metadata
language: 'no'
tags:
- token-classification
- text-classification
- ner
- intent-classification
- norwegian
- bert
base_model: NbAiLab/nb-bert-large
Joint Intent + NER Model for Norwegian Feed Orders
A joint intent classification and named entity recognition model for Norwegian animal feed order queries, fine-tuned from NbAiLab/nb-bert-large.
Task
Given a Norwegian spoken order query, the model simultaneously:
- Classifies the intent (5 classes):
create_order,edit_order,confirm,reject,help - Extracts named entities (7 entity types, IOB2):
PRODUCT,QUANTITY,UNIT,DELIVERY_METHOD,DELIVERY_DATE,ADDRESS,TANK_SILO
Test Set Results
| Metric | Score |
|---|---|
| NER Precision | 92.71% |
| NER Recall | 95.19% |
| NER F1 | 93.93% |
| Intent Accuracy | 100% |
| Intent F1 | 100% |
| Combined F1 | 96.97% |
Training
- Base model: NbAiLab/nb-bert-large (1024 hidden, 24 layers)
- Training data: 450 examples (train + val merged after HP search)
- Hyperparameters: Found via Optuna search (20 trials), then retrained on train+val
- Loss:
0.6 * intent_loss + 0.4 * ner_loss - Epochs: 5
- Learning rate: 5e-05
- Batch size: 8