金融 NER 是结构化非结构化数据的关键，做金融 NLP 的团队可以直接用这套方法低成本提升实体识别精度，值得一试。
该研究将 DeepSeek-R1-8B 与 LoRA 和 NEFTune 结合，用于金融命名实体识别。通过将 1693 条标注句子转换为指令-输入-输出三元组，并插入轻量 LoRA 矩阵及在训练时添加均匀噪声，模型在七类实体上达到 0.912 的 micro-F1，超越 Llama3-8B、Qwen3-8B 等基线。这证明低成本微调开源模型即可在专业领域取得优异效果。
Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune
Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs. However, general-purpose large language models (LLMs) often misclassify financial entities or ignore domain-specific patterns. This paper investigates the use of DeepSeek-R1-8B, a recent open-source large language model, combined with Low-Rank Adaptation (LoRA) and Noisy Embedding Fine-Tuning (NEFTune) for financial NER. Each annotated sentence in our corpus of 1693 samples is converted into an instruction-input-output triple. We insert lightweight LoRA matrices into the Transformer layers and apply NEFTune to improve generalisation by adding uniform noise to embedding vectors during training. Experiments show that the LoRA-adapted DeepSeek-R1-8B achieves a micro-F1 of 0.901 on seven entity types (Company, Date, Location, Money, Person, Product and Quantity), and adding NEFTune further boosts the micro-F1 to 0.912, outperforming Llama3-8B, Qwen3-8B, Baichuan2-7B, T5 and BERT-Base baselines.