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NuExtract3 model sees precision boost after LoRA fine-tuning

By PulseAugur Editorial · [1 sources] ·

Researchers have fine-tuned the NuExtract3 model using LoRA on a pilot corpus of 624 training and 44 evaluation books. This process significantly boosted the model's precision from 0.61 to 0.76, while recall remained stable at 0.75. The next step involves expanding the training dataset. AI

IMPACT This fine-tuning technique could lead to more precise information extraction models, benefiting applications that rely on accurate data processing.

RANK_REASON The item describes a fine-tuning process for an existing model (NuExtract3) and reports on its performance improvements, fitting the 'research' bucket. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

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

NuExtract3 model sees precision boost after LoRA fine-tuning

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The item describes a fine-tuning process for an existing model (NuExtract3) and reports on its performance improvements, fitting the 'research' bucket. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    Fine-tuning NuExtract3 (LoRA) on 624 train / 44 eval books from this pilot corpus markedly improved precision (0.61→0.76), with recall essentially unchanged (0.

    Fine-tuning NuExtract3 (LoRA) on 624 train / 44 eval books from this pilot corpus markedly improved precision (0.61→0.76), with recall essentially unchanged (0.76→0.75) vs. the zero-shot base model. Next: scale up the training set. Code: https:// github.com/cboulanger/finetune -n…

    LINKS github.com/…/finetune-nuextract-toc huggingface.co/…/nuextract3-toc-demo

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