README.md

---
language:
- nl
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: bert-base-multilingual-cased
tags:
- multi-label
- dutch
- municipal-complaints
- mbert
- bert
- pytorch
- safetensors
datasets:
- UWV/wim-synthetic-data-rd
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: WimBERT v0
  results:
  - task:
      name: Multi-label Text Classification
      type: text-classification
    dataset:
      name: UWV/WIM Synthetic RD
      type: UWV/wim-synthetic-data-rd
      split: validation
      subset: onderwerp
    metrics:
    - name: F1
      type: f1
      value: 0.932
    - name: Precision
      type: precision
      value: 0.960
    - name: Recall
      type: recall
      value: 0.905
    - name: Accuracy
      type: accuracy
      value: 0.998
  - task:
      name: Multi-label Text Classification
      type: text-classification
    dataset:
      name: UWV/WIM Synthetic RD
      type: UWV/wim-synthetic-data-rd
      split: validation
      subset: beleving
    metrics:
    - name: F1
      type: f1
      value: 0.789
    - name: Precision
      type: precision
      value: 0.859
    - name: Recall
      type: recall
      value: 0.730
    - name: Accuracy
      type: accuracy
      value: 0.971
widget:
- text: "Goedemiddag, ik heb al drie keer gebeld over mijn uitkering en krijg geen duidelijkheid."
---

# WimBERT v0

WimBERT is a dual‑head, multi‑label classifier for Dutch municipal complaint messages.
The model uses a shared mmBERT‑base encoder with two MLP heads:
- Onderwerp (topics): 96 labels
- Beleving (experience): 26 labels

Trained with a combined objective: alpha · (1 − Soft‑F1) + (1 − alpha) · BCE.

## Overview
- Encoder: mmBERT‑base (multilingual)
- Heads: 2× MLP (Linear → Dropout → ReLU → Linear)
- Labels: 96 onderwerp, 26 beleving
- Task: Multi‑label classification (sigmoid per class)
- Thresholds: Disabled (fixed 0.5 used for evaluation/inference)

## Intended Use
- Classify incoming Dutch complaint messages into topical (onderwerp) and experiential (beleving) labels.
- Useful for analytics, routing, and trend insights. Not intended for legal or benefit decisions without human review.

## Training Data
- Source: `UWV/wim-synthetic-data-rd` (train split)
- Samples: 9,351
- Labels: 96 onderwerp, 26 beleving
- Avg labels per sample: onderwerp 1.75, beleving 1.89
- Shapes: onderwerp (9351, 96), beleving (9351, 26)
- Train/Val split: 7,480 / 1,871 (80/20)

## Training Setup
- Date: 2025‑10‑20
- Hardware: NVIDIA A100 GPU
- Epochs: 15
- Batch size: 16
- Sequence length: 1,408 tokens
- Optimizer: AdamW
- Scheduler: Linear warmup (10%) → cosine annealing, `min_lr=1e‑6`
- Gradient clipping: max_norm 1.0
- Random seed: 42

### Hyperparameters
- alpha (F1 weight): 0.15
- dropout: 0.20
- encoder peak LR: 8e‑5
- temperature (Soft‑F1): 2.0
- learnable thresholds: false
- initial_threshold: 0.565 (not used, thresholds disabled)
- threshold LR mult: 5.0 (not used because thresholds disabled)

## Metrics
Final validation (500 samples):
- Onderwerp:
  - Accuracy: 99.8%
  - Precision: 0.960
  - Recall: 0.905
  - F1: 0.932
- Beleving:
  - Accuracy: 97.1%
  - Precision: 0.859
  - Recall: 0.730
  - F1: 0.789
- Combined:
  - Average Accuracy: 98.4%
  - Average F1: 0.861

## Saved Artifacts
- HF‑compatible files:
  - `model.safetensors` — encoder weights
  - `config.json` — encoder config
  - `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json` — tokenizer
  - `dual_head_state.pt` — classification heads + metadata (no thresholds included when disabled)
  - `label_names.json` — label names for both heads
- `inference_mmbert_hf_example.py` — example inference script (CLI)

## Inference
Quick start (script):
- `python inference_mmbert_hf_example.py [model_dir=. ] "Uw voorbeeldzin hier"`

Minimal code (probabilities + top‑k):
```python
import os, json, torch, torch.nn as nn
from transformers import AutoModel, AutoTokenizer

model_dir = "."
tok = AutoTokenizer.from_pretrained(model_dir)
enc = AutoModel.from_pretrained(model_dir).eval()
state = torch.load(os.path.join(model_dir, "dual_head_state.pt"), map_location="cpu")
with open(os.path.join(model_dir, "label_names.json")) as f:
    labels = json.load(f)

hidden = enc.config.hidden_size
drop = float(state.get("dropout", 0.1))
n_on, n_be = int(state["num_onderwerp"]), int(state["num_beleving"])
on_head = nn.Sequential(nn.Linear(hidden, hidden), nn.Dropout(drop), nn.ReLU(), nn.Linear(hidden, n_on)).eval()
be_head = nn.Sequential(nn.Linear(hidden, hidden), nn.Dropout(drop), nn.ReLU(), nn.Linear(hidden, n_be)).eval()
on_head.load_state_dict(state["onderwerp_head_state"])
be_head.load_state_dict(state["beleving_head_state"])

text = "Goedemiddag, ik heb al drie keer gebeld over mijn uitkering ..."
enc_inputs = tok(text, truncation=True, padding="max_length", max_length=int(state.get("max_length", 512)), return_tensors="pt")
pooled = enc(**enc_inputs).last_hidden_state[:, 0, :]
on_probs = torch.sigmoid(on_head(pooled))[0]
be_probs = torch.sigmoid(be_head(pooled))[0]

topk = lambda p, names, k=5: [(names[i], float(p[i])) for i in torch.topk(p, k=min(k, len(p))).indices]
print("Onderwerp:", topk(on_probs, labels["onderwerp"]))
print("Beleving:",  topk(be_probs, labels["beleving"]))
```

## Limitations & Risks
- Domain: Dutch complaint messages; performance may degrade out‑of‑domain or in other languages.
- Thresholding: No learned thresholds; 0.5 cutoff is a simple heuristic.
- Label imbalance and multi‑label ambiguity can affect precision/recall trade‑offs.

## Reproduction
- Script: `train_mmbert_dual_soft_f1_simplified.py`
- Env: see `requirements.txt` (PyTorch, Transformers, Datasets, wandb)
- Key config: seed 42, batch size 16, epochs 13, max_length 1408, α=0.15, encoder_peak_lr=8e‑5, warmup_ratio=0.1, min_lr=1e‑6.

## Acknowledgements
- UWV WIM synthetic RD dataset
- Hugging Face Transformers/Datasets

## License
This model is licensed under the Apache License 2.0. See `LICENSE` for details.