DOI: 10.5061/dryad.7m0cfxq8b
title: Data from: High-fidelity parameter-efficient fine-tuning for joint recognition and linking of diagnoses to ICD-10 in non-standard primary care notes

description: Joint recognition and ICD-10 linking of diagnoses in bilingual,
 non-standard Spanish and Catalan primary-care notes is challenging. We
 evaluate Parameter-Efficient Fine-Tuning (PEFT) techniques as a
 resource-conscious alternative to full fine-tuning (FFT) for multi-label
 clinical text classification. On a corpus of 21,812 Catalan and Spanish
 clinical notes from Catalonia, we compared the PEFT techniques LoRA, DoRA,
 LoHA, LoKR, and QLoRA applied to multilingual transformers (BERT, RoBERTa,
 DistilBERT, mDeBERTa). FFT delivered the best strict Micro-F1 (63.0), but
 BERT-QLoRA scored 62.2, only 0.8 points lower, while reducing trainable
 parameters by 67.5% and memory by 33.7%. Training on combined bilingual
 data consistently improved generalization across individual languages. The
 small FFT margin was confined to rare labels, indicating limited benefit
 from updating all parameters. Among PEFT techniques, QLoRA offered the
 strongest accuracy–efficiency balance; LoRA and DoRA were competitive,
 whereas LoHA and LoKR incurred larger losses. Adapter rank mattered: ranks
 below 128 sharply degraded Micro-F1. The substantial memory savings enable
 deployment on commodity GPUs while delivering performance very close to
 FFT. PEFT, particularly QLoRA, supports accurate and memory-efficient
 joint entity recognition and ICD-10 linking in multilingual, low-resource
 clinical settings.
description: # Data from: High-fidelity parameter-efficient fine-tuning for joint
 recognition and linking of diagnoses to ICD-10 in non-standard primary
 care notes Dataset DOI:
 [10.5061/dryad.7m0cfxq8b](https://doi.org/10.5061/dryad.7m0cfxq8b) ##
 Description of the data and file structure This dataset contains
 supplementary materials supporting the article *“High-Fidelity
 Parameter-Efficient Fine-Tuning for Joint Recognition and Linking of
 Diagnoses to ICD-10 in Non-Standard Primary Care Notes”* (JAMIA Open,
 2025). The files include trained model checkpoints and the corresponding
 training and evaluation scripts used in the study. These resources were
 generated through extensive experiments on a corpus of Spanish and Catalan
 primary care clinical notes. Raw clinical data are not included due to
 privacy and legal restrictions. The deposited materials enable
 reproducibility of the reported results, facilitate inspection of model
 architectures and hyperparameters, and provide code templates that can be
 adapted to similar datasets. ### Files and variables
 **bestmodels.zip.00*:** Contains the checkpoints of the best-performing
 models, selected according to the training technique (Full Fine-Tuning,
 LoRA, QLoRA, DoRA, LoHA, LoKr). **bestmodels.zip.001:** First part of the
 split archive containing the best-performing models.
 **bestmodels.zip.002:** Second part of the split archive containing the
 best-performing models. **bestmodels.zip.003:** Third part of the split
 archive containing the best-performing models. **bestmodels.zip.004:**
 Fourth part of the split archive containing the best-performing models.
 **bestmodels.zip.005:** Fifth part of the split archive containing the
 best-performing models. **bestmodels.zip.006:** Sixth part of the split
 archive containing the best-performing models. **bestmodels.zip.007:**
 Seventh part of the split archive containing the best-performing models.
 **scripts.zip:** Includes training and validation scripts, optional flags
 for experimentation, usage examples, and the list of required libraries.
 ## Code/software **Software requirements** The supplementary materials can
 be used with **Python 3.9.13**. The following open-source libraries were
 used in our experiments and are required to reproduce the workflows: *
 `bitsandbytes==0.42.0` * `cie==0.208` * `peft==0.14.0` *
 `torch==2.1.2+cu118` * `transformers==4.48.0` All packages are freely
 available via [PyPI](https://pypi.org/) or [Hugging
 Face](https://huggingface.co/). **Workflow**: 1. Install Python 3.9.13 and
 the listed dependencies. 2. Unpack the `scripts.zip` archive. 3. Follow
 the included README to run training or evaluation scripts. 4. Use
 `bestmodels.zip` checkpoints with the provided scripts for inference or
 fine-tuning. ## Access information Other publicly accessible locations of
 the Scripts and Models: *
 [https://doi.org/10.5281/zenodo.17025710](https://doi.org/10.5281/zenodo.17025710)