ltg/norbert3-coarse-absa

NorBERT3-absa-coarse-sent

This model is a fine-tuned version of NorBERT3-large, applied on the sentence-level NorPaC_absa dataset. The model is trained on a total of 25 unique coarse aspect+sentiment labels.

Example Usage

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

List of labels and abbreviations coming.

Evaluation

GP	SMH
60.76\std{1.84}	66.79\std{1.66}

The table shows weighted avg. F1 scores averaged across five seeds for the General Practitioner (GP) and Special Mental Healthcare (SMH) domain.

Citation

Coming.