metadata
language: 'no'
tags:
- token-classification
- text-classification
- ner
- intent-classification
- norwegian
- bert
base_model: NbAiLab/nb-bert-large
JointBERT for Norwegian Feed Orders
A joint intent classification and slot filling / NER model for Norwegian animal feed order queries, fine-tuned from NbAiLab/nb-bert-large.
Task
The model processes Norwegian spoken order queries and:
- Classifies the intent (6 classes):
create_order,edit_order,confirm,reject,help,reorder_last - Extracts named entities (7 entity types, IOB2):
PRODUCT,QUANTITY,UNIT,DELIVERY_METHOD,DELIVERY_DATE,ADDRESS,TANK_SILO
Test Set Results
Overall
| Metric | Score |
|---|---|
| NER Precision | 95.69% |
| NER Recall | 98.04% |
| NER F1 | 96.85% |
| Intent Accuracy | 99.07% |
| Intent F1 | 99.04% |
| Combined F1 | 97.94% |
Per-Entity NER F1
| Entity | F1 |
|---|---|
| PRODUCT | 96.00% |
| QUANTITY | 98.77% |
| UNIT | 96.97% |
| DELIVERY_METHOD | 100.00% |
| DELIVERY_DATE | 91.30% |
| ADDRESS | 97.25% |
| TANK_SILO | 97.44% |
Training
- Base model: NbAiLab/nb-bert-large (1024 hidden, 24 layers)
- Training data: 972 utterances (train + val merged after hyperparameter search)
- Hyperparameter search: Optuna (40 trials), retrained on train+val with best config
- Loss:
0.6 * intent + 0.4 * NER - Epochs: 15
- Learning rate: 3e-05
- Batch size: 16
- Weight decay: 0.1
- Warmup ratio: 0.2
- Frozen layers: 0 (full fine-tuning)