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