of Winogrande. Winogrande is a large-scale dataset for coreference resolution, commonsense reasoning, and world knowledge. It is based on the original Winograd Schema Challenge dataset.
Dataset Details
The dataset consists of almost 40K examples, each containing a sentence with a blank and two possible fill-in-the-blank options. The task is to choose the correct option that correctly fills in the blank based… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/winogrande_italian.sciq_italian
SciQ - Italian (IT)
This dataset is an Italian translation of SciQ. SciQ is a dataset for scientific questions, which were semi-automatically generated from an existing set of questions. The dataset is designed to test the ability of models to answer questions that require scientific knowledge.
Dataset Details
The dataset consists of science-related questions, where each question is associated with a correct answer and three possible distractors. The task is to predict… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/sciq_italian.simple_bench
📊 Simple Bench Dataset
A Compact Benchmark for Structured Reasoning and Multiple-Choice Evaluation in Large Language Models
Simple Bench Dataset is a structured evaluation collection derived from the Simple Bench benchmark, designed to assess reasoning, comprehension, and multiple-choice question-answering capabilities of large language models through concise yet non-trivial problems that require logical inference rather than simple retrieval; each sample consists of a natural… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/simple_bench.