datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sapient-synth-tasksource-reclor
sapient-synth-tasksource-reclor
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 4633
Task: synthetic anonymous instruction replacement
Generation
Rows were generated with google/gemma-4-31B-it and… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-tasksource-reclor.sapient-synth-platypus-reclor
sapient-synth-platypus-reclor
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 5131
Task: synthetic anonymous instruction replacement
Generation
Rows were generated with google/gemma-4-31B-it and… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-platypus-reclor.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.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.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.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.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.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.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.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.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.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.sapientinc-sudoku-extreme-timvink-sudoku-solver
Dataset Card for Dataset Name
This dataset is built on top of sapientinc/sudoku-extreme by preprocessing every example with timvink/sudoku-solver.
The additional fields are:
trajectory: the step-by-step sudoku board configuration from the question to the solution, acquired by running puzzle.solve_step() until complete
num_steps: number of calls to puzzle.solve_step() required to obtain solution
strategies_used: set of strategies used by the solver
nounatlas_srl_corpus
NounAtlas SRL Corpus
This dataset is part of the NounAtlas project, aiming to enhance Nominal Semantic Role Labeling (SRL) by providing a comprehensive inventory of nominal predicates organized into semantically-coherent frames.
Dataset Details
The NounAtlas SRL Corpus contains sentences annotated with nominal predicates and their corresponding semantic roles. This dataset is split into three subsets: training, development, and test.
Train: 22,452 sentences
Dev: 2,806… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/nounatlas_srl_corpus.sapientblock-blockchain-use-cases
SapientBlock Blockchain Use Cases
Der Datensatz enthält 255 redaktionell geprüfte Blockchain-Use-Cases aus 74 Branchen. Er stellt die öffentlich zugänglichen SapientBlock-Inhalte in einem maschinenlesbaren JSONL-Format für Forschung, Bildung, Retrieval und Quellenanalyse bereit.
SapientBlock ist ein öffentliches Forschungs- und Bildungsprojekt von ShapeNeural. Die Inhalte sind keine Rechts-, Investitions-, Unternehmens- oder technische Beratung.
Inhalt
Jeder… See the full description on the dataset page: https://huggingface.co/datasets/TooKeen/sapientblock-blockchain-use-cases.global_mmlu_lite_pt
🌎 Global-MMLU Lite (Portuguese)
A Focused Benchmark for Portuguese-Language Reasoning in Large Language Models
Global-MMLU Lite (Portuguese) is a curated subset of the Global-MMLU Lite benchmark designed to evaluate the reasoning, knowledge, and multiple-choice question-answering capabilities of large language models in Portuguese, providing a diverse and computationally efficient collection of translated and adapted QA samples across domains such as general knowledge, science… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/global_mmlu_lite_pt.enem_2025
🇧🇷 ENEM 2025 — Brazilian National High School Exam Dataset
A High-Quality Benchmark for Portuguese Academic Reasoning in Large Language Models
ENEM 2025 Dataset is a curated collection of question-answer pairs derived from the 2025 edition of the Brazilian National High School Exam (ENEM), designed to evaluate and improve the reasoning, reading comprehension, and multiple-choice answering capabilities of large language models in Brazilian Portuguese; as one of the largest… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/enem_2025.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.sapient-synth-flan-niv2-fsopt-data-task902-deceptive-opinion-spam-classification
sapient-synth-flan-niv2-fsopt-data-task902-deceptive-opinion-spam-classification
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 2935
Task: synthetic anonymous instruction replacement
Generation… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task902-deceptive-opinion-spam-classification.sapient-synth-flan-niv2-fsopt-data-task635-allegro-reviews-answer-generation
sapient-synth-flan-niv2-fsopt-data-task635-allegro-reviews-answer-generation
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 3186
Task: synthetic anonymous instruction replacement
Generation
Rows… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task635-allegro-reviews-answer-generation.global_mmlu_lite
🌍 Global-MMLU Lite Dataset
A Lightweight Benchmark for Multi-Domain Reasoning in Large Language Models
Global-MMLU Lite is a curated and efficient subset of the Global Massive Multitask Language Understanding (MMLU) benchmark, designed to evaluate and fine-tune large language models across a wide range of academic and professional domains through high-quality multiple-choice question answering; preserving the diversity and rigor of the original benchmark while significantly… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/global_mmlu_lite.sapient-synth-flan-niv2-fsopt-data-task1377-newscomm-translation
sapient-synth-flan-niv2-fsopt-data-task1377-newscomm-translation
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 3491
Task: synthetic anonymous instruction replacement
Generation
Rows were… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1377-newscomm-translation.global_mmlu_lite_en
🌍 Global-MMLU Lite (English Only)
A Focused Benchmark for English-Language Reasoning in Large Language Models
Global-MMLU Lite (English Only) is a curated subset of the Global-MMLU Lite benchmark specifically designed to evaluate the reasoning, knowledge, and multiple-choice question-answering capabilities of large language models within the English language, providing a diverse yet computationally efficient collection of structured QA samples spanning domains such as science… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/global_mmlu_lite_en.sapient-synth-tasksource-pragmeval-sarcasm
sapient-synth-tasksource-pragmeval-sarcasm
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 3753
Task: synthetic anonymous instruction replacement
Generation
Rows were generated with… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-tasksource-pragmeval-sarcasm.sapient-synth-flan-niv2-fsopt-data-task903-deceptive-opinion-spam-classification
sapient-synth-flan-niv2-fsopt-data-task903-deceptive-opinion-spam-classification
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 1703
Task: synthetic anonymous instruction replacement
Generation… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task903-deceptive-opinion-spam-classification.math_precision_benchmarking
🧮 Math Precision — Benchmarking
A Formal Framework for High-Precision Arithmetic Evaluation in Large Language Models
Math Precision — Benchmarking, developed by Sapiens Technology®, is a rigorous framework for evaluating the true arithmetic capabilities of large language models by generating fully stochastic, high-precision mathematical problems that eliminate memorization and heuristic guessing; operating in a 100-digit floating-point field, it forces extreme numerical precision… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/math_precision_benchmarking.sapient-synth-flan-dialog-fsopt-data-qrecc
sapient-synth-flan-dialog-fsopt-data-qrecc
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 20748
Task: synthetic anonymous instruction replacement
Generation
Rows were generated with… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-dialog-fsopt-data-qrecc.sapient-synth-flan-flan-zsopt-data-opinion-abstracts-idebate
sapient-synth-flan-flan-zsopt-data-opinion-abstracts-idebate
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 1307
Task: synthetic anonymous instruction replacement
Generation
Rows were generated… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-zsopt-data-opinion-abstracts-idebate.sapient-synth-flan-niv2-zsopt-data-task1375-newscomm-translation
sapient-synth-flan-niv2-zsopt-data-task1375-newscomm-translation
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 2180
Task: synthetic anonymous instruction replacement
Generation
Rows were… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1375-newscomm-translation.sapient-synth-flan-niv2-zsopt-data-task1371-newscomm-translation
sapient-synth-flan-niv2-zsopt-data-task1371-newscomm-translation
Chat-template-ready synthetic anonymous replacement examples for one Sapient source excluded from the DFM5 data mix.
Contents
Format: gzip-compressed JSON Lines under data/train.jsonl.gz
Schema: {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Files: 1
Rows: 1577
Task: synthetic anonymous instruction replacement
Generation
Rows were… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1371-newscomm-translation.