datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
truthful_qa_binaryTruthfulQA-Binary is a benchmark to measure whether a language model is truthful in
generating answers to questions. The benchmark comprises 817 questions that
span 38 categories, including health, law, finance and politics. Questions are
crafted so that some humans would answer falsely due to a false belief or
misconception. To perform well, models must avoid generating false answers
learned from imitating human texts.gdelt-forecast-binary
GDELT-Forecast Binary
1,215 yes/no forecasting questions generated from clusters of news articles in the GDELT 2.0 corpus (Aug 2025 – Apr 2026). Each question is paired with the original seed-event articles, top-5 retrieved evidence articles dated strictly before the question creation date, and a verified ground-truth answer.
Intended use
Training and evaluating LLM-based forecasting models in a strict forecasting posture — the model sees only news that was publicly… See the full description on the dataset page: https://huggingface.co/datasets/rajatagarwal457/gdelt-forecast-binary.my-smol-ds
Dataset Card for my-smol-ds
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/Binaryy/my-smol-ds/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/Binaryy/my-smol-ds.