datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MetaMedQA
MetaMedQA Dataset
Overview
MetaMedQA is an enhanced medical question-answering benchmark that builds upon the MedQA-USMLE dataset. It introduces uncertainty options and addresses issues with malformed or incorrect questions in the original dataset. Additionally, it incorporates questions from the Glianorex benchmark to assess models' ability to recognize the limits of their knowledge.
Key Features
Extended version of MedQA-USMLE
Incorporates uncertainty… See the full description on the dataset page: https://huggingface.co/datasets/maximegmd/MetaMedQA.glianorex
Multiple Choice Questions and Large Languages Models: A Case Study with Fictional Medical Data
This multiple choice question dataset on a fictional organ, the Glianorex, is used to assess the capabilities of models to answer questions on knowledge they have never encountered.
We only provide a test dataset as training models on this dataset would defeat the purpose of isolating linguistic capabilities from knowledge.
Motivation
We designed this dataset to evaluate the… See the full description on the dataset page: https://huggingface.co/datasets/maximegmd/glianorex.OncoAgent-Clinical-266K
🧬 OncoAgent Clinical Dataset — 266K
Curated Multi-Source Oncology Training Dataset
AMD Developer Hackathon 2026 · Used to fine-tune OncoAgent v1.0
Dataset Description
This dataset contains 266,854 clinical oncology training samples curated for fine-tuning large language models on cancer diagnosis, treatment recommendation, and clinical reasoning tasks.
Composition
Source
Samples
Description
PMC-Patients
~100,000
Real clinical case presentations… See the full description on the dataset page: https://huggingface.co/datasets/MaximoLopezChenlo/OncoAgent-Clinical-266K.MedQA-USMLE-4-options-clean
MedQA-USMLE-4-options-clean Dataset
Overview
MedQA-USMLE-4-options-clean is an enhanced medical question-answering benchmark that builds upon the MedQA-USMLE dataset. Physicians analyzed the 1373 questions in the original dataset and moved 52 questions that were either malformed or incomplete to another split incomplete.
Key Features
Relabeled malformed/incorrect questions
Dataset Details
Size: 1373
Language: English
Data Source… See the full description on the dataset page: https://huggingface.co/datasets/maximegmd/MedQA-USMLE-4-options-clean.the-hive-corpus
The Hive Corpus
Public, sanitized snapshot of The Hive Collective's knowledge base. Each entry is a specific, dev-targeted insight (Postgres gotchas, Next.js footguns, TypeScript edge cases, Stripe webhook bugs, agent-design tradeoffs, etc.) that passed a quality gate (specificity ≥ 0.50) at submission time.
Live API: https://api.thehivecollective.io
License: CC-BY-SA-4.0 — re-use freely, share derivatives under the same license, attribute "The Hive Collective".
Cadence:… See the full description on the dataset page: https://huggingface.co/datasets/Maximebouchard/the-hive-corpus.fracas
Dataset Card for FraCaS
Dataset Summary
This repository contains the French version of the FraCaS Test Suite introduced in this paper, as well as the original English one, in a TSV format (as opposed to the XML format provided with the original paper).
FraCaS stands for "Framework for Computational Semantics".
Supported Tasks and Leaderboards
This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment… See the full description on the dataset page: https://huggingface.co/datasets/maximoss/fracas.csqa-reasoning-dataset
Dataset Card for "commonsense_qa"
Dataset Summary
CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.… See the full description on the dataset page: https://huggingface.co/datasets/Maxime272003/csqa-reasoning-dataset.