FinHeadlineMix

FinHeadlineMix 500,000 unique synthetic financial news headlines**, generated with NVIDIA NeMo Data Designer and deduplicated with NeMo Curator. Dataset description FinHeadlineMix is a large-scale synthetic dataset of financial news headlines spanning 13 topic categories (12 domain topics plus a catch-all *Other* class). All headlines were generated using NVIDIA NeMo Data Designer, with NVIDIA's **Nemotron-3-Nano-30B-A3B** model configured as the generation model. It was produced by an iterative generation–curation pipeline that ran for 82 iterations on a single 8-way NVIDIA B200 node. Each iteration generated a batch of headlines, used NVIDIA NeMo Curator for semantic deduplication, selected maximally diverse few-shot examples via a *farthest-from-centroid* strategy, and corrected the category distribution before the next batch. The dataset is intended as a pre-training or fine-tuning corpus for a range of financial NLP tasks, including headline classification, headline generation, and representation learning. It supports the full spectrum of fine-tuning workflows, from parameter-efficient adaptation with LoRA to model compression and knowledge distillation, where large teacher models are compressed into smaller, production-ready student models. Data fields | Column | Type | Description | |--------|------|-------------| | `id` | `string` | UUID v4 — unique identifier for each headline | | `headline` | `string` | Synthetic financial news headline (5–38 words, avg ~15) | Generation methodology | Stage | Tool | Details | |-------|------|---------| | Generation | NeMo Data Designer + Nemotron-3-Nano-30B-A3B | 35K–50K headlines per batch; category-weighted sampling with per-category few-shot prompts | | Quality filter | Rule-based | Min 5 words, max 100 words, special-character ratio < 25% | | Deduplication | NeMo Curator | MiniLM-L6-v2 embeddings (384-dim), 500 K-means clusters, 90% cosine-similarity threshold — applied globally against the full accumulated corpus each