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ltg/
norbert3-coarse-absa-full

Name: norbert3-coarse-absa-full
Rating: 2.5 (17 reviews)

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norbert3-coarse-absa-full

This model is a fine-tuned version of NorBERT3-large, applied on the full sentence-level NorPaC_absa dataset. The model is trained on a total of 25 unique, coarse-grained aspect+sentiment labels. This model along with norbert3-fine-absa-full, represent the models which in practice will be used by NIPH/FHI, as they are trained on the full NorPaC_absa dataset.

Example Usage

python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-coarse-absa-full")
model = AutoModelForSequenceClassification.from_pretrained("ltg/norbert3-coarse-absa-full", trust_remote_code=True)

model.eval()

text = "fastlegen lytter til meg, men jeg synes ventetiden er for lang."

# tokenize input
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

# Run inference
with torch.no_grad():
    outputs = model(**inputs)

# Get predictions
threshold = 0.5
probs = torch.sigmoid(outputs.logits).squeeze()
predictions = [model.config.id2label[i] for i, prob in enumerate(probs) if prob > threshold]
print(predictions)
# -> ['staff_pos', 'avail_neg'] (healthcare providers and staff:positive, access and availability:negative)

Class labels

Below is the distribution of coarse-grained labels within NorPaC_absa on the comment-level.

Aspect full name	Short-name	Instances
Healthcare providers and staff	staff	1169
Organization of health services	org	502
Access and availability	avail	353
Environment and facilities	env	286
Treatment	treat	591
Uncategorized / Top-level aspects
Outcome and impact of treatment / stay	oits	319
Patient involvement and participation	pip	68
General	gen	1376
No aspect / Neutral	no-asp	92
Total		4756

Evaluation

As this model is fine-tuned on all of the data splits, there is no current evaluation. Performance metrics is however available in our paper for a version of the model trained on the train split.

Citation

{bibtext}

@inproceedings{storset-etal-2026-pain,
    title = "From Pain to Praise: Aspect-Based Sentiment Analysis for {N}orwegian Patient Feedback",
    author = "Storset, Lilja Charlotte  and
      Jelin, Elma  and
      Norman, Rebecka Maria  and
      Bjertnaes, Oyvind  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    editor = {Danilova, Vera  and
      Kurfal{\i}, Murathan  and
      S{\"o}derfeldt, Ylva  and
      Reed, Julia  and
      Burchell, Andrew},
    booktitle = "Proceedings of the 1st Workshop on Linguistic Analysis for Health ({H}ea{L}ing 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.healing-1.16/",
    doi = "10.18653/v1/2026.healing-1.16",
    pages = "191--202",
    ISBN = "979-8-89176-367-8",
    abstract = "This paper describes a new dataset for aspect-based sentiment analysis (ABSA) for analyzing patient feedback about healthcare services. In an interdisciplinary collaboration spanning the fields of natural language processing and healthcare research, we manually annotate a dataset of 2382 free-text comments collected from national patient experience surveys in Norway, covering two sub-fields of services {--} special mental healthcare and general practitioners. Annotations are provided on both the sentence- and comment-level, covering a fine-grained set of 25 unique healthcare-related aspects and their polarities. We also report results for fine-tuning both encoder- and decoder models on the resulting dataset, comparing different modeling strategies, like joint and sequential prediction of aspects and polarity. The resources developed in this work can assist healthcare researchers in the analysis of patient feedback, bringing a much more efficient approach compared to today{'}s manual analysis, potentially leading to improved patient satisfaction and clinical outcomes."
}