datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ViMU
ViMU: Benchmarking Video Metaphorical Understanding
Qi Li, Xinchao Wang*
*Corresponding author
xML Lab, National University of Singapore
Our GitHub repository contains the evaluation scripts for ViMU, a benchmark for video metaphorical understanding. The code evaluates multimodal models on four tasks:
Open-ended interpretation (OE)
Evidence grounding (EG)
Rhetoric mechanism identification (RM)
Social value signal identification (SV)
Directory Structure
Expected… See the full description on the dataset page: https://huggingface.co/datasets/LIQIIIII/ViMU.vimqa
VIMQA
VIMQA is a Vietnamese dataset for advanced reasoning and explainable multi-hop
question answering. Each question requires combining facts from two different
Vietnamese Wikipedia articles, and every example ships with sentence-level
supporting facts so a model's reasoning chain can be evaluated, not just its
final answer.
The schema follows the HotpotQA
convention, so tooling written for HotpotQA transfers with minimal changes.
Usage
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/nguyenlab/vimqa.ViMLQAVietnamese portion of MLQA for monolingual QA
The train split is the machine translated train data from SQuAD v1.1.
The validation split and the test split are from MLQA.
We created this particular version with the following code:
import datasets
# Machine translated train data from SQuAD v1.1.
mlqa_train_dev = datasets.load_dataset("facebook/mlqa", "mlqa-translate-train.vi")
# Original validation and test data from MLQA
mlqa_val_test = datasets.load_dataset("facebook/mlqa", "mlqa.vi.vi")
#… See the full description on the dataset page: https://huggingface.co/datasets/phucdev/ViMLQA.ViMedical_DiseaseThis dataset contains over 12K+ questions and symptoms related to various common diseases in Vietnamese. It's designed to aid in the classification of medical symptoms and provide preliminary disease identification. The dataset covers a wide range of diseases, including cardiovascular, digestive, neurological, dermatological, endocrine, and others.
For more information and updates about the dataset, please refer to the main repository here.
This dataset can be used for:
Data… See the full description on the dataset page: https://huggingface.co/datasets/PB3002/ViMedical_Disease.Vimuseum_QA
Vimuseum_QA: Tập dữ liệu Hỏi - Đáp về Bảo tàng Việt Nam
Vimuseum_QA là bộ dữ liệu Hỏi - Đáp (Question-Answering) bằng tiếng Việt, tập trung vào chủ đề lịch sử, văn hóa, hiện vật và thông tin tham quan của các bảo tàng tại Việt Nam. Bộ dữ liệu được thiết kế nhằm phục vụ cho việc huấn luyện các mô hình ngôn ngữ lớn (LLM Fine-tuning), xây dựng hệ thống trả lời tự động (Chatbot) hoặc ứng dụng tìm kiếm ngữ nghĩa (RAG) trong lĩnh vực du lịch và văn hóa bản địa.
📊 Tổng… See the full description on the dataset page: https://huggingface.co/datasets/Huyveo37/Vimuseum_QA.vimmrc2.0
ViMMRC 2.0
The Vietnamese Multiple-choice reading comprehension dataset version 2 (ViMMRC 2.0)
The dataset is freely available for research purposes only. Users need to sign the data agreement before receiving the dataset.
More information, please visit the NLP@UIT research group: https://nlp.uit.edu.vn/
The original Github for the dataset (including source code): https://github.com/sonlam1102/vimmrc2
Usage
from datasets import load_dataset
train =… See the full description on the dataset page: https://huggingface.co/datasets/uitnlp/vimmrc2.0.vimqa-generated-answers-pass1
Vi-MQA - Pass 1 Generated Answers & Evaluation
This repo contains the Pass 1 outputs and evaluation results for the Vi-MQA Dataset from the VMLU Benchmark Suite with a total of 4,762 records.
Folder Structure
1. Model Outputs (raw_outputs/)
Contains the formatted outputs from the 3 models evaluated in Pass 1:
results_pass1_gemma.jsonl (Gemma 4 31B IT)
results_pass1_llama.jsonl (Llama 4 Scout)
results_pass1_qwen.jsonl (Qwen3 32B)
2.… See the full description on the dataset page: https://huggingface.co/datasets/nygdon/vimqa-generated-answers-pass1.