Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune
Researchers have fine-tuned the DeepSeek-R1-8B language model for financial named-entity recognition (NER) tasks. By employing Low-Rank Adaptation (LoRA) and Noisy Embedding Fine-Tuning (NEFTune), the adapted model achieved a micro-F1 score of 0.912. This performance surpassed several other baseline models, including Llama3-8B and Qwen3-8B, demonstrating the effectiveness of these techniques for domain-specific NER. AI
IMPACT Enhances financial NER capabilities, potentially improving structured data extraction from financial documents.