qus
Datasets
All datasets matching “qus”OmniSum
OmniSum
OmniSum is an English text dataset for code summarization and code-understanding research. Each example pairs a source-code snippet with a reference summary and several intermediate reasoning, refinement, evaluation, and final-summary fields. The dataset is intended for training and evaluating models that generate concise, accurate descriptions of code behavior.
Dataset Description
OmniSum is organized around function-level source-code examples. A typical… See the full description on the dataset page: https://huggingface.co/datasets/qustfmy/OmniSum.qus-user-story-quality-refined
Refined QUS User Story Quality Dataset
Dataset Summary
This repository contains a refined and relabeled adaptation of the user-story dataset reported by Sharma and Tripathi (2025) for the evaluation of user story quality according to the Quality User Story (QUS) framework.
The original corpus contains 960 criterion-specific user-story instances organized around the eight individual quality criteria of QUS. During the preparation of the experiments reported in our… See the full description on the dataset page: https://huggingface.co/datasets/devleoespinosa/qus-user-story-quality-refined.adapta-migrant-onboarding
AdaptaAI: Multilingual RAG Benchmark for Labor Migration Onboarding
Dataset Summary
This dataset is a multilingual question-answering benchmark designed to evaluate Retrieval-Augmented Generation (RAG) systems in the domain of Indian labor migration to Russia. It covers the full onboarding lifecycle: visa and work permit procedures, housing, salary and contracts, SIM card registration, money transfers, healthcare, language requirements, and worker rights and… See the full description on the dataset page: https://huggingface.co/datasets/Qusto/adapta-migrant-onboarding.Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).QG_QusestionsData