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DeepSeek-R1-8B fine-tuned for financial NER with LoRA and NEFTune

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON This is a research paper detailing the fine-tuning of an existing open-source model with specific techniques for a particular task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepSeek-R1-8B fine-tuned for financial NER with LoRA and NEFTune

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This is a research paper detailing the fine-tuning of an existing open-source model with specific techniques for a particular task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wu Yuerong, Mingni Luo ·

    Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune

    arXiv:2606.10392v1 Announce Type: new Abstract: 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 entiti…

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