---
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](https://huggingface.co/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)