This dataset is based on the original PROMISE dataset [1] and is part of the research conducted for the paper titled "NICE: Non-Functional Requirements Identification, Classification, and Explanation Using Small Language Models," accepted at the International Conference on Software Engineering (ICSE'25). The original PROMISE dataset was introduced in the RE'17 data challenge by Cleland-Huang et al. [2]. It was further augmented by Dalpaiz et al. [3, 4] through the relabeling of Functional Requirements (FRs) and Non-Functional Requirements (NFRs) in a multi-label manner. In our work, we further relabeled the NFR sub-classes (e.g., Availability, Security, Performance) within the NFRs using the multi-label classification schema detailed in our paper. Through this repository, we present the updated, multi-label NFR version of the augmented PROMISE dataset. We removed three duplicate entries from the augmented PROMISE dataset. Specifically, we removed the following sentences from the dataset: "For each class within a sequence for a cohort, Program Administrators and Nursing Staff Members shall be able to specify which quarter that class will be offered." "Only registered customers can purchase streaming movies." "The response time shall be fast enough to maintain the flow of the game. The response time shall be no more than 2 seconds for 95% of responses and no more than 4% for the remaining responses." After deduplication, the NFRs in the augmented PROMISE dataset were labeled using a multi-label classification scheme. The dataset consists of 16 columns, with the requirement classes labeled using binary labels (0 or 1). ProjectID RequirementText IsFunctional IsQuality Availability (A) Fault Tolerance (FT) Legal (L) Look & Feel (LF) Maintainability (MN) Operability (O) Performance (PE) Portability (PO) Scalability (SC) Security (SE) Usability (US) Other (OT) For the paper pre-print and the dataset license file, please visit >https://zenodo.org/records/14709254 References [1] Original PROMISE dataset - https://zenodo.org/records/268542 [2] J. Cleland-Huang, R. Settimi, X. Zou, and P. Solc. “Automated classification of non-functional requirements”. In: Requirements engineering 12 (2007), pp. 103–120. [3] Dalpiaz, F., Dell'Anna, D., Aydemir, F.B. and Çevikol, S., 2019, September. Requirements classification with interpretable machine learning and dependency parsing. In 2019 IEEE 27th International Requirements Engineering Conference (RE) (pp. 142-152). IEEE. [4] Augmented PROMISE dataset - https://zenodo.org/records/3309669