Nemotron-SFT-Safety-v2

Dataset Description: The Nemotron-SFT-Safety-v2 data is designed to align models to be robust against a variety of safety and security concerns that may arise in unaligned large language models. This dataset is a collection of: 1. A hybrid (open-source and synthetically generated) collection of prompts designed to elicit different model vulnerabilities, and 2. Synthetically generated responses designed to steer model behavior towards safety-aligned values and enhance model robustness across a broad variety of potential attack vectors. 3. Multilingual translations of the English subset. This dataset is ready for commercial or non-commercial uses. Note that the collection of English prompts and responses in this dataset is the same as Nemotron-SFT-Safety-v1 dataset. Multilingual subsets are new. The multilingual subset: We translated the English `en` subset into six languages—German `de`, Spanish `es`, French `fr`, Japanese `ja`, Italian `it`, and Chinese `zh` using NeMo Skills using a sentence-by-sentence chunked translation pipeline with NVIDIA Riva Translate 4B Instruct v1.1. To improve translation quality, each translated example was back-translated into English and compared against the original English prompt-response pair. Examples with semantic similarity below 0.8 were filtered out, removing approximately 10–15% of examples per translated language. We also manually spot-checked the highest and lowest-scoring translations to verify that the filter was removing examples with clear translation failures or structural issues. After filtering, we used stratified sampling to retain approximately 15,000 examples per translated language. The final dataset contains about 135,000 examples: ~45,000 in English and ~15,000 each in `de`, `es`, `fr`, `ja`, `it`, and `zh`. A refresher on the English subset's consituents: Collected datasets: Nemotron Content Safety Dataset v2: Comprehensive coverage across a variety of content safety risks like self-harm, violence, criminal pla